diff --git a/.gitignore b/.gitignore deleted file mode 100755 index 8f0440b..0000000 --- a/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -# Emacs bkup files -*~ - diff --git a/README.md b/README.md index 7e665a2..b80d1e7 100755 --- a/README.md +++ b/README.md @@ -1,3 +1,4 @@ +<<<<<<< HEAD # PARSL MPI This workflow runs OpenMPI hello world jobs using Parsl. The OpenMPI hello world code is defined in `mpitest.c` and, when compiled and executed, prints the hostname and rank in the format below: @@ -84,3 +85,6 @@ No combination of `cores_per_worker`, `nodes_per_block` and `cores_per_node` ret 2. Set `nodes_per_block` to the desired number of nodes per MPI task 3. Set `cores_per_worker` to the number of cores in a single node 4. Set `parallelism` to the desired number of nodes per MPI task +======= +This repository had been made for the sole purpose of testing DVC. I'm pretty sure I keep destroying my other one so imma start trying to be safer lol. +>>>>>>> testing-repo/main diff --git a/run_on_cluster/cvae-demos/digits-demo/.ipynb_checkpoints/README-checkpoint.md b/run_on_cluster/cvae-demos/digits-demo/.ipynb_checkpoints/README-checkpoint.md new file mode 100644 index 0000000..0c3f3e9 --- /dev/null +++ b/run_on_cluster/cvae-demos/digits-demo/.ipynb_checkpoints/README-checkpoint.md @@ -0,0 +1,47 @@ +# CVAE Digits Example Demo +This demo is dervied [Francois Chollet's](https://keras.io/examples/generative/vae/) Variational AutoEncoder example, modified for online learning. In particular, some goals are: + +* Test computational performance scaling (CPUs, GPUs) +* Test model performance while saving and fitting again (in particular, adding more data instead of having all the data available at once). +* Integrations with DVC + +## Environment Set Up +To get started with the CVAE Digit Model, follow the instructions below to set up your environment. + +### Create Environment: +``` +source ~/pw/.miniconda3c/etc/profile.d/conda.sh +conda create --name python=3.9 +conda activate +``` + +### Install Packages: +The following packages need to be installed on top of a typical base Conda env. Install the packages in the following order so the environment solves correctly: +``` +conda install -y -c conda-forge tensorflow +conda install -y -c conda-forge matplotlib +conda install -y -c conda-forge pandas +conda install -y -c conda-forge dvc + +pip install mlflow +``` + +### Connect Notebook to Environmant: +``` +conda install -y ipykernel +conda install -y requests +conda install -y -c anaconda jinja2 +python -m ipykernel install --user --name= --display-name "Python ()" +``` + +### Alternative Set Up: + +1. Run `conda env update --file digits_env.yaml --name ` to install the conda environment with all the needed dependencies +2. Run `source ~/pw/.miniconda3c/etc/profile.d/conda.sh` to initiate conda +3. Run `conda activate ` to activate the environment you installed +4. Run `python -m ipykernel install --user --name= --display-name "Python ()"` to connect the environment to the notebook + +## Needed Specifications + +**Storage:** Mounted stoarge is required for dvc to run properly.\ +**GitHub:** A separate git repo that can be SSH into is also needed for dvc to run properly. SSH connection must be established on the terminal. \ No newline at end of file diff --git a/run_on_cluster/cvae-demos/digits-demo/.ipynb_checkpoints/cvae_digits_example-checkpoint.ipynb b/run_on_cluster/cvae-demos/digits-demo/.ipynb_checkpoints/cvae_digits_example-checkpoint.ipynb new file mode 100644 index 0000000..c3f7002 --- /dev/null +++ b/run_on_cluster/cvae-demos/digits-demo/.ipynb_checkpoints/cvae_digits_example-checkpoint.ipynb @@ -0,0 +1,846 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7af3de73-569a-46b0-8fe9-2e1658aea2a3", + "metadata": {}, + "source": [ + "# Variational AutoEncoder Digits Example\n", + "\n", + "Check the README for an introduction to the project and how to get started!" + ] + }, + { + "cell_type": "markdown", + "id": "657bf264-4ddd-47d4-851e-0e31b2fbd98b", + "metadata": {}, + "source": [ + "## Imports and Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4dcc2c80-8eb8-46d0-888c-b70d26e71f61", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "os.environ[\"KERAS_BACKEND\"] = \"tensorflow\"\n", + "\n", + "# ml dependencies\n", + "import tensorflow as tf\n", + "import keras\n", + "from keras import ops\n", + "from keras import layers\n", + "\n", + "# mlflow dependencies\n", + "import mlflow\n", + "from mlflow import MlflowClient\n", + "from pprint import pprint" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "19dea6e9-69e5-4cee-a546-a9db7792978b", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# create the model directory for saving outputs\n", + "model_dir = './model-dir'\n", + "os.makedirs(model_dir, exist_ok = True)\n", + "\n", + "env_name = \"digits_env\" # " + ] + }, + { + "cell_type": "markdown", + "id": "dc4c11e1-f566-4353-b220-f50d367cc310", + "metadata": {}, + "source": [ + "## Create the ML Model" + ] + }, + { + "cell_type": "markdown", + "id": "c9583330-6383-440f-acc5-ce631cf9e27a", + "metadata": {}, + "source": [ + "### Create sampling layer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "013491ac-f39f-44bb-9883-7a6cfcac1f2c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "class Sampling(layers.Layer):\n", + " \"\"\"Uses (z_mean, z_log_var) to sample z, the vector encoding a digit.\"\"\"\n", + "\n", + " def __init__(self, **kwargs):\n", + " super().__init__(**kwargs)\n", + " self.seed_generator = keras.random.SeedGenerator(1337)\n", + "\n", + " def call(self, inputs):\n", + " z_mean, z_log_var = inputs\n", + " batch = ops.shape(z_mean)[0]\n", + " dim = ops.shape(z_mean)[1]\n", + " epsilon = keras.random.normal(shape=(batch, dim), seed=self.seed_generator)\n", + " return z_mean + ops.exp(0.5 * z_log_var) * epsilon" + ] + }, + { + "cell_type": "markdown", + "id": "de4536fc-e359-49fb-bc52-54b9ff244d46", + "metadata": { + "tags": [] + }, + "source": [ + "### Build the encoder" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bc70b33c-f2bd-41fb-83f4-90ac1c4afef7", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "latent_dim = 2\n", + "\n", + "encoder_inputs = keras.Input(shape=(28, 28, 1))\n", + "x = layers.Conv2D(32, 3, activation=\"relu\", strides=2, padding=\"same\")(encoder_inputs)\n", + "x = layers.Conv2D(64, 3, activation=\"relu\", strides=2, padding=\"same\")(x)\n", + "x = layers.Flatten()(x)\n", + "x = layers.Dense(16, activation=\"relu\")(x)\n", + "z_mean = layers.Dense(latent_dim, name=\"z_mean\")(x)\n", + "z_log_var = layers.Dense(latent_dim, name=\"z_log_var\")(x)\n", + "z = Sampling()([z_mean, z_log_var])\n", + "encoder = keras.Model(encoder_inputs, [z_mean, z_log_var, z], name=\"encoder\")\n", + "encoder.summary()" + ] + }, + { + "cell_type": "markdown", + "id": "91004269-b7a7-4d19-8812-729cd8366b9e", + "metadata": {}, + "source": [ + "### Build the decoder" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bab93526-4805-4a62-95a8-3dbceb47950e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "latent_inputs = keras.Input(shape=(latent_dim,))\n", + "x = layers.Dense(7 * 7 * 64, activation=\"relu\")(latent_inputs)\n", + "x = layers.Reshape((7, 7, 64))(x)\n", + "x = layers.Conv2DTranspose(64, 3, activation=\"relu\", strides=2, padding=\"same\")(x)\n", + "x = layers.Conv2DTranspose(32, 3, activation=\"relu\", strides=2, padding=\"same\")(x)\n", + "decoder_outputs = layers.Conv2DTranspose(1, 3, activation=\"sigmoid\", padding=\"same\")(x)\n", + "decoder = keras.Model(latent_inputs, decoder_outputs, name=\"decoder\")\n", + "decoder.summary()" + ] + }, + { + "cell_type": "markdown", + "id": "85405b6c-a092-49ad-9358-5e0c38622c59", + "metadata": {}, + "source": [ + "### Define the VAE as a `Model` with a custom `train_step`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9d2ef055-a698-4458-8b30-8671d3975195", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "class VAE(keras.Model):\n", + " def __init__(self, encoder, decoder, **kwargs):\n", + " super().__init__(**kwargs)\n", + " self.encoder = encoder\n", + " self.decoder = decoder\n", + " self.total_loss_tracker = keras.metrics.Mean(name=\"total_loss\")\n", + " self.reconstruction_loss_tracker = keras.metrics.Mean(\n", + " name=\"reconstruction_loss\"\n", + " )\n", + " self.kl_loss_tracker = keras.metrics.Mean(name=\"kl_loss\")\n", + "\n", + " @property\n", + " def metrics(self):\n", + " return [\n", + " self.total_loss_tracker,\n", + " self.reconstruction_loss_tracker,\n", + " self.kl_loss_tracker,\n", + " ]\n", + "\n", + " def train_step(self, data):\n", + " with tf.GradientTape() as tape:\n", + " z_mean, z_log_var, z = self.encoder(data)\n", + " reconstruction = self.decoder(z)\n", + " reconstruction_loss = ops.mean(\n", + " ops.sum(\n", + " keras.losses.binary_crossentropy(data, reconstruction),\n", + " axis=(1, 2),\n", + " )\n", + " )\n", + " kl_loss = -0.5 * (1 + z_log_var - ops.square(z_mean) - ops.exp(z_log_var))\n", + " kl_loss = ops.mean(ops.sum(kl_loss, axis=1))\n", + " total_loss = reconstruction_loss + kl_loss\n", + " grads = tape.gradient(total_loss, self.trainable_weights)\n", + " self.optimizer.apply_gradients(zip(grads, self.trainable_weights))\n", + " self.total_loss_tracker.update_state(total_loss)\n", + " self.reconstruction_loss_tracker.update_state(reconstruction_loss)\n", + " self.kl_loss_tracker.update_state(kl_loss)\n", + " return {\n", + " \"loss\": self.total_loss_tracker.result(),\n", + " \"reconstruction_loss\": self.reconstruction_loss_tracker.result(),\n", + " \"kl_loss\": self.kl_loss_tracker.result(),\n", + " }" + ] + }, + { + "cell_type": "markdown", + "id": "0943c16f-dff8-479e-a7ee-dc38cb68249c", + "metadata": {}, + "source": [ + "## DVC\n", + "\n", + "[Data Version Control](https://dvc.org/doc/user-guide) is an open-source tool for handlin machine learning projects. It helps with data management, ML pipeline automation, and experiment management, making projects reproducible and collaboration better. DVC integrates with Git to track changes in your data and models, enabling you to version control your machine learning workflow in a way similar to how you manage code.\n", + "\n", + "-------------------------------------------------------------------------------------------------------------------------------------\n", + "\n", + "In the lines below, the `&&` symbol is used multiple times. This symbol is originally a logical operator (the command on the right will only run if the command on the left executes successfully). However, when using `!` in a Jupyter notebook, the Linux commands are executed within the directory where the notebook is currently located. This behavior prevents commands from being run in separate directories if `!cd` is on its own line. Using `&&` ensures that the `dvc` commands are executed within the `dvc` submodule directory, without affecting the repository where the notebook resides." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e83ae770-6a77-469b-b7ef-88698007156b", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "dvc_repo_link = \"git@github.com:oobielodan/digits_dvc.git\" # \n", + "dvc_storage = \"/demo-bucket\" # " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "352c4ccb-6144-4ac4-a692-c04ec4ef2ff6", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# grab your dvc repository -> the --force flag allows for this to still run if the submodule had already been created at a prior time\n", + "!git submodule add --force \"{dvc_repo_link}\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5e481ef0-3ceb-44e6-9777-4a588b8a3921", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "dvc_repo = \"digits_dvc\" # -> should appear as a folder in the current directory" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "234e2ab8-16cd-4175-ab32-a501bbdfcd59", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# DVC initialization and storage set up\n", + "!cd \"{dvc_repo}\" && dvc init\n", + "!cd \"{dvc_repo}\" && dvc remote add -d dvcstorage \"{dvc_storage}\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1711066f-c8b6-46e2-9cd4-c90be29c053f", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# initial commit to git\n", + "!cd \"{dvc_repo}\" && git add .\n", + "!cd \"{dvc_repo}\" && git commit -m \"loaded dependencies, mkdir -p, DVC init\"" + ] + }, + { + "cell_type": "markdown", + "id": "581b6282-650d-4e92-9537-811e4e09754d", + "metadata": {}, + "source": [ + "## MLFlow\n", + "MLflow is designed to help simplify the ML workflow, assisting users throughout the various stages of development and deployment. In this notebook, we use its autologging capabilities to log information on the model, its parameters and results, etc. Documentation and more information can be found at [the MLFlow website](https://mlflow.org/docs/latest/index.html).\n", + "\n", + "-------------------------------------------------------------------------------------------------------------------------------------\n", + "\n", + "To get started with MLflow, run `mlflow server --host 127.0.0.1 --port 8080` in the command line. The `mlflow server` command needs to run in the background and therefore cannot be executed directly in a Jupyter notebook, as each cell must complete execution before the next one can run." + ] + }, + { + "cell_type": "markdown", + "id": "6018ac9f-99fe-438d-8942-8285ced96f03", + "metadata": {}, + "source": [ + "### Configuration\n", + "*If you used a different host and/or port during initialization, make sure to update the following URIs accordingly.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ff10acc3-348f-4437-9767-b6f0d50924b1", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# utilize and set up the initialized server for tracking \n", + "client = MlflowClient(tracking_uri = \"http://127.0.0.1:8080\")\n", + "mlflow.set_tracking_uri(\"http://127.0.0.1:8080\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2a87c393-fc86-437e-876f-f991f38bd1f6", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# view the metadata associated with all the experiments that are currently on the server \n", + "all_experiments = client.search_experiments()\n", + "print(all_experiments)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f15316ea-3e62-407f-9a8b-766c5d46d815", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# example for accessing elements from the returned collections of experiments\n", + "default_experiment = [\n", + " {\"name\": experiment.name, \"lifecycle_stage\": experiment.lifecycle_stage}\n", + " for experiment in all_experiments\n", + " if experiment.name == \"Default\"\n", + "][0]\n", + "\n", + "pprint(default_experiment)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "932b8766-424d-40b2-bee4-5cd60ba3b329", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# working on getting the server to display in the notebook --------------------------------------\n", + "# !curl http://127.0.0.1:8080\n", + "# %%javascript\n", + "# alert(\"JavaScript is working!\");\n", + "# from IPython.display import IFrame\n", + "# IFrame(\"http://127.0.0.1:8080\", 900,500)" + ] + }, + { + "cell_type": "markdown", + "id": "c53c160f-45da-42be-9ed3-5385510e89e1", + "metadata": { + "tags": [] + }, + "source": [ + "### Experiment 1\n", + "In Experiment 1, we train the Digit CVAE model on multiple datasets. To create these datasets, we split the original dataset into five equal, randomized parts. After each training session, we save the weights and use them as the starting point for retraining the model on the next dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b3a09d3f-a2a4-49fe-b97c-e0bcfda96323", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# provide an experiment description that will appear in the UI\n", + "experiment1_description = (\n", + " \"This is the digits forecasting project.\"\n", + " \"This experiment contains the digit model for randomized numbers (0-9) trained separately.\"\n", + ")\n", + "\n", + "# provide searchable tags for the experiment\n", + "experiment1_tags = {\n", + " \"project_name\": \"digit-forecasting\",\n", + " \"model_type\": \"randomzied\",\n", + " \"team\": \"digit-ml\",\n", + " \"project_quarter\": \"Q3-2024\",\n", + " \"mlflow.note.content\": experiment1_description,\n", + "}\n", + "\n", + "# create the experiment and give it a unique name\n", + "digit_experiment1 = client.create_experiment(\n", + " name=\"Randomize_Model\", tags=experiment1_tags\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "8264adfb-09ee-4218-9c30-421d3ac41e23", + "metadata": {}, + "source": [ + "### Experiment 2\n", + "In Experiment 2, we train the Digit CVAE model on all digit samples simultaneously, without any subsequent retraining using the weights." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fb5258b2-2c71-475d-83fd-d597cfc05454", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# provide an experiment description that will appear in the UI\n", + "experiment2_description = (\n", + " \"This is the digits forecasting project.\"\n", + " \"This experiment contains the digit model for numbers (0-9) trained all together.\"\n", + ")\n", + "\n", + "# provide searchable tags for the experiment\n", + "experiment2_tags = {\n", + " \"project_name\": \"digit-forecasting\",\n", + " \"model_type\": \"all digits\",\n", + " \"team\": \"digit-ml\",\n", + " \"project_quarter\": \"Q3-2024\",\n", + " \"mlflow.note.content\": experiment2_description,\n", + "}\n", + "\n", + "# create the experiment and give it a unique name\n", + "digit_experiment2 = client.create_experiment(\n", + " name=\"Together_Model\", tags=experiment2_tags\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "27de39b5-391a-4305-a317-e15c14a8c4a5", + "metadata": {}, + "source": [ + "### Experiment 3\n", + "In Experiment 3, we revisit the approach used in Experiment 1 - initializing the model with the weights from a previous training session and retraining it from there. However in this experiment, we train the Digit CVAE model sequentially on each of the 10 digits (0–9), one digit at a time. After each training session, we save the weights and use them to retrain the model on the next digit. This approach induces a 'forgetting' effect, where the model gradually loses its ability to recognize previous digits with each subsequent training session." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6d8e4009-d290-4053-93e9-b665b04c5aea", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# provide an experiment description that will appear in the UI\n", + "experiment3_description = (\n", + " \"This is the digits forecasting project.\"\n", + " \"This experiment contains the digit model for each of the numbers (0-9) trained separately.\"\n", + ")\n", + "\n", + "# provide searchable tags that define characteristics of the runs that will be in this experiment\n", + "experiment3_tags = {\n", + " \"project_name\": \"digit-forecasting\",\n", + " \"model_type\": \"sequential\",\n", + " \"team\": \"digit-ml\",\n", + " \"project_quarter\": \"Q3-2024\",\n", + " \"mlflow.note.content\": experiment3_description,\n", + "}\n", + "\n", + "# create the experiment and give it a unique name\n", + "digit_experiment3 = client.create_experiment(\n", + " name=\"Sequenced_Model\", tags=experiment3_tags\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "3234d529-70a9-4806-a3da-fc55d3564864", + "metadata": {}, + "source": [ + "### Experiment Set Up" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a7db30da-8a05-4e72-b1e2-907bc7ea6631", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# save each of the experiment's metadata\n", + "digit_experiment1 = mlflow.set_experiment(\"Randomize_Model\")\n", + "digit_experiment2 = mlflow.set_experiment(\"Together_Model\")\n", + "digit_experiment3 = mlflow.set_experiment(\"Sequenced_Model\")" + ] + }, + { + "cell_type": "markdown", + "id": "a3f662b7-a8c8-4ae4-9f60-b6de69e0ddef", + "metadata": {}, + "source": [ + "## Train the VAE" + ] + }, + { + "cell_type": "markdown", + "id": "4d8c6708-4ea5-45af-8b6c-13406429f4c3", + "metadata": {}, + "source": [ + "*Make sure that 'vae.weights.h5' does not already exist in the model directory if you want to training from the beginning.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c645768a-9b70-4568-8e59-7544678392ee", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "vae = VAE(encoder, decoder)\n", + "vae.compile(optimizer=keras.optimizers.Adam())\n", + "(x_train, Y_train), (x_test, Y_test) = keras.datasets.mnist.load_data()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0cfc6419-1cbc-4261-ac12-92779e526d1b", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "early_stopping_cb = keras.callbacks.EarlyStopping(patience = 5, restore_best_weights = True) # stops training early if the validation loss does not improve\n", + "\n", + "def train_model(num, model, data, experiment):\n", + " if os.path.exists(os.path.join(model_dir, 'vae.weights.h5')): # if the model has already been trained at least once, load that model\n", + " model.load_weights(os.path.join(model_dir, 'vae.weights.h5'))\n", + " \n", + " mlflow.autolog()\n", + " \n", + " run_name = f\"{num}_test\" # define a run name for this iteration of training\n", + " artifact_path = f\"{num}\" # define an artifact path that the model will be saved to\n", + " \n", + " # initiate the MLflow run context\n", + " with mlflow.start_run(run_name = run_name, experiment_id = experiment) as run:\n", + " \n", + " history = model.fit(data, epochs=30, batch_size=128, callbacks = [early_stopping_cb])\n", + " model.save_weights(os.path.join(model_dir, 'vae.weights.h5')) # save model weights after training\n", + "\n", + " hist_pd = pd.DataFrame(history.history)\n", + " hist_pd.to_csv(os.path.join(model_dir, f'history_{num}.csv'), index = False)" + ] + }, + { + "cell_type": "markdown", + "id": "72cf688b-97d3-4a20-bc27-dae71d7528dc", + "metadata": { + "tags": [] + }, + "source": [ + "### Experiment 1\n", + "*How to filter mnist data found here: https://stackoverflow.com/questions/51202181/how-do-i-select-only-a-specific-digit-from-the-mnist-dataset-provided-by-keras*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38582c97-e230-4ac6-a4cd-5518bf02c151", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# retraining the model n times\n", + "count = 0\n", + "n = 5\n", + "\n", + "mnist_digits = np.expand_dims(np.concatenate([x_train, x_test], axis=0), -1).astype(\"float32\") / 255\n", + "\n", + "for arr in np.array_split(mnist_digits, n):\n", + " count += 1\n", + " train_model(f\"rand_{count}\", vae, arr, digit_experiment3.experiment_id)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6cc99efc-4727-4904-ab3a-2bb9df539125", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# add model 1 to dvc \n", + "!cp ./\"{model_dir}\"/vae.weights.h5 \"{dvc_repo}\"/experiment_1.weights.h5\n", + "!sh dvcgit.sh experiment_1.weights.h5 \"digit experiment 1\" \"{dvc_repo}\" \"{env_name}\"\n", + "\n", + "!rm \"{dvc_storage}\"/experiment_1.weights.h5\n", + "!rm ./\"{model_dir}\"/vae.weights.h5" + ] + }, + { + "cell_type": "markdown", + "id": "ade80f16-6a1e-4e26-b4ce-77314d6fff91", + "metadata": {}, + "source": [ + "### Experiment 2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a8849863-fcf5-46dd-997b-78a245dc0e2a", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# train all numbers at the same time\n", + "train_model(\"all\", vae, mnist_digits, digit_experiment2.experiment_id)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "22343e08-f3c6-4fb8-aee5-7a5e24cca01f", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# add model 2 to dvc \n", + "!cp ./\"{model_dir}\"/vae.weights.h5 \"{dvc_repo}\"/experiment_2.weights.h5\n", + "!sh dvcgit.sh experiment_2.weights.h5 \"digit experiment 2\" \"{dvc_repo}\" \"{env_name}\"\n", + "\n", + "!rm \"{dvc_repo}\"/experiment_2.weights.h5\n", + "!rm ./\"{model_dir}\"/vae.weights.h5" + ] + }, + { + "cell_type": "markdown", + "id": "7cc7acce-d93a-4724-9dfa-4b970b969414", + "metadata": {}, + "source": [ + "### Experiment 3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b7662d9d-9844-4f16-8a90-7c4642675abe", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# training one number at a time\n", + "for num in np.arange(10):\n", + " train_filter = np.where(Y_train == num)\n", + " test_filter = np.where(Y_test == num)\n", + " \n", + " x_trn = x_train[train_filter]\n", + " x_tst = x_test[test_filter]\n", + " \n", + " mnist_digits = np.expand_dims(np.concatenate([x_trn, x_tst], axis=0), -1).astype(\"float32\") / 255\n", + " train_model(num, vae, mnist_digits, digit_experiment1.experiment_id)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ea85634-da45-498e-98fa-46f413516499", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# add model 3 to dvc \n", + "!cp ./\"{model_dir}\"/vae.weights.h5 \"{dvc_repo}\"/experiment_3.weights.h5\n", + "!sh dvcgit.sh experiment_3.weights.h5 \"digit experiment 3\" \"{dvc_repo}\" \"{env_name}\"\n", + "\n", + "!rm \"{dvc_repo}\"/experiment_3.weights.h5\n", + "!rm ./\"{model_dir}\"/vae.weights.h5" + ] + }, + { + "cell_type": "markdown", + "id": "0f35ce03-037f-4f63-98aa-ef411029326f", + "metadata": {}, + "source": [ + "------------------------------------------------------------------------------------------------\n", + "*`dvcgit.sh` is a script used for dvc and git tracking. The correct call is as follows (all arguments are required):* `sh dvcgit.sh `" + ] + }, + { + "cell_type": "markdown", + "id": "7494fca6-d4b7-4ef3-91a5-4f1422526fd5", + "metadata": { + "tags": [] + }, + "source": [ + "## Display a grid of reconstructed digits in the latent space" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a7d63bff-e2c6-40a1-a8f8-d375b008557e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "def plot_latent_space(vae, n=30, figsize=15):\n", + " # display a n*n 2D manifold of digits\n", + " digit_size = 28\n", + " scale = 1.0\n", + " figure = np.zeros((digit_size * n, digit_size * n))\n", + " # linearly spaced coordinates corresponding to the 2D plot\n", + " # of digit classes in the latent space\n", + " grid_x = np.linspace(-scale, scale, n)\n", + " grid_y = np.linspace(-scale, scale, n)[::-1]\n", + "\n", + " for i, yi in enumerate(grid_y):\n", + " for j, xi in enumerate(grid_x):\n", + " z_sample = np.array([[xi, yi]])\n", + " x_decoded = vae.decoder.predict(z_sample, verbose=0)\n", + " digit = x_decoded[0].reshape(digit_size, digit_size)\n", + " figure[\n", + " i * digit_size : (i + 1) * digit_size,\n", + " j * digit_size : (j + 1) * digit_size,\n", + " ] = digit\n", + "\n", + " plt.figure(figsize=(figsize, figsize))\n", + " start_range = digit_size // 2\n", + " end_range = n * digit_size + start_range\n", + " pixel_range = np.arange(start_range, end_range, digit_size)\n", + " sample_range_x = np.round(grid_x, 1)\n", + " sample_range_y = np.round(grid_y, 1)\n", + " plt.xticks(pixel_range, sample_range_x)\n", + " plt.yticks(pixel_range, sample_range_y)\n", + " plt.xlabel(\"z[0]\")\n", + " plt.ylabel(\"z[1]\")\n", + " plt.imshow(figure, cmap=\"Greys_r\")\n", + " plt.show()\n", + "\n", + "plot_latent_space(vae)" + ] + }, + { + "cell_type": "markdown", + "id": "fbc89d55-5962-463f-ab7f-f0332e2e3430", + "metadata": {}, + "source": [ + "## Display how the latent space clusters digits" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "68dabfbc-7024-4831-8432-6682c42dab7f", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def plot_label_clusters(vae, data, labels):\n", + " # display a 2D plot of the digit classes in the latent space\n", + " z_mean, _, _ = vae.encoder.predict(data, verbose=0)\n", + " plt.figure(figsize=(12, 10))\n", + " plt.scatter(z_mean[:, 0], z_mean[:, 1], c=labels)\n", + " plt.colorbar()\n", + " plt.xlabel(\"z[0]\")\n", + " plt.ylabel(\"z[1]\")\n", + " plt.show()\n", + "\n", + "(x_train, y_train), _ = keras.datasets.mnist.load_data()\n", + "x_train = np.expand_dims(x_train, -1).astype(\"float32\") / 255\n", + "\n", + "plot_label_clusters(vae, x_train, y_train)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:digits_env]", + "language": "python", + "name": "conda-env-digits_env-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/run_on_cluster/cvae-demos/digits-demo/.ipynb_checkpoints/dvcgit-checkpoint.sh b/run_on_cluster/cvae-demos/digits-demo/.ipynb_checkpoints/dvcgit-checkpoint.sh new file mode 100644 index 0000000..e715612 --- /dev/null +++ b/run_on_cluster/cvae-demos/digits-demo/.ipynb_checkpoints/dvcgit-checkpoint.sh @@ -0,0 +1,24 @@ +#!/bin/bash + +# Start Conda and select env (you will need to cross-check the paths and env name) + +source ~/pw/.miniconda3c/etc/profile.d/conda.sh +conda activate digits_env + +# Run the git commands using input from the command line; +# $1 stores the first entry on the command line that launches +# the shell script and so on. Add as many as you need. + +file_name=$1 +commit_message="$2" +dvc_dir="$3" + +cd "$dvc_dir" + +dvc add "$file_name" +git add .gitignore +git add "${file_name}.dvc" + +dvc push +git commit -m "$commit_message" +git push \ No newline at end of file diff --git a/run_on_cluster/cvae-demos/digits-demo/README.md b/run_on_cluster/cvae-demos/digits-demo/README.md new file mode 100644 index 0000000..0c86ff3 --- /dev/null +++ b/run_on_cluster/cvae-demos/digits-demo/README.md @@ -0,0 +1,47 @@ +# CVAE Digits Example Demo +This demo is dervied [Francois Chollet's](https://keras.io/examples/generative/vae/) Variational AutoEncoder example, modified for online learning. In particular, some goals are: + +* Test computational performance scaling (CPUs, GPUs) +* Test model performance while saving and fitting again (in particular, adding more data instead of having all the data available at once). +* Integrations with DVC + +## Environment Set Up +To get started with the CVAE Digit Model, follow the instructions below to set up your environment. + +### Create Environment: +``` +source ~/pw/.miniconda3c/etc/profile.d/conda.sh +conda create --name python=3.9 +conda activate +``` + +### Install Packages: +The following packages need to be installed on top of a typical base Conda env. Install the packages in the following order so the environment solves correctly: +``` +conda install -y -c conda-forge tensorflow +conda install -y -c conda-forge matplotlib +conda install -y -c conda-forge pandas +conda install -y -c conda-forge dvc + +pip install mlflow +``` + +### Connect Notebook to Environment: +``` +conda install -y ipykernel +conda install -y requests +conda install -y -c anaconda jinja2 +python -m ipykernel install --user --name= --display-name "Python ()" +``` + +### Alternative Set Up: + +1. Run `conda env update --file digits_env.yaml --name ` to install the conda environment with all the needed dependencies +2. Run `source ~/pw/.miniconda3c/etc/profile.d/conda.sh` to initiate conda +3. Run `conda activate ` to activate the environment you installed +4. Run `python -m ipykernel install --user --name= --display-name "Python ()"` to connect the environment to the notebook + +## Needed Specifications + +**Storage:** Mounted storage is required for dvc to run properly.\ +**GitHub:** A separate git repo that can be SSH into is also needed for dvc to run properly. SSH connection must be established on the terminal. diff --git a/run_on_cluster/cvae-demos/digits-demo/cvae_digits_example.ipynb b/run_on_cluster/cvae-demos/digits-demo/cvae_digits_example.ipynb new file mode 100644 index 0000000..1fd6aab --- /dev/null +++ b/run_on_cluster/cvae-demos/digits-demo/cvae_digits_example.ipynb @@ -0,0 +1,829 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7af3de73-569a-46b0-8fe9-2e1658aea2a3", + "metadata": {}, + "source": [ + "# Variational AutoEncoder Digits Example\n", + "\n", + "Check the README for an introduction to the project and how to get started!" + ] + }, + { + "cell_type": "markdown", + "id": "657bf264-4ddd-47d4-851e-0e31b2fbd98b", + "metadata": {}, + "source": [ + "## Imports and Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4dcc2c80-8eb8-46d0-888c-b70d26e71f61", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "os.environ[\"KERAS_BACKEND\"] = \"tensorflow\"\n", + "\n", + "# ml dependencies\n", + "import tensorflow as tf\n", + "import keras\n", + "from keras import ops\n", + "from keras import layers\n", + "\n", + "# mlflow dependencies\n", + "import mlflow\n", + "from mlflow import MlflowClient\n", + "from pprint import pprint" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "19dea6e9-69e5-4cee-a546-a9db7792978b", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# create the model directory for saving outputs\n", + "model_dir = './model-dir'\n", + "os.makedirs(model_dir, exist_ok = True)\n", + "\n", + "env_name = \"digits_env\" # " + ] + }, + { + "cell_type": "markdown", + "id": "dc4c11e1-f566-4353-b220-f50d367cc310", + "metadata": {}, + "source": [ + "## Create the ML Model" + ] + }, + { + "cell_type": "markdown", + "id": "c9583330-6383-440f-acc5-ce631cf9e27a", + "metadata": {}, + "source": [ + "### Create sampling layer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "013491ac-f39f-44bb-9883-7a6cfcac1f2c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "class Sampling(layers.Layer):\n", + " \"\"\"Uses (z_mean, z_log_var) to sample z, the vector encoding a digit.\"\"\"\n", + "\n", + " def __init__(self, **kwargs):\n", + " super().__init__(**kwargs)\n", + " self.seed_generator = keras.random.SeedGenerator(1337)\n", + "\n", + " def call(self, inputs):\n", + " z_mean, z_log_var = inputs\n", + " batch = ops.shape(z_mean)[0]\n", + " dim = ops.shape(z_mean)[1]\n", + " epsilon = keras.random.normal(shape=(batch, dim), seed=self.seed_generator)\n", + " return z_mean + ops.exp(0.5 * z_log_var) * epsilon" + ] + }, + { + "cell_type": "markdown", + "id": "de4536fc-e359-49fb-bc52-54b9ff244d46", + "metadata": { + "tags": [] + }, + "source": [ + "### Build the encoder" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bc70b33c-f2bd-41fb-83f4-90ac1c4afef7", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "latent_dim = 2\n", + "\n", + "encoder_inputs = keras.Input(shape=(28, 28, 1))\n", + "x = layers.Conv2D(32, 3, activation=\"relu\", strides=2, padding=\"same\")(encoder_inputs)\n", + "x = layers.Conv2D(64, 3, activation=\"relu\", strides=2, padding=\"same\")(x)\n", + "x = layers.Flatten()(x)\n", + "x = layers.Dense(16, activation=\"relu\")(x)\n", + "z_mean = layers.Dense(latent_dim, name=\"z_mean\")(x)\n", + "z_log_var = layers.Dense(latent_dim, name=\"z_log_var\")(x)\n", + "z = Sampling()([z_mean, z_log_var])\n", + "encoder = keras.Model(encoder_inputs, [z_mean, z_log_var, z], name=\"encoder\")\n", + "encoder.summary()" + ] + }, + { + "cell_type": "markdown", + "id": "91004269-b7a7-4d19-8812-729cd8366b9e", + "metadata": {}, + "source": [ + "### Build the decoder" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bab93526-4805-4a62-95a8-3dbceb47950e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "latent_inputs = keras.Input(shape=(latent_dim,))\n", + "x = layers.Dense(7 * 7 * 64, activation=\"relu\")(latent_inputs)\n", + "x = layers.Reshape((7, 7, 64))(x)\n", + "x = layers.Conv2DTranspose(64, 3, activation=\"relu\", strides=2, padding=\"same\")(x)\n", + "x = layers.Conv2DTranspose(32, 3, activation=\"relu\", strides=2, padding=\"same\")(x)\n", + "decoder_outputs = layers.Conv2DTranspose(1, 3, activation=\"sigmoid\", padding=\"same\")(x)\n", + "decoder = keras.Model(latent_inputs, decoder_outputs, name=\"decoder\")\n", + "decoder.summary()" + ] + }, + { + "cell_type": "markdown", + "id": "85405b6c-a092-49ad-9358-5e0c38622c59", + "metadata": {}, + "source": [ + "### Define the VAE as a `Model` with a custom `train_step`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9d2ef055-a698-4458-8b30-8671d3975195", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "class VAE(keras.Model):\n", + " def __init__(self, encoder, decoder, **kwargs):\n", + " super().__init__(**kwargs)\n", + " self.encoder = encoder\n", + " self.decoder = decoder\n", + " self.total_loss_tracker = keras.metrics.Mean(name=\"total_loss\")\n", + " self.reconstruction_loss_tracker = keras.metrics.Mean(\n", + " name=\"reconstruction_loss\"\n", + " )\n", + " self.kl_loss_tracker = keras.metrics.Mean(name=\"kl_loss\")\n", + "\n", + " @property\n", + " def metrics(self):\n", + " return [\n", + " self.total_loss_tracker,\n", + " self.reconstruction_loss_tracker,\n", + " self.kl_loss_tracker,\n", + " ]\n", + "\n", + " def train_step(self, data):\n", + " with tf.GradientTape() as tape:\n", + " z_mean, z_log_var, z = self.encoder(data)\n", + " reconstruction = self.decoder(z)\n", + " reconstruction_loss = ops.mean(\n", + " ops.sum(\n", + " keras.losses.binary_crossentropy(data, reconstruction),\n", + " axis=(1, 2),\n", + " )\n", + " )\n", + " kl_loss = -0.5 * (1 + z_log_var - ops.square(z_mean) - ops.exp(z_log_var))\n", + " kl_loss = ops.mean(ops.sum(kl_loss, axis=1))\n", + " total_loss = reconstruction_loss + kl_loss\n", + " grads = tape.gradient(total_loss, self.trainable_weights)\n", + " self.optimizer.apply_gradients(zip(grads, self.trainable_weights))\n", + " self.total_loss_tracker.update_state(total_loss)\n", + " self.reconstruction_loss_tracker.update_state(reconstruction_loss)\n", + " self.kl_loss_tracker.update_state(kl_loss)\n", + " return {\n", + " \"loss\": self.total_loss_tracker.result(),\n", + " \"reconstruction_loss\": self.reconstruction_loss_tracker.result(),\n", + " \"kl_loss\": self.kl_loss_tracker.result(),\n", + " }" + ] + }, + { + "cell_type": "markdown", + "id": "0943c16f-dff8-479e-a7ee-dc38cb68249c", + "metadata": {}, + "source": [ + "## DVC\n", + "\n", + "[Data Version Control](https://dvc.org/doc/user-guide) is an open-source tool for handling machine learning projects. It helps with data management, ML pipeline automation, and experiment management, making projects reproducible and collaboration better. DVC integrates with Git to track changes in your data and models, enabling you to version control your machine learning workflow in a way similar to how you manage code.\n", + "\n", + "-------------------------------------------------------------------------------------------------------------------------------------\n", + "\n", + "In the lines below, the `&&` symbol is used multiple times. This symbol is originally a logical operator (the command on the right will only run if the command on the left executes successfully). However, when using `!` in a Jupyter notebook, the Linux commands are executed within the directory where the notebook is currently located. This behavior prevents commands from being run in separate directories if `!cd` is on its own line. Using `&&` ensures that the `dvc` commands are executed within the `dvc` submodule directory, without affecting the repository where the notebook resides." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e83ae770-6a77-469b-b7ef-88698007156b", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "dvc_repo_link = \"git@github.com:oobielodan/digits_dvc.git\" # \n", + "dvc_storage = \"/demo-bucket\" # " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "352c4ccb-6144-4ac4-a692-c04ec4ef2ff6", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# grab your dvc repository -> the --force flag allows for this to still run if the submodule had already been created at a prior time\n", + "!git submodule add --force \"{dvc_repo_link}\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5e481ef0-3ceb-44e6-9777-4a588b8a3921", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "dvc_repo = \"digits_dvc\" # -> should appear as a folder in the current directory" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "234e2ab8-16cd-4175-ab32-a501bbdfcd59", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# DVC initialization and storage set up\n", + "!cd \"{dvc_repo}\" && dvc init\n", + "!cd \"{dvc_repo}\" && dvc remote add -d dvcstorage \"{dvc_storage}\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1711066f-c8b6-46e2-9cd4-c90be29c053f", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# initial commit to git\n", + "!cd \"{dvc_repo}\" && git add .\n", + "!cd \"{dvc_repo}\" && git commit -m \"loaded dependencies, mkdir -p, DVC init\"" + ] + }, + { + "cell_type": "markdown", + "id": "581b6282-650d-4e92-9537-811e4e09754d", + "metadata": {}, + "source": [ + "## MLFlow\n", + "MLflow is designed to help simplify the ML workflow, assisting users throughout the various stages of development and deployment. In this notebook, we use its autologging capabilities to log information on the model, its parameters and results, etc. Documentation and more information can be found at [the MLFlow website](https://mlflow.org/docs/latest/index.html).\n", + "\n", + "-------------------------------------------------------------------------------------------------------------------------------------\n", + "\n", + "To get started with MLflow, run `mlflow server --host 127.0.0.1 --port 8080` in the command line. The `mlflow server` command needs to run in the background and therefore cannot be executed directly in a Jupyter notebook, as each cell must complete execution before the next one can run." + ] + }, + { + "cell_type": "markdown", + "id": "6018ac9f-99fe-438d-8942-8285ced96f03", + "metadata": {}, + "source": [ + "### Configuration\n", + "*If you used a different host and/or port during initialization, make sure to update the following URIs accordingly.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ff10acc3-348f-4437-9767-b6f0d50924b1", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# utilize and set up the initialized server for tracking \n", + "client = MlflowClient(tracking_uri = \"http://127.0.0.1:8080\")\n", + "mlflow.set_tracking_uri(\"http://127.0.0.1:8080\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2a87c393-fc86-437e-876f-f991f38bd1f6", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# view the metadata associated with all the experiments that are currently on the server \n", + "all_experiments = client.search_experiments()\n", + "print(all_experiments)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f15316ea-3e62-407f-9a8b-766c5d46d815", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# example for accessing elements from the returned collections of experiments\n", + "default_experiment = [\n", + " {\"name\": experiment.name, \"lifecycle_stage\": experiment.lifecycle_stage}\n", + " for experiment in all_experiments\n", + " if experiment.name == \"Default\"\n", + "][0]\n", + "\n", + "pprint(default_experiment)" + ] + }, + { + "cell_type": "markdown", + "id": "c53c160f-45da-42be-9ed3-5385510e89e1", + "metadata": { + "tags": [] + }, + "source": [ + "### Experiment 1\n", + "In Experiment 1, we train the Digit CVAE model on multiple datasets. To create these datasets, we split the original dataset into five equal, randomized parts. After each training session, we save the weights and use them as the starting point for retraining the model on the next dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b3a09d3f-a2a4-49fe-b97c-e0bcfda96323", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# provide an experiment description that will appear in the UI\n", + "experiment1_description = (\n", + " \"This is the digits forecasting project.\"\n", + " \"This experiment contains the digit model for randomized numbers (0-9) trained separately.\"\n", + ")\n", + "\n", + "# provide searchable tags for the experiment\n", + "experiment1_tags = {\n", + " \"project_name\": \"digit-forecasting\",\n", + " \"model_type\": \"randomzied\",\n", + " \"team\": \"digit-ml\",\n", + " \"project_quarter\": \"Q3-2024\",\n", + " \"mlflow.note.content\": experiment1_description,\n", + "}\n", + "\n", + "# create the experiment and give it a unique name\n", + "digit_experiment1 = client.create_experiment(\n", + " name=\"Randomize_Model\", tags=experiment1_tags\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "8264adfb-09ee-4218-9c30-421d3ac41e23", + "metadata": {}, + "source": [ + "### Experiment 2\n", + "In Experiment 2, we train the Digit CVAE model on all digit samples simultaneously, without any subsequent retraining using the weights." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fb5258b2-2c71-475d-83fd-d597cfc05454", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# provide an experiment description that will appear in the UI\n", + "experiment2_description = (\n", + " \"This is the digits forecasting project.\"\n", + " \"This experiment contains the digit model for numbers (0-9) trained all together.\"\n", + ")\n", + "\n", + "# provide searchable tags for the experiment\n", + "experiment2_tags = {\n", + " \"project_name\": \"digit-forecasting\",\n", + " \"model_type\": \"all digits\",\n", + " \"team\": \"digit-ml\",\n", + " \"project_quarter\": \"Q3-2024\",\n", + " \"mlflow.note.content\": experiment2_description,\n", + "}\n", + "\n", + "# create the experiment and give it a unique name\n", + "digit_experiment2 = client.create_experiment(\n", + " name=\"Together_Model\", tags=experiment2_tags\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "27de39b5-391a-4305-a317-e15c14a8c4a5", + "metadata": {}, + "source": [ + "### Experiment 3\n", + "In Experiment 3, we revisit the approach used in Experiment 1 - initializing the model with the weights from a previous training session and retraining it from there. However in this experiment, we train the Digit CVAE model sequentially on each of the 10 digits (0–9), one digit at a time. After each training session, we save the weights and use them to retrain the model on the next digit. This approach induces a 'forgetting' effect, where the model gradually loses its ability to recognize previous digits with each subsequent training session." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6d8e4009-d290-4053-93e9-b665b04c5aea", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# provide an experiment description that will appear in the UI\n", + "experiment3_description = (\n", + " \"This is the digits forecasting project.\"\n", + " \"This experiment contains the digit model for each of the numbers (0-9) trained separately.\"\n", + ")\n", + "\n", + "# provide searchable tags that define characteristics of the runs that will be in this experiment\n", + "experiment3_tags = {\n", + " \"project_name\": \"digit-forecasting\",\n", + " \"model_type\": \"sequential\",\n", + " \"team\": \"digit-ml\",\n", + " \"project_quarter\": \"Q3-2024\",\n", + " \"mlflow.note.content\": experiment3_description,\n", + "}\n", + "\n", + "# create the experiment and give it a unique name\n", + "digit_experiment3 = client.create_experiment(\n", + " name=\"Sequenced_Model\", tags=experiment3_tags\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "3234d529-70a9-4806-a3da-fc55d3564864", + "metadata": {}, + "source": [ + "### Experiment Set Up" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a7db30da-8a05-4e72-b1e2-907bc7ea6631", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# save each of the experiment's metadata\n", + "digit_experiment1 = mlflow.set_experiment(\"Randomize_Model\")\n", + "digit_experiment2 = mlflow.set_experiment(\"Together_Model\")\n", + "digit_experiment3 = mlflow.set_experiment(\"Sequenced_Model\")" + ] + }, + { + "cell_type": "markdown", + "id": "a3f662b7-a8c8-4ae4-9f60-b6de69e0ddef", + "metadata": {}, + "source": [ + "## Train the VAE" + ] + }, + { + "cell_type": "markdown", + "id": "4d8c6708-4ea5-45af-8b6c-13406429f4c3", + "metadata": {}, + "source": [ + "*Make sure that 'vae.weights.h5' does not already exist in the model directory if you want to training from the beginning.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c645768a-9b70-4568-8e59-7544678392ee", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "vae = VAE(encoder, decoder)\n", + "vae.compile(optimizer=keras.optimizers.Adam())\n", + "(x_train, Y_train), (x_test, Y_test) = keras.datasets.mnist.load_data()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0cfc6419-1cbc-4261-ac12-92779e526d1b", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "early_stopping_cb = keras.callbacks.EarlyStopping(patience = 5, restore_best_weights = True) # stops training early if the validation loss does not improve\n", + "\n", + "def train_model(num, model, data, experiment):\n", + " if os.path.exists(os.path.join(model_dir, 'vae.weights.h5')): # if the model has already been trained at least once, load that model\n", + " model.load_weights(os.path.join(model_dir, 'vae.weights.h5'))\n", + " \n", + " mlflow.autolog()\n", + " \n", + " run_name = f\"{num}_test\" # define a run name for this iteration of training\n", + " artifact_path = f\"{num}\" # define an artifact path that the model will be saved to\n", + " \n", + " # initiate the MLflow run context - training needs to happen inside of the mlflow run or you will run into problems with double logging\n", + " with mlflow.start_run(run_name = run_name, experiment_id = experiment) as run:\n", + " \n", + " history = model.fit(data, epochs=30, batch_size=128, callbacks = [early_stopping_cb])\n", + " model.save_weights(os.path.join(model_dir, 'vae.weights.h5')) # save model weights after training\n", + "\n", + " hist_pd = pd.DataFrame(history.history)\n", + " hist_pd.to_csv(os.path.join(model_dir, f'history_{num}.csv'), index = False)" + ] + }, + { + "cell_type": "markdown", + "id": "72cf688b-97d3-4a20-bc27-dae71d7528dc", + "metadata": { + "tags": [] + }, + "source": [ + "### Experiment 1\n", + "*How to filter mnist data found here: https://stackoverflow.com/questions/51202181/how-do-i-select-only-a-specific-digit-from-the-mnist-dataset-provided-by-keras*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38582c97-e230-4ac6-a4cd-5518bf02c151", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# retraining the model n times\n", + "count = 0\n", + "n = 5\n", + "\n", + "mnist_digits = np.expand_dims(np.concatenate([x_train, x_test], axis=0), -1).astype(\"float32\") / 255\n", + "\n", + "for arr in np.array_split(mnist_digits, n):\n", + " count += 1\n", + " train_model(f\"rand_{count}\", vae, arr, digit_experiment3.experiment_id)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6cc99efc-4727-4904-ab3a-2bb9df539125", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# add model 1 to dvc \n", + "!cp ./\"{model_dir}\"/vae.weights.h5 \"{dvc_repo}\"/experiment_1.weights.h5\n", + "!sh dvcgit.sh experiment_1.weights.h5 \"digit experiment 1\" \"{dvc_repo}\" \"{env_name}\"\n", + "\n", + "!rm \"{dvc_storage}\"/experiment_1.weights.h5\n", + "!rm ./\"{model_dir}\"/vae.weights.h5" + ] + }, + { + "cell_type": "markdown", + "id": "ade80f16-6a1e-4e26-b4ce-77314d6fff91", + "metadata": {}, + "source": [ + "### Experiment 2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a8849863-fcf5-46dd-997b-78a245dc0e2a", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# train all numbers at the same time\n", + "train_model(\"all\", vae, mnist_digits, digit_experiment2.experiment_id)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "22343e08-f3c6-4fb8-aee5-7a5e24cca01f", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# add model 2 to dvc \n", + "!cp ./\"{model_dir}\"/vae.weights.h5 \"{dvc_repo}\"/experiment_2.weights.h5\n", + "!sh dvcgit.sh experiment_2.weights.h5 \"digit experiment 2\" \"{dvc_repo}\" \"{env_name}\"\n", + "\n", + "!rm \"{dvc_repo}\"/experiment_2.weights.h5\n", + "!rm ./\"{model_dir}\"/vae.weights.h5" + ] + }, + { + "cell_type": "markdown", + "id": "7cc7acce-d93a-4724-9dfa-4b970b969414", + "metadata": {}, + "source": [ + "### Experiment 3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b7662d9d-9844-4f16-8a90-7c4642675abe", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# training one number at a time\n", + "for num in np.arange(10):\n", + " train_filter = np.where(Y_train == num)\n", + " test_filter = np.where(Y_test == num)\n", + " \n", + " x_trn = x_train[train_filter]\n", + " x_tst = x_test[test_filter]\n", + " \n", + " mnist_digits = np.expand_dims(np.concatenate([x_trn, x_tst], axis=0), -1).astype(\"float32\") / 255\n", + " train_model(num, vae, mnist_digits, digit_experiment1.experiment_id)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ea85634-da45-498e-98fa-46f413516499", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# add model 3 to dvc \n", + "!cp ./\"{model_dir}\"/vae.weights.h5 \"{dvc_repo}\"/experiment_3.weights.h5\n", + "!sh dvcgit.sh experiment_3.weights.h5 \"digit experiment 3\" \"{dvc_repo}\" \"{env_name}\"\n", + "\n", + "!rm \"{dvc_repo}\"/experiment_3.weights.h5\n", + "!rm ./\"{model_dir}\"/vae.weights.h5" + ] + }, + { + "cell_type": "markdown", + "id": "0f35ce03-037f-4f63-98aa-ef411029326f", + "metadata": {}, + "source": [ + "------------------------------------------------------------------------------------------------\n", + "*`dvcgit.sh` is a script used for dvc and git tracking. The correct call is as follows (all arguments are required):* `sh dvcgit.sh `" + ] + }, + { + "cell_type": "markdown", + "id": "7494fca6-d4b7-4ef3-91a5-4f1422526fd5", + "metadata": { + "tags": [] + }, + "source": [ + "## Display a grid of reconstructed digits in the latent space" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a7d63bff-e2c6-40a1-a8f8-d375b008557e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "def plot_latent_space(vae, n=30, figsize=15):\n", + " # display a n*n 2D manifold of digits\n", + " digit_size = 28\n", + " scale = 1.0\n", + " figure = np.zeros((digit_size * n, digit_size * n))\n", + " # linearly spaced coordinates corresponding to the 2D plot\n", + " # of digit classes in the latent space\n", + " grid_x = np.linspace(-scale, scale, n)\n", + " grid_y = np.linspace(-scale, scale, n)[::-1]\n", + "\n", + " for i, yi in enumerate(grid_y):\n", + " for j, xi in enumerate(grid_x):\n", + " z_sample = np.array([[xi, yi]])\n", + " x_decoded = vae.decoder.predict(z_sample, verbose=0)\n", + " digit = x_decoded[0].reshape(digit_size, digit_size)\n", + " figure[\n", + " i * digit_size : (i + 1) * digit_size,\n", + " j * digit_size : (j + 1) * digit_size,\n", + " ] = digit\n", + "\n", + " plt.figure(figsize=(figsize, figsize))\n", + " start_range = digit_size // 2\n", + " end_range = n * digit_size + start_range\n", + " pixel_range = np.arange(start_range, end_range, digit_size)\n", + " sample_range_x = np.round(grid_x, 1)\n", + " sample_range_y = np.round(grid_y, 1)\n", + " plt.xticks(pixel_range, sample_range_x)\n", + " plt.yticks(pixel_range, sample_range_y)\n", + " plt.xlabel(\"z[0]\")\n", + " plt.ylabel(\"z[1]\")\n", + " plt.imshow(figure, cmap=\"Greys_r\")\n", + " plt.show()\n", + "\n", + "plot_latent_space(vae)" + ] + }, + { + "cell_type": "markdown", + "id": "fbc89d55-5962-463f-ab7f-f0332e2e3430", + "metadata": {}, + "source": [ + "## Display how the latent space clusters digits" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "68dabfbc-7024-4831-8432-6682c42dab7f", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def plot_label_clusters(vae, data, labels):\n", + " # display a 2D plot of the digit classes in the latent space\n", + " z_mean, _, _ = vae.encoder.predict(data, verbose=0)\n", + " plt.figure(figsize=(12, 10))\n", + " plt.scatter(z_mean[:, 0], z_mean[:, 1], c=labels)\n", + " plt.colorbar()\n", + " plt.xlabel(\"z[0]\")\n", + " plt.ylabel(\"z[1]\")\n", + " plt.show()\n", + "\n", + "(x_train, y_train), _ = keras.datasets.mnist.load_data()\n", + "x_train = np.expand_dims(x_train, -1).astype(\"float32\") / 255\n", + "\n", + "plot_label_clusters(vae, x_train, y_train)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:digits_env]", + "language": "python", + "name": "conda-env-digits_env-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/run_on_cluster/cvae-demos/digits-demo/digits_env.yaml b/run_on_cluster/cvae-demos/digits-demo/digits_env.yaml new file mode 100644 index 0000000..a1d99e4 --- /dev/null +++ b/run_on_cluster/cvae-demos/digits-demo/digits_env.yaml @@ -0,0 +1,370 @@ +name: digits_env +channels: + - anaconda + - conda-forge + - defaults +dependencies: + - _libgcc_mutex=0.1=conda_forge + - _openmp_mutex=4.5=2_gnu + - absl-py=2.1.0=pyhd8ed1ab_0 + - aiohappyeyeballs=2.3.5=pyhd8ed1ab_0 + - aiohttp=3.10.3=py39hcd6043d_0 + - aiohttp-retry=2.8.3=pyhd8ed1ab_0 + - aiosignal=1.3.1=pyhd8ed1ab_0 + - alsa-lib=1.2.12=h4ab18f5_0 + - amqp=5.2.0=pyhd8ed1ab_1 + - annotated-types=0.7.0=pyhd8ed1ab_0 + - antlr-python-runtime=4.9.3=pyhd8ed1ab_1 + - appdirs=1.4.4=pyh9f0ad1d_0 + - asttokens=2.0.5=pyhd3eb1b0_0 + - astunparse=1.6.3=pyhd8ed1ab_0 + - async-timeout=4.0.3=pyhd8ed1ab_0 + - asyncssh=2.14.1=pyhd8ed1ab_0 + - atk-1.0=2.38.0=h04ea711_2 + - atpublic=3.0.1=pyhd8ed1ab_0 + - attrs=24.2.0=pyh71513ae_0 + - backcall=0.2.0=pyhd3eb1b0_0 + - backports.zoneinfo=0.2.1=py39hf3d152e_8 + - billiard=4.2.0=py39hd1e30aa_0 + - brotli=1.1.0=hd590300_1 + - brotli-bin=1.1.0=hd590300_1 + - brotli-python=1.1.0=py39h3d6467e_1 + - bzip2=1.0.8=h4bc722e_7 + - c-ares=1.33.0=ha66036c_0 + - ca-certificates=2024.7.4=hbcca054_0 + - cached-property=1.5.2=hd8ed1ab_1 + - cached_property=1.5.2=pyha770c72_1 + - cairo=1.18.0=hebfffa5_3 + - celery=5.3.5=pyhd8ed1ab_0 + - certifi=2024.7.4=py39h06a4308_0 + - cffi=1.17.0=py39h49a4b6b_0 + - charset-normalizer=3.3.2=pyhd8ed1ab_0 + - click=8.1.7=unix_pyh707e725_0 + - click-didyoumean=0.3.1=pyhd8ed1ab_0 + - click-plugins=1.1.1=py_0 + - click-repl=0.3.0=pyhd8ed1ab_0 + - colorama=0.4.6=pyhd8ed1ab_0 + - comm=0.2.1=py39h06a4308_0 + - configobj=5.0.8=pyhd8ed1ab_0 + - contourpy=1.2.1=py39h7633fee_0 + - cryptography=43.0.0=py39h5c34e2d_0 + - cuda-crt-tools=12.6.20=ha770c72_0 + - cuda-cudart=12.6.37=he02047a_0 + - cuda-cudart_linux-64=12.6.37=h85509e4_0 + - cuda-nvcc-tools=12.6.20=he02047a_0 + - cuda-nvrtc=12.6.20=he02047a_0 + - cuda-nvtx=12.6.37=he02047a_0 + - cuda-nvvm-tools=12.6.20=he02047a_0 + - cuda-version=12.6=h7480c83_3 + - cudnn=8.9.7.29=h092f7fd_3 + - cycler=0.12.1=pyhd8ed1ab_0 + - dbus=1.13.6=h5008d03_3 + - debugpy=1.6.7=py39h6a678d5_0 + - decorator=5.1.1=pyhd8ed1ab_0 + - dictdiffer=0.9.0=pyhd8ed1ab_0 + - diskcache=5.6.3=pyhd8ed1ab_0 + - distro=1.9.0=pyhd8ed1ab_0 + - double-conversion=3.3.0=h59595ed_0 + - dpath=2.2.0=pyha770c72_0 + - dulwich=0.22.1=py39ha68c5e3_0 + - dvc=3.53.2=pyhd8ed1ab_0 + - dvc-data=3.15.2=pyhd8ed1ab_0 + - dvc-http=2.32.0=pyhd8ed1ab_0 + - dvc-objects=5.1.0=pyhd8ed1ab_1 + - dvc-render=1.0.2=pyhd8ed1ab_0 + - dvc-studio-client=0.21.0=pyhd8ed1ab_0 + - dvc-task=0.4.0=pyhd8ed1ab_0 + - entrypoints=0.4=pyhd8ed1ab_0 + - exceptiongroup=1.2.0=py39h06a4308_0 + - executing=0.8.3=pyhd3eb1b0_0 + - expat=2.6.2=h59595ed_0 + - filelock=3.15.4=pyhd8ed1ab_0 + - flatbuffers=24.3.25=h59595ed_0 + - flatten-dict=0.4.2=pyhd8ed1ab_1 + - flufl.lock=8.1.0=pyhd8ed1ab_0 + - font-ttf-dejavu-sans-mono=2.37=hab24e00_0 + - font-ttf-inconsolata=3.000=h77eed37_0 + - font-ttf-source-code-pro=2.038=h77eed37_0 + - font-ttf-ubuntu=0.83=h77eed37_2 + - fontconfig=2.14.2=h14ed4e7_0 + - fonts-conda-ecosystem=1=0 + - fonts-conda-forge=1=0 + - fonttools=4.53.1=py39hcd6043d_0 + - freetype=2.12.1=h267a509_2 + - fribidi=1.0.10=h36c2ea0_0 + - frozenlist=1.4.1=py39hd1e30aa_0 + - fsspec=2024.6.1=pyhff2d567_0 + - funcy=2.0=pyhd8ed1ab_0 + - future=1.0.0=pyhd8ed1ab_0 + - gast=0.5.5=pyhd8ed1ab_0 + - gdk-pixbuf=2.42.12=hb9ae30d_0 + - giflib=5.2.2=hd590300_0 + - gitdb=4.0.11=pyhd8ed1ab_0 + - gitpython=3.1.43=pyhd8ed1ab_0 + - google-pasta=0.2.0=pyhd8ed1ab_1 + - grandalf=0.7=pyhd8ed1ab_0 + - graphite2=1.3.13=h59595ed_1003 + - graphviz=12.0.0=hba01fac_0 + - grpcio=1.62.2=py39h174d805_0 + - gtk2=2.24.33=h6470451_5 + - gto=1.7.1=pyhd8ed1ab_1 + - gts=0.7.6=h977cf35_4 + - h2=4.1.0=pyhd8ed1ab_0 + - h5py=3.11.0=nompi_py39h24b94d4_102 + - harfbuzz=9.0.0=hda332d3_1 + - hdf5=1.14.3=nompi_hdf9ad27_105 + - hpack=4.0.0=pyh9f0ad1d_0 + - hydra-core=1.3.2=pyhd8ed1ab_0 + - hyperframe=6.0.1=pyhd8ed1ab_0 + - icu=75.1=he02047a_0 + - idna=3.7=pyhd8ed1ab_0 + - importlib-resources=6.4.0=pyhd8ed1ab_0 + - importlib_metadata=8.2.0=hd8ed1ab_0 + - importlib_resources=6.4.0=pyhd8ed1ab_0 + - ipykernel=6.28.0=py39h06a4308_0 + - ipython=8.15.0=py39h06a4308_0 + - iterative-telemetry=0.0.8=pyhd8ed1ab_0 + - jedi=0.19.1=py39h06a4308_0 + - jinja2=3.1.4=py39h06a4308_0 + - jupyter_client=8.6.0=py39h06a4308_0 + - jupyter_core=5.7.2=py39h06a4308_0 + - keras=3.4.1=pyhd8ed1ab_2 + - keyutils=1.6.1=h166bdaf_0 + - kiwisolver=1.4.5=py39h7633fee_1 + - kombu=5.3.3=py39hf3d152e_0 + - krb5=1.21.3=h659f571_0 + - lcms2=2.16=hb7c19ff_0 + - ld_impl_linux-64=2.38=h1181459_1 + - lerc=4.0.0=h27087fc_0 + - libabseil=20240116.2=cxx17_he02047a_1 + - libaec=1.1.3=h59595ed_0 + - libblas=3.9.0=23_linux64_openblas + - libbrotlicommon=1.1.0=hd590300_1 + - libbrotlidec=1.1.0=hd590300_1 + - libbrotlienc=1.1.0=hd590300_1 + - libcblas=3.9.0=23_linux64_openblas + - libclang-cpp18.1=18.1.8=default_hf981a13_2 + - libclang13=18.1.8=default_h9def88c_2 + - libcublas=12.6.0.22=he02047a_0 + - libcufft=11.2.6.28=he02047a_0 + - libcups=2.3.3=h4637d8d_4 + - libcurand=10.3.7.37=he02047a_0 + - libcurl=8.9.1=hdb1bdb2_0 + - libcusolver=11.6.4.38=he02047a_0 + - libcusparse=12.5.2.23=he02047a_0 + - libdeflate=1.21=h4bc722e_0 + - libdrm=2.4.122=h4ab18f5_0 + - libedit=3.1.20191231=he28a2e2_2 + - libev=4.33=hd590300_2 + - libexpat=2.6.2=h59595ed_0 + - libffi=3.4.4=h6a678d5_1 + - libgcc-ng=14.1.0=h77fa898_0 + - libgd=2.3.3=hd3e95f3_10 + - libgfortran-ng=14.1.0=h69a702a_0 + - libgfortran5=14.1.0=hc5f4f2c_0 + - libgit2=1.8.1=he8d1d4c_1 + - libglib=2.80.3=h315aac3_2 + - libgomp=14.1.0=h77fa898_0 + - libgrpc=1.62.2=h15f2491_0 + - libiconv=1.17=hd590300_2 + - libjpeg-turbo=3.0.0=hd590300_1 + - liblapack=3.9.0=23_linux64_openblas + - libllvm18=18.1.8=h8b73ec9_2 + - libnghttp2=1.58.0=h47da74e_1 + - libnsl=2.0.1=hd590300_0 + - libnvjitlink=12.6.20=he02047a_0 + - libopenblas=0.3.27=pthreads_hac2b453_1 + - libpciaccess=0.18=hd590300_0 + - libpng=1.6.43=h2797004_0 + - libpq=16.4=h482b261_0 + - libprotobuf=4.25.3=h08a7969_0 + - libre2-11=2023.09.01=h5a48ba9_2 + - librsvg=2.58.2=h9564881_1 + - libsodium=1.0.18=h7b6447c_0 + - libsqlite=3.46.0=hde9e2c9_0 + - libssh2=1.11.0=h0841786_0 + - libstdcxx-ng=14.1.0=hc0a3c3a_0 + - libtiff=4.6.0=h46a8edc_4 + - libuuid=2.38.1=h0b41bf4_0 + - libwebp-base=1.4.0=hd590300_0 + - libxcb=1.16=hd590300_0 + - libxcrypt=4.4.36=hd590300_1 + - libxkbcommon=1.7.0=h2c5496b_1 + - libxml2=2.12.7=he7c6b58_4 + - libxslt=1.1.39=h76b75d6_0 + - libzlib=1.3.1=h4ab18f5_1 + - markdown=3.6=pyhd8ed1ab_0 + - markdown-it-py=3.0.0=pyhd8ed1ab_0 + - markupsafe=2.1.5=py39hd1e30aa_0 + - matplotlib=3.9.1=py39hf3d152e_2 + - matplotlib-base=3.9.1=py39h0565ad7_2 + - matplotlib-inline=0.1.6=py39h06a4308_0 + - mdurl=0.1.2=pyhd8ed1ab_0 + - ml_dtypes=0.4.0=py39hfc16268_1 + - multidict=6.0.5=py39hd1e30aa_0 + - munkres=1.1.4=pyh9f0ad1d_0 + - mysql-common=8.3.0=h70512c7_5 + - mysql-libs=8.3.0=ha479ceb_5 + - namex=0.0.8=pyhd8ed1ab_0 + - nccl=2.22.3.1=hbc370b7_1 + - ncurses=6.4=h6a678d5_0 + - nest-asyncio=1.6.0=py39h06a4308_0 + - networkx=3.2.1=pyhd8ed1ab_0 + - numpy=1.26.4=py39h474f0d3_0 + - omegaconf=2.3.0=pyhd8ed1ab_0 + - openjpeg=2.5.2=h488ebb8_0 + - openssl=3.3.1=h4bc722e_2 + - opt_einsum=3.3.0=pyhc1e730c_2 + - optree=0.12.1=py39hedd12f3_0 + - orjson=3.10.7=py39h5cde264_0 + - packaging=24.1=pyhd8ed1ab_0 + - pandas=2.2.2=py39hfc16268_1 + - pango=1.54.0=h4c5309f_1 + - parso=0.8.3=pyhd3eb1b0_0 + - pathlib2=2.3.7.post1=py39hf3d152e_3 + - pathspec=0.12.1=pyhd8ed1ab_0 + - pcre2=10.44=hba22ea6_2 + - pexpect=4.8.0=pyhd3eb1b0_3 + - pickleshare=0.7.5=pyhd3eb1b0_1003 + - pillow=10.4.0=py39h16a7006_0 + - pip=24.2=py39h06a4308_0 + - pixman=0.43.2=h59595ed_0 + - platformdirs=3.11.0=pyhd8ed1ab_0 + - prompt-toolkit=3.0.47=pyha770c72_0 + - prompt_toolkit=3.0.47=hd8ed1ab_0 + - protobuf=4.25.3=py39h1be52a0_0 + - psutil=6.0.0=py39hd3abc70_0 + - pthread-stubs=0.4=h36c2ea0_1001 + - ptyprocess=0.7.0=pyhd3eb1b0_2 + - pure_eval=0.2.2=pyhd3eb1b0_0 + - pycparser=2.22=pyhd8ed1ab_0 + - pydantic=2.8.2=pyhd8ed1ab_0 + - pydantic-core=2.20.1=py39h5cde264_0 + - pydot=3.0.1=py39hf3d152e_0 + - pygit2=1.15.1=py39hcd6043d_0 + - pygments=2.18.0=pyhd8ed1ab_0 + - pygtrie=2.5.0=pyhd8ed1ab_0 + - pyopenssl=24.2.1=pyhd8ed1ab_2 + - pyparsing=3.1.2=pyhd8ed1ab_0 + - pyside6=6.7.2=py39h8242bd1_2 + - pysocks=1.7.1=pyha2e5f31_6 + - python=3.9.18=h0755675_1_cpython + - python-dateutil=2.9.0=pyhd8ed1ab_0 + - python-flatbuffers=24.3.25=pyh59ac667_0 + - python-gssapi=1.8.3=py39h01551a1_0 + - python-tzdata=2024.1=pyhd8ed1ab_0 + - python_abi=3.9=4_cp39 + - pytz=2024.1=pyhd8ed1ab_0 + - pywin32-on-windows=0.1.0=pyh1179c8e_3 + - pyyaml=6.0.2=py39hcd6043d_0 + - pyzmq=25.1.2=py39h6a678d5_0 + - qhull=2020.2=h434a139_5 + - qt6-main=6.7.2=hb12f9c5_4 + - re2=2023.09.01=h7f4b329_2 + - readline=8.2=h5eee18b_0 + - requests=2.32.3=py39h06a4308_0 + - rich=13.7.1=pyhd8ed1ab_0 + - ruamel.yaml=0.18.6=py39hd1e30aa_0 + - ruamel.yaml.clib=0.2.8=py39hd1e30aa_0 + - scmrepo=3.3.7=pyhd8ed1ab_0 + - semver=3.0.2=pyhd8ed1ab_0 + - setuptools=72.1.0=py39h06a4308_0 + - shellingham=1.5.4=pyhd8ed1ab_0 + - shortuuid=1.0.13=pyhd8ed1ab_0 + - shtab=1.7.1=pyhd8ed1ab_0 + - six=1.16.0=pyh6c4a22f_0 + - smmap=5.0.0=pyhd8ed1ab_0 + - snappy=1.2.1=ha2e4443_0 + - sqlite=3.32.3=hcee41ef_1 + - sqltrie=0.11.1=pyhd8ed1ab_0 + - stack_data=0.2.0=pyhd3eb1b0_0 + - tabulate=0.9.0=pyhd8ed1ab_1 + - tensorboard=2.17.0=pyhd8ed1ab_0 + - tensorboard-data-server=0.7.0=py39hd4f0224_1 + - tensorflow=2.17.0=cuda120py39h298b457_0 + - tensorflow-base=2.17.0=cuda120py39hee30cbf_0 + - tensorflow-estimator=2.17.0=cuda120py39h225b957_0 + - termcolor=2.4.0=pyhd8ed1ab_0 + - tk=8.6.13=noxft_h4845f30_101 + - tomlkit=0.13.0=pyha770c72_0 + - tornado=6.4.1=py39hd3abc70_0 + - tqdm=4.66.5=pyhd8ed1ab_0 + - traitlets=5.14.3=py39h06a4308_0 + - typer=0.12.3=pyhd8ed1ab_0 + - typer-slim=0.12.3=pyhd8ed1ab_0 + - typer-slim-standard=0.12.3=hd8ed1ab_0 + - typing-extensions=4.12.2=hd8ed1ab_0 + - typing_extensions=4.12.2=pyha770c72_0 + - tzdata=2024a=h04d1e81_0 + - unicodedata2=15.1.0=py39hd1e30aa_0 + - urllib3=2.2.2=pyhd8ed1ab_1 + - vine=5.1.0=pyhd8ed1ab_0 + - voluptuous=0.15.2=pyhd8ed1ab_0 + - wayland=1.23.0=h5291e77_0 + - wcwidth=0.2.13=pyhd8ed1ab_0 + - werkzeug=3.0.3=pyhd8ed1ab_0 + - wheel=0.43.0=py39h06a4308_0 + - wrapt=1.16.0=py39hd1e30aa_0 + - xcb-util=0.4.1=hb711507_2 + - xcb-util-cursor=0.1.4=h4ab18f5_2 + - xcb-util-image=0.4.0=hb711507_2 + - xcb-util-keysyms=0.4.1=hb711507_0 + - xcb-util-renderutil=0.3.10=hb711507_0 + - xcb-util-wm=0.4.2=hb711507_0 + - xkeyboard-config=2.42=h4ab18f5_0 + - xorg-kbproto=1.0.7=h7f98852_1002 + - xorg-libice=1.1.1=hd590300_0 + - xorg-libsm=1.2.4=h7391055_0 + - xorg-libx11=1.8.9=hb711507_1 + - xorg-libxau=1.0.11=hd590300_0 + - xorg-libxdmcp=1.1.3=h7f98852_0 + - xorg-libxext=1.3.4=h0b41bf4_2 + - xorg-libxrender=0.9.11=hd590300_0 + - xorg-renderproto=0.11.1=h7f98852_1002 + - xorg-xextproto=7.3.0=h0b41bf4_1003 + - xorg-xproto=7.0.31=h7f98852_1007 + - xz=5.4.6=h5eee18b_1 + - yaml=0.2.5=h7f98852_2 + - yarl=1.9.4=py39hd1e30aa_0 + - zc.lockfile=3.0.post1=pyhd8ed1ab_0 + - zeromq=4.3.5=h6a678d5_0 + - zipp=3.19.2=pyhd8ed1ab_0 + - zlib=1.3.1=h4ab18f5_1 + - zstandard=0.23.0=py39h623c9ba_0 + - zstd=1.5.6=ha6fb4c9_0 + - pip: + - alembic==1.13.2 + - aniso8601==9.0.1 + - blinker==1.8.2 + - cachetools==5.4.0 + - cloudpickle==3.0.0 + - databricks-sdk==0.30.0 + - deprecated==1.2.14 + - docker==7.1.0 + - flask==3.0.3 + - google-auth==2.33.0 + - graphene==3.3 + - graphql-core==3.2.3 + - graphql-relay==3.2.0 + - greenlet==3.0.3 + - gunicorn==22.0.0 + - importlib-metadata==7.2.1 + - itsdangerous==2.2.0 + - joblib==1.4.2 + - mako==1.3.5 + - mlflow==2.15.1 + - mlflow-skinny==2.15.1 + - opentelemetry-api==1.26.0 + - opentelemetry-sdk==1.26.0 + - opentelemetry-semantic-conventions==0.47b0 + - pyarrow==15.0.2 + - pyasn1==0.6.0 + - pyasn1-modules==0.4.0 + - querystring-parser==1.2.4 + - rsa==4.9 + - scikit-learn==1.5.1 + - scipy==1.13.1 + - sqlalchemy==2.0.32 + - sqlparse==0.5.1 + - threadpoolctl==3.5.0 +prefix: /home/lobielodan/pw/.miniconda3c/envs/digits_env diff --git a/run_on_cluster/cvae-demos/digits-demo/dvcgit.sh b/run_on_cluster/cvae-demos/digits-demo/dvcgit.sh new file mode 100644 index 0000000..89d6411 --- /dev/null +++ b/run_on_cluster/cvae-demos/digits-demo/dvcgit.sh @@ -0,0 +1,25 @@ +#!/bin/bash + +# Run the git commands using input from the command line; +# $1 stores the first entry on the command line that launches +# the shell script and so on. Add as many as you need. + +file_name=$1 +commit_message="$2" +dvc_dir="$3" +env_name="$4" + +# Start Conda and select env (you will need to cross-check the paths and env name) + +source ~/pw/.miniconda3c/etc/profile.d/conda.sh +conda activate "$env_name" + +cd "$dvc_dir" + +dvc add "$file_name" +git add .gitignore +git add "${file_name}.dvc" + +dvc push +git commit -m "$commit_message" +git push \ No newline at end of file diff --git a/run_on_cluster/cvae-demos/weather-demo/.ipynb_checkpoints/cvae_log-checkpoint.ipynb b/run_on_cluster/cvae-demos/weather-demo/.ipynb_checkpoints/cvae_log-checkpoint.ipynb new file mode 100644 index 0000000..066f2cd --- /dev/null +++ b/run_on_cluster/cvae-demos/weather-demo/.ipynb_checkpoints/cvae_log-checkpoint.ipynb @@ -0,0 +1,3027 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f6226ca5-0869-4973-8369-3469cbdff43c", + "metadata": { + "papermill": { + "duration": 0.002878, + "end_time": "2024-07-25T14:33:23.439471", + "exception": false, + "start_time": "2024-07-25T14:33:23.436593", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# **CVAE Training**" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "094dca89-dffa-4a56-8263-371fb372a2cb", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-25T14:33:23.450909Z", + "iopub.status.busy": "2024-07-25T14:33:23.450607Z", + "iopub.status.idle": "2024-07-25T14:33:26.110522Z", + "shell.execute_reply": "2024-07-25T14:33:26.109977Z" + }, + "papermill": { + "duration": 2.66821, + "end_time": "2024-07-25T14:33:26.111708", + "exception": false, + "start_time": "2024-07-25T14:33:23.443498", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:33:24.314602: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:10575] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2024-07-25 14:33:24.314670: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:479] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2024-07-25 14:33:24.316256: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1442] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2024-07-25 14:33:24.322678: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TF version: 2.16.2\n", + "GPU is available\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:33:26.057549: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2024-07-25 14:33:26.103956: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2024-07-25 14:33:26.105981: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n" + ] + } + ], + "source": [ + "import os, json\n", + "\n", + "import papermill as pm\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import tensorflow as tf\n", + "import netCDF4\n", + "import cartopy\n", + "\n", + "from tensorflow import keras\n", + "from keras import layers\n", + "from sklearn.model_selection import train_test_split \n", + "\n", + "print(\"TF version:\", tf.__version__)\n", + "print(\"GPU is\", \"available\" if tf.config.list_physical_devices('GPU') else \"NOT AVAILABLE\")" + ] + }, + { + "cell_type": "markdown", + "id": "06a8b533-cdaa-4efd-938b-ef1d5a84338e", + "metadata": { + "papermill": { + "duration": 0.004051, + "end_time": "2024-07-25T14:33:26.118482", + "exception": false, + "start_time": "2024-07-25T14:33:26.114431", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Download and Convert Data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4b08e8b0-f9fa-40b9-852b-8650e681cff2", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-25T14:33:26.129663Z", + "iopub.status.busy": "2024-07-25T14:33:26.129164Z", + "iopub.status.idle": "2024-07-25T14:33:26.132328Z", + "shell.execute_reply": "2024-07-25T14:33:26.131850Z" + }, + "papermill": { + "duration": 0.009123, + "end_time": "2024-07-25T14:33:26.133395", + "exception": false, + "start_time": "2024-07-25T14:33:26.124272", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "data_pdir = \"./gefs_data\"\n", + "data_dir = \"./gefs_data/converted/\"\n", + "model_dir = './model_dir'" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2ff4403c-9c45-4129-902b-93ebfe9749e3", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-25T14:33:26.141466Z", + "iopub.status.busy": "2024-07-25T14:33:26.141114Z", + "iopub.status.idle": "2024-07-25T14:33:26.151581Z", + "shell.execute_reply": "2024-07-25T14:33:26.151111Z" + }, + "papermill": { + "duration": 0.017279, + "end_time": "2024-07-25T14:33:26.152714", + "exception": false, + "start_time": "2024-07-25T14:33:26.135435", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# data definitions\n", + "\n", + "from scripts.get_data import download_file\n", + "from scripts.get_data import convert_file\n", + "from scripts.get_data import subset_file\n", + "from scripts.get_data import remove_data # removes all data\n", + "\n", + "# data loading\n", + "def load_data(data_dir): \n", + " files = [f for f in os.listdir(data_dir) if ('subset' in f and 'tmp' not in f)]\n", + " \n", + " all_data = ((np.expand_dims(\n", + " np.concatenate(\n", + " [netCDF4.Dataset(data_dir + converted_file)['msl'][:] for converted_file in files]\n", + " ),\n", + " -1\n", + " ).astype(\"float32\") - 85000) / (110000 - 85000)).astype(\"float16\")\n", + " \n", + " return all_data" + ] + }, + { + "cell_type": "markdown", + "id": "d03ebb2a-bc7b-4f52-98d3-ec85c51c7a5f", + "metadata": { + "papermill": { + "duration": 0.005869, + "end_time": "2024-07-25T14:33:26.160673", + "exception": false, + "start_time": "2024-07-25T14:33:26.154804", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Neural Network Design\n", + "\n", + "We need to get to a small latent space. Conv2D networks are good because they help reduce the number of connections in a network in a meaningful way. I'm using terms as defined in [this definition of conv2D](https://towardsdatascience.com/conv2d-to-finally-understand-what-happens-in-the-forward-pass-1bbaafb0b148).\n", + "\n", + "**Definitions:**\n", + "K -> kernel size;\n", + "P -> padding;\n", + "S -> stride;\n", + "D -> Dilation;\n", + "G -> Groups\n", + "\n", + "**Filter options:**\n", + "Longitude is easy because it is large and even, so as long as you have an even stride, you get integer results when dividing.\n", + "e.g. lon 9: stride 4, lat 7: stride 5\n", + "\n", + "- Latitude - whole numbers occurr for P = 2 & K = 3 or K = 11.\n", + "- 11 grid points * 0.25 deg * 100 km/deg = 275 km filter window (a good scale for weather)\n", + "- 9 grid points * 0.25 deg * 100 km/deg = 225 km\n", + "- Longitude - whole numbers occur for P = 0 & K = 11 (nice match with Latitude), P = 1 & K = 3 or 13, P = 2 & K = 5.\n", + "\n", + "For a 5 x 7 filter with 3 stride (no overlap) and no padding:\n", + "- lat: (721 - 4) / 3 = 239 possible steps (good whole number!)\n", + "- lon: (1440 - 4) / 3 = 478.6666 possible steps\n", + "\n", + "## Load and Preprocess Training Data:\n", + "The standard way of manipulating arrays in Conv2D layers in TF is to use arrays in the shape:\n", + "`batch_size, height, width, channels = data.shape`\n", + "In our case, the the `batch_size` is the number of image frames (i.e. separate samples or rows in a `.csv` file), the `height` and `width` define the size of the image frame in number of pixels, and the `channels` are the number of layers in the frames. Typically, channels are color layers (e.g. RGB or CMYK) but in our case, we could use different metereological variables. However, for this first experiment, **we only need one channel** because we're only going to use mean sea level pressure (msl).\n", + "\n", + "## Build the Encoder:\n", + "GFS grids I have available here are at 0.25 degree resolution. I'm doing this as a \"worst case\" scenario since there are also 0.5 and 1.0 degree grids with lower resolution but I can't find that data quickly and don't know what's available.\n", + "\n", + "These 0.25 degree grids are 721 x 1440.\n", + "Each forecast file is 3 hourly for 10 days = 8 steps/forecast * 10 days = 80 \"frames\"\n", + "This demo is only using two forecasts from the control ensemble\n", + "(one launched Jan 01, 2019 and one launched Jan 02, 2019) -> this is only \n", + "a small subset of the variability possible in the model.\n", + "\n", + "This particular data set spans 2000-2019 and there are 5 ensemble members.\n", + "\n", + "## Build the Decoder:\n", + "With the 11 x 11 and 5 x 5 filters, non-overlapping stride, applied here, we have a final \"image\" size of 14 x 27 and 64 channels." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f0144303-f1c9-4220-8178-3988d707688c", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-25T14:33:26.171963Z", + "iopub.status.busy": "2024-07-25T14:33:26.171654Z", + "iopub.status.idle": "2024-07-25T14:33:26.189049Z", + "shell.execute_reply": "2024-07-25T14:33:26.188536Z" + }, + "papermill": { + "duration": 0.024702, + "end_time": "2024-07-25T14:33:26.190177", + "exception": false, + "start_time": "2024-07-25T14:33:26.165475", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# model defintions\n", + "\n", + "class Sampling(layers.Layer):\n", + " \"\"\"Uses (z_mean, z_log_var) to sample z, the vector encoding a digit.\"\"\"\n", + "\n", + " def call(self, inputs):\n", + " z_mean, z_log_var = inputs\n", + " batch = tf.shape(z_mean)[0]\n", + " dim = tf.shape(z_mean)[1]\n", + " epsilon = tf.keras.backend.random_normal(shape=(batch, dim))\n", + " return z_mean + tf.exp(0.5 * z_log_var) * epsilon\n", + "\n", + "def build_encoder(latent_dim):\n", + " encoder_inputs = keras.Input(shape=(721, 1440, 1))\n", + " \n", + " x = layers.Conv2D(32, 11, activation = \"relu\", strides = [9, 10], padding = \"valid\")(encoder_inputs)\n", + " x = layers.Conv2D(64, [5,9], activation = \"relu\", strides = [5, 9], padding = \"valid\")(x)\n", + " x = layers.Flatten()(x)\n", + " x = layers.Dense(16, activation=\"relu\")(x)\n", + " \n", + " z_mean = layers.Dense(latent_dim, name=\"z_mean\")(x)\n", + " z_log_var = layers.Dense(latent_dim, name=\"z_log_var\")(x)\n", + " z = Sampling()([z_mean, z_log_var])\n", + " \n", + " encoder = keras.Model(encoder_inputs, [z_mean, z_log_var, z], name = \"encoder\")\n", + " \n", + " print(encoder.summary())\n", + " return encoder\n", + "\n", + "def build_decoder(latent_dim):\n", + " latent_inputs = keras.Input(shape=(latent_dim,))\n", + " x = layers.Dense(15 * 15 * 64, activation=\"relu\")(latent_inputs)\n", + " x = layers.Reshape((15, 15, 64))(x)\n", + " # FIXME - there is something wrong here, but at least there is a pattern.\n", + " # Using output_padding as a fudge factor -> it may be that there is exactly\n", + " # one \"missing\" filter stamp/convolution because for both Conv2DTranspose\n", + " # operations, output_padding is set to maximum it could be in both dims\n", + " # (i.e. exactly one less than the stride of each filter).\n", + " x = layers.Conv2DTranspose(64, [5, 9], activation = \"relu\", strides = [5,9], padding = \"valid\", output_padding = [4, 8])(x)\n", + " x = layers.Conv2DTranspose(32, 11, activation = \"relu\", strides = [9,10], padding = \"valid\", output_padding = [8, 9])(x)\n", + " decoder_outputs = layers.Conv2DTranspose(1, 3, activation = \"sigmoid\", padding = \"same\")(x)\n", + " decoder = keras.Model(latent_inputs, decoder_outputs, name = \"decoder\")\n", + " \n", + " print(decoder.summary())\n", + " return decoder\n", + "\n", + "class VAE(keras.Model):\n", + " def __init__(self, encoder, decoder, **kwargs):\n", + " super(VAE, self).__init__(**kwargs)\n", + " self.encoder = encoder\n", + " self.decoder = decoder\n", + " self.total_loss_tracker = keras.metrics.Mean(name = \"total_loss\")\n", + " self.reconstruction_loss_tracker = keras.metrics.Mean(name = \"reconstruction_loss\")\n", + " self.kl_loss_tracker = keras.metrics.Mean(name = \"kl_loss\")\n", + "\n", + " @property\n", + " def metrics(self):\n", + " return [\n", + " self.total_loss_tracker,\n", + " self.reconstruction_loss_tracker,\n", + " self.kl_loss_tracker,\n", + " ]\n", + "\n", + " def train_step(self, data):\n", + " \n", + " with tf.GradientTape() as tape:\n", + " z_mean, z_log_var, z = self.encoder(data)\n", + " reconstruction = self.decoder(z)\n", + " # FIXME: Normalize loss with the number of features (28 * 28)\n", + " n_features = 28 * 28\n", + " reconstruction_loss = tf.reduce_mean(\n", + " tf.reduce_sum(\n", + " keras.losses.binary_crossentropy(data, reconstruction), axis = (1, 2)\n", + " )\n", + " ) / n_features\n", + " kl_loss = -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var))\n", + " kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis = 1)) / n_features\n", + " total_loss = (reconstruction_loss + kl_loss)\n", + " grads = tape.gradient(total_loss, self.trainable_weights)\n", + " self.optimizer.apply_gradients(zip(grads, self.trainable_weights))\n", + " self.total_loss_tracker.update_state(total_loss)\n", + " self.reconstruction_loss_tracker.update_state(reconstruction_loss)\n", + " self.kl_loss_tracker.update_state(kl_loss)\n", + " \n", + " return {\n", + " \"loss\": self.total_loss_tracker.result(),\n", + " \"reconstruction_loss\": self.reconstruction_loss_tracker.result(),\n", + " \"kl_loss\": self.kl_loss_tracker.result(),\n", + " }\n", + "\n", + " # Needed to validate (validation loss) and to evaluate\n", + " def test_step(self, data):\n", + " if type(data) == tuple:\n", + " data, _ = data\n", + " \n", + " z_mean, z_log_var, z = self.encoder(data)\n", + " reconstruction = self.decoder(z)\n", + " # FIXME: Normalize loss with the number of features (28 * 28)\n", + " n_features = 28 * 28\n", + " reconstruction_loss = tf.reduce_mean(\n", + " tf.reduce_sum(\n", + " keras.losses.binary_crossentropy(data, reconstruction), axis = (1, 2)\n", + " )\n", + " ) / n_features\n", + " kl_loss = -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var))\n", + " kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis = 1)) / n_features\n", + " total_loss = (reconstruction_loss + kl_loss)\n", + " # grads = tape.gradient(total_loss, self.trainable_weights)\n", + " # self.optimizer.apply_gradients(zip(grads, self.trainable_weights))\n", + " self.total_loss_tracker.update_state(total_loss)\n", + " self.reconstruction_loss_tracker.update_state(reconstruction_loss)\n", + " self.kl_loss_tracker.update_state(kl_loss)\n", + " \n", + " return {\n", + " \"loss\": self.total_loss_tracker.result(),\n", + " \"reconstruction_loss\": self.reconstruction_loss_tracker.result(),\n", + " \"kl_loss\": self.kl_loss_tracker.result(),\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a61f638f-42a9-4d44-8f31-c6c42017a6bf", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-25T14:33:26.197721Z", + "iopub.status.busy": "2024-07-25T14:33:26.197316Z", + "iopub.status.idle": "2024-07-25T14:33:26.219243Z", + "shell.execute_reply": "2024-07-25T14:33:26.218779Z" + }, + "papermill": { + "duration": 0.02803, + "end_time": "2024-07-25T14:33:26.220360", + "exception": false, + "start_time": "2024-07-25T14:33:26.192330", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# training definitions\n", + "\n", + "def train_model(X_train, X_test, X_valid, date, vae, model_dir):\n", + " early_stopping_cb = keras.callbacks.EarlyStopping(patience = 5, restore_best_weights = True) # stops training early if the validation loss does not improve\n", + " \n", + " if os.path.exists(os.path.join(model_dir, 'vae.weights.h5')): # if the model has already been trained at least once, load that model\n", + " vae.load_weights(os.path.join(model_dir, 'vae.weights.h5'))\n", + "\n", + " history = vae.fit(\n", + " X_train, epochs = 50, batch_size = 40,\n", + " callbacks = [early_stopping_cb],\n", + " validation_data = (X_valid,)\n", + " )\n", + "\n", + " vae.save_weights(os.path.join(model_dir, 'vae.weights.h5')) # save model weights after training\n", + " !cp model_dir/vae.weights.h5 model_dir/vae.weights_{date}.h5 # make a copy to dvc save\n", + " \n", + " hist_pd = pd.DataFrame(history.history)\n", + " hist_pd.to_csv(os.path.join(model_dir, f'history_{date}.csv'), index = False)\n", + "\n", + " test_loss = vae.evaluate(X_test)\n", + " test_loss = dict(zip([\"loss\", \"reconstruction_loss\", \"kl_loss\"], test_loss))\n", + "\n", + " print('Test loss:', test_loss)\n", + "\n", + " with open(os.path.join(model_dir, f'test_loss_{date}.json'), 'w') as json_file:\n", + " json.dump(test_loss, json_file, indent = 4)\n", + " \n", + " print(date)\n", + " !sh scripts/run_dvcgit.sh model_dir/history_{date}.csv f\"{date}\"\n", + " !sh scripts/run_dvcgit.sh model_dir/vae.weights_{date}.h5 f\"{date}\"\n", + " !rm model_dir/vae.weights_{date}.h5 # delete copy\n", + " \n", + "def run_train(num_files, date, vae, data_dir, model_dir):\n", + " slp = load_data(data_dir) # load data\n", + " print(\"shape:\", np.shape(slp)) # verify data shape\n", + " \n", + " # split the data - y values are throw away\n", + " X_train, X_test, y_train, y_test = train_test_split(slp[0:(num_files * 40 - 1), :, :, :], np.arange(0, num_files * 40 - 1), test_size = 0.2, random_state = 1)\n", + " X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size = 0.25, random_state = 1) # 0.25 x 0.8 = 0.2\n", + "\n", + " train_model(X_train, X_test, X_valid, date, vae, model_dir)\n", + " remove_data(data_pdir)" + ] + }, + { + "cell_type": "markdown", + "id": "3defdd05-1774-4e56-bd64-2f760c004842", + "metadata": { + "papermill": { + "duration": 0.005824, + "end_time": "2024-07-25T14:33:26.228836", + "exception": false, + "start_time": "2024-07-25T14:33:26.223012", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Train the VAE model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "3dd6a16c-2ff9-42fe-bdcd-b8a970f88b9f", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-25T14:33:26.239825Z", + "iopub.status.busy": "2024-07-25T14:33:26.239501Z", + "iopub.status.idle": "2024-07-25T14:33:26.810929Z", + "shell.execute_reply": "2024-07-25T14:33:26.810406Z" + }, + "papermill": { + "duration": 0.577884, + "end_time": "2024-07-25T14:33:26.812082", + "exception": false, + "start_time": "2024-07-25T14:33:26.234198", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:33:26.248557: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:33:26.250546: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2024-07-25 14:33:26.252578: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2024-07-25 14:33:26.384473: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2024-07-25 14:33:26.386279: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2024-07-25 14:33:26.387873: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2024-07-25 14:33:26.389371: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1928] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 20710 MB memory: -> device: 0, name: NVIDIA A10G, pci bus id: 0000:00:1e.0, compute capability: 8.6\n" + ] + }, + { + "data": { + "text/html": [ + "
Model: \"encoder\"\n",
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+       "┃ Layer (type)        ┃ Output Shape      ┃    Param # ┃ Connected to      ┃\n",
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+       "│ input_layer         │ (None, 721, 1440, │          0 │ -                 │\n",
+       "│ (InputLayer)        │ 1)                │            │                   │\n",
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+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
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+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ reshape (Reshape)               │ (None, 15, 15, 64)     │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_transpose                │ (None, 79, 143, 64)    │       184,384 │\n",
+       "│ (Conv2DTranspose)               │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_transpose_1              │ (None, 721, 1440, 32)  │       247,840 │\n",
+       "│ (Conv2DTranspose)               │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_transpose_2              │ (None, 721, 1440, 1)   │           289 │\n",
+       "│ (Conv2DTranspose)               │                        │               │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
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Devices:\n", + "I0000 00:00:1721918226.962736 82762 service.cc:153] StreamExecutor device (0): NVIDIA A10G, Compute Capability 8.6\n", + "2024-07-25 14:37:07.028323: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:37:07.216872: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:465] Loaded cuDNN version 8907\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:37:11.537023: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 5.07GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n", + "2024-07-25 14:37:11.537091: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 5.07GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n", + "2024-07-25 14:37:11.537113: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 5.07GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n", + "2024-07-25 14:37:11.537134: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 5.08GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n", + "2024-07-25 14:37:11.537154: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 5.07GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n", + "2024-07-25 14:37:11.537166: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 5.07GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n", + "2024-07-25 14:37:11.537185: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 5.07GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:37:14.091869: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng0{} for conv (f32[40,32,721,1440]{3,2,1,0}, u8[0]{0}) custom-call(f32[40,64,79,143]{3,2,1,0}, f32[64,32,11,11]{3,2,1,0}), window={size=11x11 stride=9x10}, dim_labels=bf01_oi01->bf01, custom_call_target=\"__cudnn$convBackwardInput\", backend_config={\"operation_queue_id\":\"0\",\"wait_on_operation_queues\":[],\"cudnn_conv_backend_config\":{\"activation_mode\":\"kNone\",\"conv_result_scale\":1,\"side_input_scale\":0,\"leakyrelu_alpha\":0}} is taking a while...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:37:58.156554: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 45.064752505s\n", + "Trying algorithm eng0{} for conv (f32[40,32,721,1440]{3,2,1,0}, u8[0]{0}) custom-call(f32[40,64,79,143]{3,2,1,0}, f32[64,32,11,11]{3,2,1,0}), window={size=11x11 stride=9x10}, dim_labels=bf01_oi01->bf01, custom_call_target=\"__cudnn$convBackwardInput\", backend_config={\"operation_queue_id\":\"0\",\"wait_on_operation_queues\":[],\"cudnn_conv_backend_config\":{\"activation_mode\":\"kNone\",\"conv_result_scale\":1,\"side_input_scale\":0,\"leakyrelu_alpha\":0}} is taking a while...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:38:20.227982: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng0{} for conv (f32[64,32,11,11]{3,2,1,0}, u8[0]{0}) custom-call(f32[40,32,721,1440]{3,2,1,0}, f32[40,64,79,143]{3,2,1,0}), window={size=11x11 stride=9x10}, dim_labels=bf01_oi01->bf01, custom_call_target=\"__cudnn$convBackwardFilter\", backend_config={\"operation_queue_id\":\"0\",\"wait_on_operation_queues\":[],\"cudnn_conv_backend_config\":{\"activation_mode\":\"kNone\",\"conv_result_scale\":1,\"side_input_scale\":0,\"leakyrelu_alpha\":0}} is taking a while...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:38:24.929055: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 5.701130817s\n", + "Trying algorithm eng0{} for conv (f32[64,32,11,11]{3,2,1,0}, u8[0]{0}) custom-call(f32[40,32,721,1440]{3,2,1,0}, f32[40,64,79,143]{3,2,1,0}), window={size=11x11 stride=9x10}, dim_labels=bf01_oi01->bf01, custom_call_target=\"__cudnn$convBackwardFilter\", backend_config={\"operation_queue_id\":\"0\",\"wait_on_operation_queues\":[],\"cudnn_conv_backend_config\":{\"activation_mode\":\"kNone\",\"conv_result_scale\":1,\"side_input_scale\":0,\"leakyrelu_alpha\":0}} is taking a while...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "I0000 00:00:1721918311.974033 82762 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "\u001b[1m1/8\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m10:05\u001b[0m 87s/step - kl_loss: 4.5086e-04 - loss: 860.0428 - reconstruction_loss: 860.0424" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m2/8\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 238ms/step - kl_loss: 4.5014e-04 - loss: 860.0762 - reconstruction_loss: 860.0758 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/8\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 237ms/step - 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reconstruction_loss: 860.0724" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m6/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 235ms/step - kl_loss: 4.4814e-04 - loss: 860.0573 - reconstruction_loss: 860.0568" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m7/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 235ms/step - kl_loss: 4.4794e-04 - loss: 860.0480 - reconstruction_loss: 860.0475" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:38:37.027997: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng0{} for conv (f32[7,32,721,1440]{3,2,1,0}, u8[0]{0}) custom-call(f32[7,64,79,143]{3,2,1,0}, f32[64,32,11,11]{3,2,1,0}), window={size=11x11 stride=9x10}, dim_labels=bf01_oi01->bf01, custom_call_target=\"__cudnn$convBackwardInput\", backend_config={\"operation_queue_id\":\"0\",\"wait_on_operation_queues\":[],\"cudnn_conv_backend_config\":{\"activation_mode\":\"kNone\",\"conv_result_scale\":1,\"side_input_scale\":0,\"leakyrelu_alpha\":0}} is taking a while...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:38:43.915898: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 7.887961793s\n", + "Trying algorithm eng0{} for conv (f32[7,32,721,1440]{3,2,1,0}, u8[0]{0}) custom-call(f32[7,64,79,143]{3,2,1,0}, f32[64,32,11,11]{3,2,1,0}), window={size=11x11 stride=9x10}, dim_labels=bf01_oi01->bf01, custom_call_target=\"__cudnn$convBackwardInput\", backend_config={\"operation_queue_id\":\"0\",\"wait_on_operation_queues\":[],\"cudnn_conv_backend_config\":{\"activation_mode\":\"kNone\",\"conv_result_scale\":1,\"side_input_scale\":0,\"leakyrelu_alpha\":0}} is taking a while...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3s/step - kl_loss: 4.4780e-04 - loss: 860.0314 - reconstruction_loss: 860.0309 " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "I0000 00:00:1721918331.535562 82761 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_2', 136 bytes spill stores, 136 bytes spill loads\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:38:57.834935: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng0{} for conv (f32[16,32,721,1440]{3,2,1,0}, u8[0]{0}) custom-call(f32[16,64,79,143]{3,2,1,0}, f32[64,32,11,11]{3,2,1,0}), window={size=11x11 stride=9x10}, dim_labels=bf01_oi01->bf01, custom_call_target=\"__cudnn$convBackwardInput\", backend_config={\"operation_queue_id\":\"0\",\"wait_on_operation_queues\":[],\"cudnn_conv_backend_config\":{\"activation_mode\":\"kNone\",\"conv_result_scale\":1,\"side_input_scale\":0,\"leakyrelu_alpha\":0}} is taking a while...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:39:14.861863: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 18.026990723s\n", + "Trying algorithm eng0{} for conv (f32[16,32,721,1440]{3,2,1,0}, u8[0]{0}) custom-call(f32[16,64,79,143]{3,2,1,0}, f32[64,32,11,11]{3,2,1,0}), window={size=11x11 stride=9x10}, dim_labels=bf01_oi01->bf01, custom_call_target=\"__cudnn$convBackwardInput\", backend_config={\"operation_queue_id\":\"0\",\"wait_on_operation_queues\":[],\"cudnn_conv_backend_config\":{\"activation_mode\":\"kNone\",\"conv_result_scale\":1,\"side_input_scale\":0,\"leakyrelu_alpha\":0}} is taking a while...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m133s\u001b[0m 7s/step - kl_loss: 4.4769e-04 - loss: 860.0184 - reconstruction_loss: 860.0179 - val_kl_loss: 4.4777e-04 - val_loss: 859.8553 - val_reconstruction_loss: 859.8549\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 2/50\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "\u001b[1m1/8\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 342ms/step - kl_loss: 4.4777e-04 - loss: 859.7046 - reconstruction_loss: 859.7042" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m2/8\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 236ms/step - kl_loss: 4.4817e-04 - loss: 859.6471 - reconstruction_loss: 859.6466" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/8\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 236ms/step - 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kl_loss: 5.0598e-04 - loss: 859.8282 - reconstruction_loss: 859.8278 - val_kl_loss: 5.0742e-04 - val_loss: 859.8666 - val_reconstruction_loss: 859.8661\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 19/50\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "\u001b[1m1/8\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 332ms/step - kl_loss: 5.0742e-04 - loss: 859.6811 - reconstruction_loss: 859.6806" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m2/8\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 236ms/step - kl_loss: 5.0731e-04 - loss: 859.7928 - reconstruction_loss: 859.7924" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/8\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 236ms/step - 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reconstruction_loss: 859.8207" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m6/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 235ms/step - kl_loss: 5.0728e-04 - loss: 859.8064 - reconstruction_loss: 859.8059" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m7/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 235ms/step - kl_loss: 5.0728e-04 - loss: 859.8033 - reconstruction_loss: 859.8029" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 258ms/step - kl_loss: 5.0728e-04 - loss: 859.8211 - reconstruction_loss: 859.8206 - val_kl_loss: 5.0995e-04 - val_loss: 859.8673 - val_reconstruction_loss: 859.8668\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:40:05.914039: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng0{} for conv (f32[32,32,721,1440]{3,2,1,0}, u8[0]{0}) custom-call(f32[32,64,79,143]{3,2,1,0}, f32[64,32,11,11]{3,2,1,0}), window={size=11x11 stride=9x10}, dim_labels=bf01_oi01->bf01, custom_call_target=\"__cudnn$convBackwardInput\", backend_config={\"operation_queue_id\":\"0\",\"wait_on_operation_queues\":[],\"cudnn_conv_backend_config\":{\"activation_mode\":\"kNone\",\"conv_result_scale\":1,\"side_input_scale\":0,\"leakyrelu_alpha\":0}} is taking a while...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:40:40.965805: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 36.051839104s\n", + "Trying algorithm eng0{} for conv (f32[32,32,721,1440]{3,2,1,0}, u8[0]{0}) custom-call(f32[32,64,79,143]{3,2,1,0}, f32[64,32,11,11]{3,2,1,0}), window={size=11x11 stride=9x10}, dim_labels=bf01_oi01->bf01, custom_call_target=\"__cudnn$convBackwardInput\", backend_config={\"operation_queue_id\":\"0\",\"wait_on_operation_queues\":[],\"cudnn_conv_backend_config\":{\"activation_mode\":\"kNone\",\"conv_result_scale\":1,\"side_input_scale\":0,\"leakyrelu_alpha\":0}} is taking a while...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:35\u001b[0m 48s/step - kl_loss: 5.0865e-04 - loss: 860.0677 - reconstruction_loss: 860.0673" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m2/3\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - kl_loss: 5.0865e-04 - loss: 860.0721 - reconstruction_loss: 860.0717 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - kl_loss: 5.0865e-04 - loss: 860.1227 - reconstruction_loss: 860.1223" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m48s\u001b[0m 90ms/step - kl_loss: 5.0865e-04 - loss: 860.1481 - reconstruction_loss: 860.1476\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test loss: {'loss': 860.2235107421875, 'reconstruction_loss': 0.0005086498567834496, 'kl_loss': 860.2239990234375}\n", + "200320\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "!\r", + "If DVC froze, see `hardlink_lock` in <\u001b[36mhttps://man.dvc.org/config#core\u001b[39m>\r", + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[?25l\u001b[32m⠋\u001b[0m Checking graph\r\n", + "\u001b[?25h\r", + "\u001b[1A\u001b[2K\r", + "!\r", + " 0% Adding...| |0/1 [00:00\r", + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "!\r", + "Collecting |0.00 [00:00, ?entry/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "Collecting |11.0 [00:00, 102entry/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "Collecting |22.0 [00:00, 95.3entry/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "Collecting |23.0 [00:00, 91.1entry/s]\r\n", + "\r", + "!\r", + "Pushing\r\n", + "\r", + "!\u001b[A\r\n", + "\r", + " 0% Checking cache in '/aws-dvc-bucket/files/md5'| |0/? [00:00\r\n", + "Your name and email address were configured automatically based\r\n", + "on your username and hostname. Please check that they are accurate.\r\n", + "You can suppress this message by setting them explicitly. Run the\r\n", + "following command and follow the instructions in your editor to edit\r\n", + "your configuration file:\r\n", + "\r\n", + " git config --global --edit\r\n", + "\r\n", + "After doing this, you may fix the identity used for this commit with:\r\n", + "\r\n", + " git commit --amend --reset-author\r\n", + "\r\n", + " 1 file changed, 5 insertions(+)\r\n", + " create mode 100644 run_on_cluster/cvae-weather-ensemble/model_dir/history_200320.csv.dvc\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: Permanently added 'github.com,140.82.114.3' (ECDSA) to the list of known hosts.\r", + "\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enumerating objects: 10, done.\r\n", + "Counting objects: 10% (1/10)\r", + "Counting objects: 20% (2/10)\r", + "Counting objects: 30% (3/10)\r", + "Counting objects: 40% (4/10)\r", + "Counting objects: 50% (5/10)\r", + "Counting objects: 60% (6/10)\r", + "Counting objects: 70% (7/10)\r", + "Counting objects: 80% (8/10)\r", + "Counting objects: 90% (9/10)\r", + "Counting objects: 100% (10/10)\r", + "Counting objects: 100% (10/10), done.\r\n", + "Delta compression using up to 4 threads\r\n", + "Compressing objects: 16% (1/6)\r", + "Compressing objects: 33% (2/6)\r", + "Compressing objects: 50% (3/6)\r", + "Compressing objects: 66% (4/6)\r", + "Compressing objects: 83% (5/6)\r", + "Compressing objects: 100% (6/6)\r", + "Compressing objects: 100% (6/6), done.\r\n", + "Writing objects: 16% (1/6)\r", + "Writing objects: 33% (2/6)\r", + "Writing objects: 50% (3/6)\r", + "Writing objects: 66% (4/6)\r", + "Writing objects: 83% (5/6)\r", + "Writing objects: 100% (6/6)\r", + "Writing objects: 100% (6/6), 605 bytes | 605.00 KiB/s, done.\r\n", + "Total 6 (delta 4), reused 0 (delta 0), pack-reused 0\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "remote: Resolving deltas: 0% (0/4)\u001b[K\r", + "remote: Resolving deltas: 25% (1/4)\u001b[K\r", + "remote: Resolving deltas: 50% (2/4)\u001b[K\r", + "remote: Resolving deltas: 75% (3/4)\u001b[K\r", + "remote: Resolving deltas: 100% (4/4)\u001b[K\r", + "remote: Resolving deltas: 100% (4/4), completed with 4 local objects.\u001b[K\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "To github.com:oobielodan/parsl_mpi.git\r\n", + " 18e8707..82f1e98 main -> main\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "!\r", + "If DVC froze, see `hardlink_lock` in <\u001b[36mhttps://man.dvc.org/config#core\u001b[39m>\r", + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[?25l\u001b[32m⠋\u001b[0m Checking graph\r\n", + "\u001b[?25h\r", + "\u001b[1A\u001b[2K\r", + "!\r", + " 0% Adding...| |0/1 [00:00\r", + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "!\r", + "Collecting |0.00 [00:00, ?entry/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "Collecting |9.00 [00:00, 88.1entry/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "Collecting |18.0 [00:00, 85.5entry/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "Collecting |24.0 [00:00, 82.1entry/s]\r\n", + "\r", + "!\r", + "Pushing" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r\n", + "\r", + "!\u001b[A\r\n", + "\r", + " 0% Checking cache in '/aws-dvc-bucket/files/md5'| |0/? [00:00\r\n", + "Your name and email address were configured automatically based\r\n", + "on your username and hostname. Please check that they are accurate.\r\n", + "You can suppress this message by setting them explicitly. Run the\r\n", + "following command and follow the instructions in your editor to edit\r\n", + "your configuration file:\r\n", + "\r\n", + " git config --global --edit\r\n", + "\r\n", + "After doing this, you may fix the identity used for this commit with:\r\n", + "\r\n", + " git commit --amend --reset-author\r\n", + "\r\n", + " 1 file changed, 5 insertions(+)\r\n", + " create mode 100644 run_on_cluster/cvae-weather-ensemble/model_dir/vae.weights_200320.h5.dvc\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: Permanently added 'github.com,140.82.114.3' (ECDSA) to the list of known hosts.\r", + "\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enumerating objects: 10, done.\r\n", + "Counting objects: 10% (1/10)\r", + "Counting objects: 20% (2/10)\r", + "Counting objects: 30% (3/10)\r", + "Counting objects: 40% (4/10)\r", + "Counting objects: 50% (5/10)\r", + "Counting objects: 60% (6/10)\r", + "Counting objects: 70% (7/10)\r", + "Counting objects: 80% (8/10)\r", + "Counting objects: 90% (9/10)\r", + "Counting objects: 100% (10/10)\r", + "Counting objects: 100% (10/10), done.\r\n", + "Delta compression using up to 4 threads\r\n", + "Compressing objects: 16% (1/6)\r", + "Compressing objects: 33% (2/6)\r", + "Compressing objects: 50% (3/6)\r", + "Compressing objects: 66% (4/6)\r", + "Compressing objects: 83% (5/6)\r", + "Compressing objects: 100% (6/6)\r", + "Compressing objects: 100% (6/6), done.\r\n", + "Writing objects: 16% (1/6)\r", + "Writing objects: 33% (2/6)\r", + "Writing objects: 50% (3/6)\r", + "Writing objects: 66% (4/6)\r", + "Writing objects: 83% (5/6)\r", + "Writing objects: 100% (6/6)\r", + "Writing objects: 100% (6/6), 597 bytes | 597.00 KiB/s, done.\r\n", + "Total 6 (delta 4), reused 0 (delta 0), pack-reused 0\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "remote: Resolving deltas: 0% (0/4)\u001b[K\r", + "remote: Resolving deltas: 25% (1/4)\u001b[K\r", + "remote: Resolving deltas: 50% (2/4)\u001b[K\r", + "remote: Resolving deltas: 75% (3/4)\u001b[K\r", + "remote: Resolving deltas: 100% (4/4)\u001b[K\r", + "remote: Resolving deltas: 100% (4/4), completed with 4 local objects.\u001b[K\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "To github.com:oobielodan/parsl_mpi.git\r\n", + " 82f1e98..b816144 main -> main\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Memory usage after training: {'current': 6979584, 'peak': 18227546112}\n" + ] + } + ], + "source": [ + "# training\n", + "\n", + "num_files = 0\n", + "\n", + "# get wanted data -------------------------------------------------------------------------\n", + "for month in months:\n", + " for ensemble in ensembles:\n", + " download_file(year, month, day, ensemble, data_pdir)\n", + " convert_file(year, month, day, ensemble, data_dir)\n", + " subset_file(f'pres_msl_{year}{month}{day}00_{ensemble}.nc', data_dir)\n", + "\n", + " if f'pres_msl_{year}{month}{day}00_{ensemble}.nc' in os.listdir(data_dir):\n", + " num_files += 1\n", + "# ------------------------------------------------------------------------------------------ \n", + " \n", + "run_train(num_files, year + day, vae, data_dir, model_dir) # run training \n", + " \n", + "print(\"Memory usage after training:\", tf.config.experimental.get_memory_info('GPU:0'))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:cvae_env]", + "language": "python", + "name": "conda-env-cvae_env-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + }, + "papermill": { + "default_parameters": {}, + "duration": 457.414216, + "end_time": "2024-07-25T14:40:59.958587", + "environment_variables": {}, + "exception": null, + "input_path": "cvae_training.ipynb", + "output_path": "cvae_log.ipynb", + "parameters": { + "day": "20", + "year": "2003" + }, + "start_time": "2024-07-25T14:33:22.544371", + "version": "2.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/run_on_cluster/cvae-weather-ensemble/.ipynb_checkpoints/CVAE_example-checkpoint.ipynb b/run_on_cluster/cvae-demos/weather-demo/.ipynb_checkpoints/cvae_runner_and_example-checkpoint.ipynb similarity index 78% rename from run_on_cluster/cvae-weather-ensemble/.ipynb_checkpoints/CVAE_example-checkpoint.ipynb rename to run_on_cluster/cvae-demos/weather-demo/.ipynb_checkpoints/cvae_runner_and_example-checkpoint.ipynb index d1c1b21..6f609cd 100644 --- a/run_on_cluster/cvae-weather-ensemble/.ipynb_checkpoints/CVAE_example-checkpoint.ipynb +++ b/run_on_cluster/cvae-demos/weather-demo/.ipynb_checkpoints/cvae_runner_and_example-checkpoint.ipynb @@ -6,41 +6,9 @@ "tags": [] }, "source": [ - "# **Demo using CVAE on CORE2 data**\n", - "This is based on an example from Keras.io by fchollet. Please see last cell for original code and links.\n", + "# **CVAE Weather Ensemble Model**\n", "\n", - "# Conda env Setup\n", - "The following is the list steps needed to be taken in order to set up a correct conda environment for running this notebook.\n", - "\n", - "**Create Environment:**\n", - "```bash\n", - "source ~/pw/.miniconda3c/etc/profile.d/conda.sh\n", - "conda create --name NAME python=3.9\n", - "conda activate NAME\n", - "```\n", - "\n", - "**Install Pacakages:** \n", - "\n", - "The following packages need to be installed on top of a typical base Conda env. Install the packages in the following order so the environment solves correctly:\n", - "```bash\n", - "conda install -y -c conda-forge tensorflow\n", - "conda install -y -c conda-forge netCDF4 # For reading nc files\n", - "conda install -y -c conda-forge cartopy # For making maps\n", - "conda install -y -c conda-forge matplotlib\n", - "conda install -y -c conda-forge pandas\n", - "conda install -y -c conda-forge scikit-learn\n", - "conda install -y -c conda-forge papermill\n", - "conda install -y -c conda-forge dvc\n", - "conda install -y -c conda-forge nco\n", - "conda install -y -c conda-forge cdo # For converting grib to nc\n", - "```\n", - "**Connect Notebook to Environment:**\n", - "```bash\n", - "conda install -y ipykernel\n", - "conda install -y requests\n", - "conda install -y -c anaconda jinja2\n", - "python -m ipykernel install --user --name=NAME --display-name \"Python (NAME)\"\n", - "```" + "This notebook provides examples for working with weather data along with a section to launch online learning." ] }, { @@ -99,6 +67,7 @@ }, "outputs": [], "source": [ + "# DVC initialization and storage set up\n", "!dvc init --subdir\n", "!dvc remote add -d dvcstorage /aws-dvc-bucket" ] @@ -106,10 +75,12 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "# make DVC initialization commit\n", + "# initial commit to git\n", "!git add .\n", "!git commit -m \"loaded dependencies, mkdir -p, DVC init\"" ] @@ -119,7 +90,7 @@ "metadata": {}, "source": [ "# Download and Convert Data\n", - "On my [first Google hit for GEFS](https://www.ncei.noaa.gov/products/weather-climate-models/global-ensemble-forecast), I clicked on [AWS Open Data Registry for GEFS](https://registry.opendata.aws/noaa-gefs-pds/) and selected [NOAA GEFS Re-forecast](https://registry.opendata.aws/noaa-gefs-reforecast/) which has no useage restrictions. The [GEFS Re-forecast data documentation](https://noaa-gefs-retrospective.s3.amazonaws.com/Description_of_reforecast_data.pdf) is very clear and we're going to download two files, 57 MB each. The date of the initialization of the re-forecast is in the file name in the format YYYYMMDDHH. The c00, p01, p02, p03, p04 are the control and perturbation ensemble members (5 total)." + "On my [first Google hit for GEFS](https://www.ncei.noaa.gov/products/weather-climate-models/global-ensemble-forecast), I clicked on [AWS Open Data Registry for GEFS](https://registry.opendata.aws/noaa-gefs-pds/) and selected [NOAA GEFS Re-forecast](https://registry.opendata.aws/noaa-gefs-reforecast/) which has no useage restrictions. The [GEFS Re-forecast data documentation](https://noaa-gefs-retrospective.s3.amazonaws.com/Description_of_reforecast_data.pdf) is very clear. The date of the initialization of the re-forecast is in the file name in the format YYYYMMDDHH. The c00, p01, p02, p03, p04 are the control and perturbation ensemble members (5 total)." ] }, { @@ -152,7 +123,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Examples:" + "## Examples" ] }, { @@ -163,6 +134,7 @@ }, "outputs": [], "source": [ + "# example parameters\n", "ex_year = \"2018\"\n", "ex_month = \"01\"\n", "ex_day = \"01\"\n", @@ -179,7 +151,7 @@ "source": [ "# example for getting and converting files \n", "download_file(ex_year, ex_month, ex_day, ex_ensemble, data_pdir)\n", - "convert_file(ex_year, ex_month, ex_day, ex_ensemble, data_pdir)" + "convert_file(ex_year, ex_month, ex_day, ex_ensemble, data_dir)" ] }, { @@ -244,7 +216,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Execute Online Training" + "# Execute Online Training\n", + "\n", + "*add a note about how datat is randomized, sorted, and stuff*" ] }, { @@ -268,11 +242,10 @@ "outputs": [], "source": [ "# parameters for pm -> change the lists to fit your needs\n", - "years = [\"2000\", \"2001\", \"2002\", \"2003\", \"2004\", \"2005\", \"2006\"] # , \"2007\", \"2008\", \"2009\", \n", + "years = [\"2000\", \"2001\", \"2002\", \"2003\"]#, \"2004\", \"2005\", \"2006\"] # , \"2007\", \"2008\", \"2009\", \n", " # \"2010\", \"2011\", \"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\"]\n", - "# months = [\"01\", \"02\", \"03\", \"04\", \"05\", \"06\", \"07\", \"08\", \"09\", \"10\", \"11\", \"12\"] \n", - "days = [\"01\", \"02\", \"03\", \"04\", \"05\", \"06\", \"07\", \"08\", \"09\", \"10\", \"11\", \"12\", \"13\", \"14\", \"15\", \n", - " \"16\", \"17\", \"18\", \"19\", \"20\", \"21\", \"22\", \"23\", \"24\", \"25\", \"26\", \"27\", \"28\", \"29\", \"30\", \"31\"] # forecasts are 10 days long, so (01, 10, 20) provides converage of whole year." + "days = [\"01\", \"10\", \"20\"] # \"02\", \"03\", \"04\", \"05\", \"06\", \"07\", \"08\", \"09\", \"10\", \"11\", \"12\", \"13\", \"14\", \"15\", \n", + " # \"16\", \"17\", \"18\", \"19\", \"20\", \"21\", \"22\", \"23\", \"24\", \"25\", \"26\", \"27\", \"28\", \"29\", \"30\", \"31\"]" ] }, { @@ -286,10 +259,10 @@ "for y in years:\n", " for d in days:\n", " pm.execute_notebook(\n", - " 'CVAE_training.ipynb',\n", - " 'CVAE_log.ipynb',\n", + " 'cvae_training.ipynb',\n", + " 'cvae_log.ipynb',\n", " parameters = dict(year = y, day = d),\n", - " kernel_name = 'cvae_env' \n", + " kernel_name = 'cvae_env' # this should be changed to whatever you chose to be during environment set up\n", " )" ] }, diff --git a/run_on_cluster/cvae-weather-ensemble/CVAE_training.ipynb b/run_on_cluster/cvae-demos/weather-demo/.ipynb_checkpoints/cvae_training-checkpoint.ipynb similarity index 89% rename from run_on_cluster/cvae-weather-ensemble/CVAE_training.ipynb rename to run_on_cluster/cvae-demos/weather-demo/.ipynb_checkpoints/cvae_training-checkpoint.ipynb index a18dbe8..414a099 100644 --- a/run_on_cluster/cvae-weather-ensemble/CVAE_training.ipynb +++ b/run_on_cluster/cvae-demos/weather-demo/.ipynb_checkpoints/cvae_training-checkpoint.ipynb @@ -5,7 +5,7 @@ "id": "f6226ca5-0869-4973-8369-3469cbdff43c", "metadata": {}, "source": [ - "# **CVAE_training**" + "# **CVAE Training**" ] }, { @@ -41,8 +41,18 @@ "id": "06a8b533-cdaa-4efd-938b-ef1d5a84338e", "metadata": {}, "source": [ - "# Download and Convert Data\n", - "On my [first Google hit for GEFS](https://www.ncei.noaa.gov/products/weather-climate-models/global-ensemble-forecast), I clicked on [AWS Open Data Registry for GEFS](https://registry.opendata.aws/noaa-gefs-pds/) and selected [NOAA GEFS Re-forecast](https://registry.opendata.aws/noaa-gefs-reforecast/) which has no useage restrictions. The [GEFS Re-forecast data documentation](https://noaa-gefs-retrospective.s3.amazonaws.com/Description_of_reforecast_data.pdf) is very clear and we're going to download two files, 57 MB each. The date of the initialization of the re-forecast is in the file name in the format YYYYMMDDHH. The c00, p01, p02, p03, p04 are the control and perturbation ensemble members (5 total)." + "# Download and Convert Data" + ] + }, + { + "cell_type": "markdown", + "id": "412796a9-48ba-4a38-b8ae-d1ef35f1fab5", + "metadata": {}, + "source": [ + "### How we cut down the data\n", + "\n", + "1. In the `subset_file` function from the `get_data` library, we used the `ncks -v -d ,,, infile.nc -O subset_infile.nc` command to grab every other time step from the original data file. \n", + "2. In the `load_data` function, we grab the subseted, converted files and concatenate them together to form one dataset before implementing min/max normalization scaling and casting the DataFrame to `float16`" ] }, { @@ -76,19 +86,16 @@ "from scripts.get_data import remove_data # removes all data\n", "\n", "# data loading\n", - "def load_data(data_dir): \n", - " for data_file in os.listdir(data_dir):\n", - " subset_data(data_file, data_dir)\n", - " \n", - " files = [f for f in os.listdir(data_dir) if ('subset' in f and 'tmp' not in f)]\n", + "def load_data(data_dir): \n", + " files = [f for f in os.listdir(data_dir) if ('subset' in f and 'tmp' not in f)] # get th list of subseted and converted files from the data directory\n", " \n", " all_data = ((np.expand_dims(\n", " np.concatenate(\n", - " [netCDF4.Dataset(data_dir + converted_file)['msl'][:] for converted_file in files]\n", + " [netCDF4.Dataset(data_dir + converted_file)['msl'][:] for converted_file in files] # grab the msl data from each of the files\n", " ),\n", " -1\n", - " ).astype(\"float32\") - 85000) / (110000 - 85000)).astype(\"float16\")\n", - " \n", + " ).astype(\"float32\") - 85000) / (110000 - 85000)).astype(\"float16\") # min/max normalization scaling and data casting\n", + " print(all_data)\n", " return all_data" ] }, @@ -161,14 +168,14 @@ " z_mean, z_log_var = inputs\n", " batch = tf.shape(z_mean)[0]\n", " dim = tf.shape(z_mean)[1]\n", - " epsilon = tf.keras.backend.random_normal(shape=(batch, dim))\n", + " epsilon = tf.keras.backend.random_normal(shape = (batch, dim))\n", " return z_mean + tf.exp(0.5 * z_log_var) * epsilon\n", "\n", "def build_encoder(latent_dim):\n", " encoder_inputs = keras.Input(shape=(721, 1440, 1))\n", " \n", - " x = layers.Conv2D(32, 11, activation = \"relu\", strides = [9, 10], padding = \"valid\")(encoder_inputs)\n", - " x = layers.Conv2D(64, [5,9], activation = \"relu\", strides = [5, 9], padding = \"valid\")(x)\n", + " x = layers.Conv2D(32, 11, activation=\"relu\", strides=[9, 10], padding=\"valid\")(encoder_inputs)\n", + " x = layers.Conv2D(64, [5,9], activation=\"relu\", strides=[5, 9], padding=\"valid\")(x)\n", " x = layers.Flatten()(x)\n", " x = layers.Dense(16, activation=\"relu\")(x)\n", " \n", @@ -176,13 +183,14 @@ " z_log_var = layers.Dense(latent_dim, name=\"z_log_var\")(x)\n", " z = Sampling()([z_mean, z_log_var])\n", " \n", - " encoder = keras.Model(encoder_inputs, [z_mean, z_log_var, z], name = \"encoder\")\n", + " encoder = keras.Model(encoder_inputs, [z_mean, z_log_var, z], name=\"encoder\")\n", " \n", " print(encoder.summary())\n", " return encoder\n", "\n", "def build_decoder(latent_dim):\n", " latent_inputs = keras.Input(shape=(latent_dim,))\n", + " \n", " x = layers.Dense(15 * 15 * 64, activation=\"relu\")(latent_inputs)\n", " x = layers.Reshape((15, 15, 64))(x)\n", " # FIXME - there is something wrong here, but at least there is a pattern.\n", @@ -190,10 +198,11 @@ " # one \"missing\" filter stamp/convolution because for both Conv2DTranspose\n", " # operations, output_padding is set to maximum it could be in both dims\n", " # (i.e. exactly one less than the stride of each filter).\n", - " x = layers.Conv2DTranspose(64, [5, 9], activation = \"relu\", strides = [5,9], padding = \"valid\", output_padding = [4, 8])(x)\n", - " x = layers.Conv2DTranspose(32, 11, activation = \"relu\", strides = [9,10], padding = \"valid\", output_padding = [8, 9])(x)\n", - " decoder_outputs = layers.Conv2DTranspose(1, 3, activation = \"sigmoid\", padding = \"same\")(x)\n", - " decoder = keras.Model(latent_inputs, decoder_outputs, name = \"decoder\")\n", + " x = layers.Conv2DTranspose(64, [5, 9], activation=\"relu\", strides=[5,9], padding=\"valid\", output_padding=[4, 8])(x)\n", + " x = layers.Conv2DTranspose(32, 11, activation=\"relu\", strides=[9,10], padding=\"valid\", output_padding=[8, 9])(x)\n", + " \n", + " decoder_outputs = layers.Conv2DTranspose(1, 3, activation=\"sigmoid\", padding=\"same\")(x)\n", + " decoder = keras.Model(latent_inputs, decoder_outputs, name=\"decoder\")\n", " \n", " print(decoder.summary())\n", " return decoder\n", @@ -309,7 +318,9 @@ " with open(os.path.join(model_dir, f'test_loss_{date}.json'), 'w') as json_file:\n", " json.dump(test_loss, json_file, indent = 4)\n", " \n", - " !sh scripts/run_dvcgit.sh model_dir/history_{date}.csv model_dir/vae.weights_{date}.h5 main f\"{date}\"\n", + " print(date)\n", + " !sh scripts/run_dvcgit.sh model_dir/history_{date}.csv f\"{date}\"\n", + " !sh scripts/run_dvcgit.sh model_dir/vae.weights_{date}.h5 f\"{date}\"\n", " !rm model_dir/vae.weights_{date}.h5 # delete copy\n", " \n", "def run_train(num_files, date, vae, data_dir, model_dir):\n", @@ -394,7 +405,8 @@ "for month in months:\n", " for ensemble in ensembles:\n", " download_file(year, month, day, ensemble, data_pdir)\n", - " convert_file(year, month, day, ensemble, data_pdir)\n", + " convert_file(year, month, day, ensemble, data_dir)\n", + " subset_file(f'pres_msl_{year}{month}{day}00_{ensemble}.nc', data_dir)\n", "\n", " if f'pres_msl_{year}{month}{day}00_{ensemble}.nc' in os.listdir(data_dir):\n", " num_files += 1\n", diff --git a/run_on_cluster/cvae-demos/weather-demo/README.md b/run_on_cluster/cvae-demos/weather-demo/README.md new file mode 100644 index 0000000..3782c3b --- /dev/null +++ b/run_on_cluster/cvae-demos/weather-demo/README.md @@ -0,0 +1,66 @@ +# CVAE Weather Ensemble Model + +This is the CVAE Weather Ensemble Model repository. The model leverages the power of Conditional Variational Autoencoders (CVAE) to provide an ensemble training system for weather predictions while also answering the big data problem. Its capability for online training allows for continuous learning from new data streams without compromising performance. That is, data used to train the model is processed in small batches, the model being retrained with each new batch. + +## Key Features + +* **Conditional Variational Autoencoder (CVAE):** Utilizes CVAE with Conv2d networks to model complex dependencies in weather data, enabling accurate probabilistic forecasts. +* **Online Training:** Implements a dynamic training approach that can adapt to real-time incoming data, ensuring up-to-date forecasts. +* **Data Version Control (DVC):** Manages datasets and model versions effectively with DVC, facilitating reproducibility and collaboration. + +## Getting Started + +To get started with the CVAE Weather Ensemble Model, follow the instructions below to set up your environment. + +### 1. Conda env setup +*The following is the list steps needed to be taken in order to set up a correct conda environment for running this notebook.* + +**Create Environment:** +```bash +source ~/pw/.miniconda3c/etc/profile.d/conda.sh +conda create --name python=3.9 +conda activate +``` + +**Install Pacakages:** + +The following packages need to be installed on top of a typical base Conda env. Install the packages in the following order so the environment solves correctly: +```bash +conda install -y -c conda-forge tensorflow +conda install -y -c conda-forge netCDF4 # For reading nc files +conda install -y -c conda-forge cartopy # For making maps +conda install -y -c conda-forge matplotlib +conda install -y -c conda-forge pandas +conda install -y -c conda-forge scikit-learn +conda install -y -c conda-forge papermill # For running online training +conda install -y -c conda-forge dvc # For dvc +conda install -y -c conda-forge nco +conda install -y -c conda-forge cdo # For converting grib to nc +``` +**Connect Notebook to Environment:** +```bash +conda install -y ipykernel +conda install -y requests +conda install -y -c anaconda jinja2 +python -m ipykernel install --user --name= --display-name "Python ()" +``` + +*`conda env update --file cvae_env.yaml --name ` can also be used to recreate the working conda environment used to build this model. After this executes, run the following:* +* *`source ~/pw/.miniconda3c/etc/profile.d/conda.sh`* +* *`conda activate `* +* *`python -m ipykernel install --user --name= --display-name "Python ()"`* + +### 2. Open the `cvae_runner_and_example.ipynb`, `cvae_training.ipynb`, and `cvae_log.ipynb` notebooks + +When opening the notbooks make sure to set each of their kernel to the one just created. Save the notebook after this is done. You can also close out of the `log` and `training` notebooks if wanted. Note: in order to see an updated log when running the online trainging, the `log` notebook must be closed and opened again throughout the session. + +### 3. Continue to `cvae_runner_and_example.ipynb` and follow the instructions in the notebook + +## Needed Specifications +**GPU & Memory:** The program requires at least 32 G of RAM.\ +**Storage:** Mounted stoarge is required for dvc to run properly.\ +**GitHub:** A git repo that can be SSH into is also needed for dvc to run properly. SSH connection must be established on the terminal. + +## + +*This model is based on an [example](https://keras.io/examples/generative/vae/) from Keras.io by fchollet ([GitHub](https://github.com/keras-team/keras-io/blob/master/examples/generative/vae.py))* diff --git a/run_on_cluster/cvae-weather-ensemble/working_env.yaml b/run_on_cluster/cvae-demos/weather-demo/cvae_env.yaml similarity index 100% rename from run_on_cluster/cvae-weather-ensemble/working_env.yaml rename to run_on_cluster/cvae-demos/weather-demo/cvae_env.yaml diff --git a/run_on_cluster/cvae-demos/weather-demo/cvae_log.ipynb b/run_on_cluster/cvae-demos/weather-demo/cvae_log.ipynb new file mode 100644 index 0000000..066f2cd --- /dev/null +++ b/run_on_cluster/cvae-demos/weather-demo/cvae_log.ipynb @@ -0,0 +1,3027 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f6226ca5-0869-4973-8369-3469cbdff43c", + "metadata": { + "papermill": { + "duration": 0.002878, + "end_time": "2024-07-25T14:33:23.439471", + "exception": false, + "start_time": "2024-07-25T14:33:23.436593", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# **CVAE Training**" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "094dca89-dffa-4a56-8263-371fb372a2cb", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-25T14:33:23.450909Z", + "iopub.status.busy": "2024-07-25T14:33:23.450607Z", + "iopub.status.idle": "2024-07-25T14:33:26.110522Z", + "shell.execute_reply": "2024-07-25T14:33:26.109977Z" + }, + "papermill": { + "duration": 2.66821, + "end_time": "2024-07-25T14:33:26.111708", + "exception": false, + "start_time": "2024-07-25T14:33:23.443498", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:33:24.314602: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:10575] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2024-07-25 14:33:24.314670: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:479] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2024-07-25 14:33:24.316256: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1442] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2024-07-25 14:33:24.322678: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TF version: 2.16.2\n", + "GPU is available\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:33:26.057549: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2024-07-25 14:33:26.103956: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2024-07-25 14:33:26.105981: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n" + ] + } + ], + "source": [ + "import os, json\n", + "\n", + "import papermill as pm\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import tensorflow as tf\n", + "import netCDF4\n", + "import cartopy\n", + "\n", + "from tensorflow import keras\n", + "from keras import layers\n", + "from sklearn.model_selection import train_test_split \n", + "\n", + "print(\"TF version:\", tf.__version__)\n", + "print(\"GPU is\", \"available\" if tf.config.list_physical_devices('GPU') else \"NOT AVAILABLE\")" + ] + }, + { + "cell_type": "markdown", + "id": "06a8b533-cdaa-4efd-938b-ef1d5a84338e", + "metadata": { + "papermill": { + "duration": 0.004051, + "end_time": "2024-07-25T14:33:26.118482", + "exception": false, + "start_time": "2024-07-25T14:33:26.114431", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Download and Convert Data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4b08e8b0-f9fa-40b9-852b-8650e681cff2", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-25T14:33:26.129663Z", + "iopub.status.busy": "2024-07-25T14:33:26.129164Z", + "iopub.status.idle": "2024-07-25T14:33:26.132328Z", + "shell.execute_reply": "2024-07-25T14:33:26.131850Z" + }, + "papermill": { + "duration": 0.009123, + "end_time": "2024-07-25T14:33:26.133395", + "exception": false, + "start_time": "2024-07-25T14:33:26.124272", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "data_pdir = \"./gefs_data\"\n", + "data_dir = \"./gefs_data/converted/\"\n", + "model_dir = './model_dir'" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2ff4403c-9c45-4129-902b-93ebfe9749e3", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-25T14:33:26.141466Z", + "iopub.status.busy": "2024-07-25T14:33:26.141114Z", + "iopub.status.idle": "2024-07-25T14:33:26.151581Z", + "shell.execute_reply": "2024-07-25T14:33:26.151111Z" + }, + "papermill": { + "duration": 0.017279, + "end_time": "2024-07-25T14:33:26.152714", + "exception": false, + "start_time": "2024-07-25T14:33:26.135435", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# data definitions\n", + "\n", + "from scripts.get_data import download_file\n", + "from scripts.get_data import convert_file\n", + "from scripts.get_data import subset_file\n", + "from scripts.get_data import remove_data # removes all data\n", + "\n", + "# data loading\n", + "def load_data(data_dir): \n", + " files = [f for f in os.listdir(data_dir) if ('subset' in f and 'tmp' not in f)]\n", + " \n", + " all_data = ((np.expand_dims(\n", + " np.concatenate(\n", + " [netCDF4.Dataset(data_dir + converted_file)['msl'][:] for converted_file in files]\n", + " ),\n", + " -1\n", + " ).astype(\"float32\") - 85000) / (110000 - 85000)).astype(\"float16\")\n", + " \n", + " return all_data" + ] + }, + { + "cell_type": "markdown", + "id": "d03ebb2a-bc7b-4f52-98d3-ec85c51c7a5f", + "metadata": { + "papermill": { + "duration": 0.005869, + "end_time": "2024-07-25T14:33:26.160673", + "exception": false, + "start_time": "2024-07-25T14:33:26.154804", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Neural Network Design\n", + "\n", + "We need to get to a small latent space. Conv2D networks are good because they help reduce the number of connections in a network in a meaningful way. I'm using terms as defined in [this definition of conv2D](https://towardsdatascience.com/conv2d-to-finally-understand-what-happens-in-the-forward-pass-1bbaafb0b148).\n", + "\n", + "**Definitions:**\n", + "K -> kernel size;\n", + "P -> padding;\n", + "S -> stride;\n", + "D -> Dilation;\n", + "G -> Groups\n", + "\n", + "**Filter options:**\n", + "Longitude is easy because it is large and even, so as long as you have an even stride, you get integer results when dividing.\n", + "e.g. lon 9: stride 4, lat 7: stride 5\n", + "\n", + "- Latitude - whole numbers occurr for P = 2 & K = 3 or K = 11.\n", + "- 11 grid points * 0.25 deg * 100 km/deg = 275 km filter window (a good scale for weather)\n", + "- 9 grid points * 0.25 deg * 100 km/deg = 225 km\n", + "- Longitude - whole numbers occur for P = 0 & K = 11 (nice match with Latitude), P = 1 & K = 3 or 13, P = 2 & K = 5.\n", + "\n", + "For a 5 x 7 filter with 3 stride (no overlap) and no padding:\n", + "- lat: (721 - 4) / 3 = 239 possible steps (good whole number!)\n", + "- lon: (1440 - 4) / 3 = 478.6666 possible steps\n", + "\n", + "## Load and Preprocess Training Data:\n", + "The standard way of manipulating arrays in Conv2D layers in TF is to use arrays in the shape:\n", + "`batch_size, height, width, channels = data.shape`\n", + "In our case, the the `batch_size` is the number of image frames (i.e. separate samples or rows in a `.csv` file), the `height` and `width` define the size of the image frame in number of pixels, and the `channels` are the number of layers in the frames. Typically, channels are color layers (e.g. RGB or CMYK) but in our case, we could use different metereological variables. However, for this first experiment, **we only need one channel** because we're only going to use mean sea level pressure (msl).\n", + "\n", + "## Build the Encoder:\n", + "GFS grids I have available here are at 0.25 degree resolution. I'm doing this as a \"worst case\" scenario since there are also 0.5 and 1.0 degree grids with lower resolution but I can't find that data quickly and don't know what's available.\n", + "\n", + "These 0.25 degree grids are 721 x 1440.\n", + "Each forecast file is 3 hourly for 10 days = 8 steps/forecast * 10 days = 80 \"frames\"\n", + "This demo is only using two forecasts from the control ensemble\n", + "(one launched Jan 01, 2019 and one launched Jan 02, 2019) -> this is only \n", + "a small subset of the variability possible in the model.\n", + "\n", + "This particular data set spans 2000-2019 and there are 5 ensemble members.\n", + "\n", + "## Build the Decoder:\n", + "With the 11 x 11 and 5 x 5 filters, non-overlapping stride, applied here, we have a final \"image\" size of 14 x 27 and 64 channels." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f0144303-f1c9-4220-8178-3988d707688c", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-25T14:33:26.171963Z", + "iopub.status.busy": "2024-07-25T14:33:26.171654Z", + "iopub.status.idle": "2024-07-25T14:33:26.189049Z", + "shell.execute_reply": "2024-07-25T14:33:26.188536Z" + }, + "papermill": { + "duration": 0.024702, + "end_time": "2024-07-25T14:33:26.190177", + "exception": false, + "start_time": "2024-07-25T14:33:26.165475", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# model defintions\n", + "\n", + "class Sampling(layers.Layer):\n", + " \"\"\"Uses (z_mean, z_log_var) to sample z, the vector encoding a digit.\"\"\"\n", + "\n", + " def call(self, inputs):\n", + " z_mean, z_log_var = inputs\n", + " batch = tf.shape(z_mean)[0]\n", + " dim = tf.shape(z_mean)[1]\n", + " epsilon = tf.keras.backend.random_normal(shape=(batch, dim))\n", + " return z_mean + tf.exp(0.5 * z_log_var) * epsilon\n", + "\n", + "def build_encoder(latent_dim):\n", + " encoder_inputs = keras.Input(shape=(721, 1440, 1))\n", + " \n", + " x = layers.Conv2D(32, 11, activation = \"relu\", strides = [9, 10], padding = \"valid\")(encoder_inputs)\n", + " x = layers.Conv2D(64, [5,9], activation = \"relu\", strides = [5, 9], padding = \"valid\")(x)\n", + " x = layers.Flatten()(x)\n", + " x = layers.Dense(16, activation=\"relu\")(x)\n", + " \n", + " z_mean = layers.Dense(latent_dim, name=\"z_mean\")(x)\n", + " z_log_var = layers.Dense(latent_dim, name=\"z_log_var\")(x)\n", + " z = Sampling()([z_mean, z_log_var])\n", + " \n", + " encoder = keras.Model(encoder_inputs, [z_mean, z_log_var, z], name = \"encoder\")\n", + " \n", + " print(encoder.summary())\n", + " return encoder\n", + "\n", + "def build_decoder(latent_dim):\n", + " latent_inputs = keras.Input(shape=(latent_dim,))\n", + " x = layers.Dense(15 * 15 * 64, activation=\"relu\")(latent_inputs)\n", + " x = layers.Reshape((15, 15, 64))(x)\n", + " # FIXME - there is something wrong here, but at least there is a pattern.\n", + " # Using output_padding as a fudge factor -> it may be that there is exactly\n", + " # one \"missing\" filter stamp/convolution because for both Conv2DTranspose\n", + " # operations, output_padding is set to maximum it could be in both dims\n", + " # (i.e. exactly one less than the stride of each filter).\n", + " x = layers.Conv2DTranspose(64, [5, 9], activation = \"relu\", strides = [5,9], padding = \"valid\", output_padding = [4, 8])(x)\n", + " x = layers.Conv2DTranspose(32, 11, activation = \"relu\", strides = [9,10], padding = \"valid\", output_padding = [8, 9])(x)\n", + " decoder_outputs = layers.Conv2DTranspose(1, 3, activation = \"sigmoid\", padding = \"same\")(x)\n", + " decoder = keras.Model(latent_inputs, decoder_outputs, name = \"decoder\")\n", + " \n", + " print(decoder.summary())\n", + " return decoder\n", + "\n", + "class VAE(keras.Model):\n", + " def __init__(self, encoder, decoder, **kwargs):\n", + " super(VAE, self).__init__(**kwargs)\n", + " self.encoder = encoder\n", + " self.decoder = decoder\n", + " self.total_loss_tracker = keras.metrics.Mean(name = \"total_loss\")\n", + " self.reconstruction_loss_tracker = keras.metrics.Mean(name = \"reconstruction_loss\")\n", + " self.kl_loss_tracker = keras.metrics.Mean(name = \"kl_loss\")\n", + "\n", + " @property\n", + " def metrics(self):\n", + " return [\n", + " self.total_loss_tracker,\n", + " self.reconstruction_loss_tracker,\n", + " self.kl_loss_tracker,\n", + " ]\n", + "\n", + " def train_step(self, data):\n", + " \n", + " with tf.GradientTape() as tape:\n", + " z_mean, z_log_var, z = self.encoder(data)\n", + " reconstruction = self.decoder(z)\n", + " # FIXME: Normalize loss with the number of features (28 * 28)\n", + " n_features = 28 * 28\n", + " reconstruction_loss = tf.reduce_mean(\n", + " tf.reduce_sum(\n", + " keras.losses.binary_crossentropy(data, reconstruction), axis = (1, 2)\n", + " )\n", + " ) / n_features\n", + " kl_loss = -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var))\n", + " kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis = 1)) / n_features\n", + " total_loss = (reconstruction_loss + kl_loss)\n", + " grads = tape.gradient(total_loss, self.trainable_weights)\n", + " self.optimizer.apply_gradients(zip(grads, self.trainable_weights))\n", + " self.total_loss_tracker.update_state(total_loss)\n", + " self.reconstruction_loss_tracker.update_state(reconstruction_loss)\n", + " self.kl_loss_tracker.update_state(kl_loss)\n", + " \n", + " return {\n", + " \"loss\": self.total_loss_tracker.result(),\n", + " \"reconstruction_loss\": self.reconstruction_loss_tracker.result(),\n", + " \"kl_loss\": self.kl_loss_tracker.result(),\n", + " }\n", + "\n", + " # Needed to validate (validation loss) and to evaluate\n", + " def test_step(self, data):\n", + " if type(data) == tuple:\n", + " data, _ = data\n", + " \n", + " z_mean, z_log_var, z = self.encoder(data)\n", + " reconstruction = self.decoder(z)\n", + " # FIXME: Normalize loss with the number of features (28 * 28)\n", + " n_features = 28 * 28\n", + " reconstruction_loss = tf.reduce_mean(\n", + " tf.reduce_sum(\n", + " keras.losses.binary_crossentropy(data, reconstruction), axis = (1, 2)\n", + " )\n", + " ) / n_features\n", + " kl_loss = -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var))\n", + " kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis = 1)) / n_features\n", + " total_loss = (reconstruction_loss + kl_loss)\n", + " # grads = tape.gradient(total_loss, self.trainable_weights)\n", + " # self.optimizer.apply_gradients(zip(grads, self.trainable_weights))\n", + " self.total_loss_tracker.update_state(total_loss)\n", + " self.reconstruction_loss_tracker.update_state(reconstruction_loss)\n", + " self.kl_loss_tracker.update_state(kl_loss)\n", + " \n", + " return {\n", + " \"loss\": self.total_loss_tracker.result(),\n", + " \"reconstruction_loss\": self.reconstruction_loss_tracker.result(),\n", + " \"kl_loss\": self.kl_loss_tracker.result(),\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a61f638f-42a9-4d44-8f31-c6c42017a6bf", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-25T14:33:26.197721Z", + "iopub.status.busy": "2024-07-25T14:33:26.197316Z", + "iopub.status.idle": "2024-07-25T14:33:26.219243Z", + "shell.execute_reply": "2024-07-25T14:33:26.218779Z" + }, + "papermill": { + "duration": 0.02803, + "end_time": "2024-07-25T14:33:26.220360", + "exception": false, + "start_time": "2024-07-25T14:33:26.192330", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# training definitions\n", + "\n", + "def train_model(X_train, X_test, X_valid, date, vae, model_dir):\n", + " early_stopping_cb = keras.callbacks.EarlyStopping(patience = 5, restore_best_weights = True) # stops training early if the validation loss does not improve\n", + " \n", + " if os.path.exists(os.path.join(model_dir, 'vae.weights.h5')): # if the model has already been trained at least once, load that model\n", + " vae.load_weights(os.path.join(model_dir, 'vae.weights.h5'))\n", + "\n", + " history = vae.fit(\n", + " X_train, epochs = 50, batch_size = 40,\n", + " callbacks = [early_stopping_cb],\n", + " validation_data = (X_valid,)\n", + " )\n", + "\n", + " vae.save_weights(os.path.join(model_dir, 'vae.weights.h5')) # save model weights after training\n", + " !cp model_dir/vae.weights.h5 model_dir/vae.weights_{date}.h5 # make a copy to dvc save\n", + " \n", + " hist_pd = pd.DataFrame(history.history)\n", + " hist_pd.to_csv(os.path.join(model_dir, f'history_{date}.csv'), index = False)\n", + "\n", + " test_loss = vae.evaluate(X_test)\n", + " test_loss = dict(zip([\"loss\", \"reconstruction_loss\", \"kl_loss\"], test_loss))\n", + "\n", + " print('Test loss:', test_loss)\n", + "\n", + " with open(os.path.join(model_dir, f'test_loss_{date}.json'), 'w') as json_file:\n", + " json.dump(test_loss, json_file, indent = 4)\n", + " \n", + " print(date)\n", + " !sh scripts/run_dvcgit.sh model_dir/history_{date}.csv f\"{date}\"\n", + " !sh scripts/run_dvcgit.sh model_dir/vae.weights_{date}.h5 f\"{date}\"\n", + " !rm model_dir/vae.weights_{date}.h5 # delete copy\n", + " \n", + "def run_train(num_files, date, vae, data_dir, model_dir):\n", + " slp = load_data(data_dir) # load data\n", + " print(\"shape:\", np.shape(slp)) # verify data shape\n", + " \n", + " # split the data - y values are throw away\n", + " X_train, X_test, y_train, y_test = train_test_split(slp[0:(num_files * 40 - 1), :, :, :], np.arange(0, num_files * 40 - 1), test_size = 0.2, random_state = 1)\n", + " X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size = 0.25, random_state = 1) # 0.25 x 0.8 = 0.2\n", + "\n", + " train_model(X_train, X_test, X_valid, date, vae, model_dir)\n", + " remove_data(data_pdir)" + ] + }, + { + "cell_type": "markdown", + "id": "3defdd05-1774-4e56-bd64-2f760c004842", + "metadata": { + "papermill": { + "duration": 0.005824, + "end_time": "2024-07-25T14:33:26.228836", + "exception": false, + "start_time": "2024-07-25T14:33:26.223012", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Train the VAE model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "3dd6a16c-2ff9-42fe-bdcd-b8a970f88b9f", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-25T14:33:26.239825Z", + "iopub.status.busy": "2024-07-25T14:33:26.239501Z", + "iopub.status.idle": "2024-07-25T14:33:26.810929Z", + "shell.execute_reply": "2024-07-25T14:33:26.810406Z" + }, + "papermill": { + "duration": 0.577884, + "end_time": "2024-07-25T14:33:26.812082", + "exception": false, + "start_time": "2024-07-25T14:33:26.234198", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:33:26.248557: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:33:26.250546: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2024-07-25 14:33:26.252578: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2024-07-25 14:33:26.384473: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2024-07-25 14:33:26.386279: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2024-07-25 14:33:26.387873: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2024-07-25 14:33:26.389371: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1928] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 20710 MB memory: -> device: 0, name: NVIDIA A10G, pci bus id: 0000:00:1e.0, compute capability: 8.6\n" + ] + }, + { + "data": { + "text/html": [ + "
Model: \"encoder\"\n",
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+       "┃ Layer (type)        ┃ Output Shape      ┃    Param # ┃ Connected to      ┃\n",
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+       "│ input_layer         │ (None, 721, 1440, │          0 │ -                 │\n",
+       "│ (InputLayer)        │ 1)                │            │                   │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ conv2d (Conv2D)     │ (None, 79, 143,   │      3,904 │ input_layer[0][0] │\n",
+       "│                     │ 32)               │            │                   │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ conv2d_1 (Conv2D)   │ (None, 15, 15,    │     92,224 │ conv2d[0][0]      │\n",
+       "│                     │ 64)               │            │                   │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ flatten (Flatten)   │ (None, 14400)     │          0 │ conv2d_1[0][0]    │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ dense (Dense)       │ (None, 16)        │    230,416 │ flatten[0][0]     │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
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+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
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+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ input_layer_1 (InputLayer)      │ (None, 2)              │             0 │\n",
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+       "│ dense_1 (Dense)                 │ (None, 14400)          │        43,200 │\n",
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+       "│ reshape (Reshape)               │ (None, 15, 15, 64)     │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_transpose                │ (None, 79, 143, 64)    │       184,384 │\n",
+       "│ (Conv2DTranspose)               │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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+       "│ (Conv2DTranspose)               │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_transpose_2              │ (None, 721, 1440, 1)   │           289 │\n",
+       "│ (Conv2DTranspose)               │                        │               │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
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+       "
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+       "
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Devices:\n", + "I0000 00:00:1721918226.962736 82762 service.cc:153] StreamExecutor device (0): NVIDIA A10G, Compute Capability 8.6\n", + "2024-07-25 14:37:07.028323: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:37:07.216872: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:465] Loaded cuDNN version 8907\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:37:11.537023: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 5.07GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n", + "2024-07-25 14:37:11.537091: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 5.07GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n", + "2024-07-25 14:37:11.537113: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 5.07GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n", + "2024-07-25 14:37:11.537134: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 5.08GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n", + "2024-07-25 14:37:11.537154: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 5.07GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n", + "2024-07-25 14:37:11.537166: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 5.07GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n", + "2024-07-25 14:37:11.537185: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 5.07GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:37:14.091869: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng0{} for conv (f32[40,32,721,1440]{3,2,1,0}, u8[0]{0}) custom-call(f32[40,64,79,143]{3,2,1,0}, f32[64,32,11,11]{3,2,1,0}), window={size=11x11 stride=9x10}, dim_labels=bf01_oi01->bf01, custom_call_target=\"__cudnn$convBackwardInput\", backend_config={\"operation_queue_id\":\"0\",\"wait_on_operation_queues\":[],\"cudnn_conv_backend_config\":{\"activation_mode\":\"kNone\",\"conv_result_scale\":1,\"side_input_scale\":0,\"leakyrelu_alpha\":0}} is taking a while...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:37:58.156554: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 45.064752505s\n", + "Trying algorithm eng0{} for conv (f32[40,32,721,1440]{3,2,1,0}, u8[0]{0}) custom-call(f32[40,64,79,143]{3,2,1,0}, f32[64,32,11,11]{3,2,1,0}), window={size=11x11 stride=9x10}, dim_labels=bf01_oi01->bf01, custom_call_target=\"__cudnn$convBackwardInput\", backend_config={\"operation_queue_id\":\"0\",\"wait_on_operation_queues\":[],\"cudnn_conv_backend_config\":{\"activation_mode\":\"kNone\",\"conv_result_scale\":1,\"side_input_scale\":0,\"leakyrelu_alpha\":0}} is taking a while...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:38:20.227982: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng0{} for conv (f32[64,32,11,11]{3,2,1,0}, u8[0]{0}) custom-call(f32[40,32,721,1440]{3,2,1,0}, f32[40,64,79,143]{3,2,1,0}), window={size=11x11 stride=9x10}, dim_labels=bf01_oi01->bf01, custom_call_target=\"__cudnn$convBackwardFilter\", backend_config={\"operation_queue_id\":\"0\",\"wait_on_operation_queues\":[],\"cudnn_conv_backend_config\":{\"activation_mode\":\"kNone\",\"conv_result_scale\":1,\"side_input_scale\":0,\"leakyrelu_alpha\":0}} is taking a while...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:38:24.929055: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 5.701130817s\n", + "Trying algorithm eng0{} for conv (f32[64,32,11,11]{3,2,1,0}, u8[0]{0}) custom-call(f32[40,32,721,1440]{3,2,1,0}, f32[40,64,79,143]{3,2,1,0}), window={size=11x11 stride=9x10}, dim_labels=bf01_oi01->bf01, custom_call_target=\"__cudnn$convBackwardFilter\", backend_config={\"operation_queue_id\":\"0\",\"wait_on_operation_queues\":[],\"cudnn_conv_backend_config\":{\"activation_mode\":\"kNone\",\"conv_result_scale\":1,\"side_input_scale\":0,\"leakyrelu_alpha\":0}} is taking a while...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "I0000 00:00:1721918311.974033 82762 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "\u001b[1m1/8\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m10:05\u001b[0m 87s/step - kl_loss: 4.5086e-04 - loss: 860.0428 - reconstruction_loss: 860.0424" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m2/8\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 238ms/step - kl_loss: 4.5014e-04 - loss: 860.0762 - reconstruction_loss: 860.0758 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/8\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 237ms/step - kl_loss: 4.4938e-04 - loss: 860.0538 - reconstruction_loss: 860.0533" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m4/8\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 236ms/step - kl_loss: 4.4880e-04 - loss: 860.0824 - reconstruction_loss: 860.0819" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m5/8\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 236ms/step - kl_loss: 4.4841e-04 - loss: 860.0728 - reconstruction_loss: 860.0724" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m6/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 235ms/step - kl_loss: 4.4814e-04 - loss: 860.0573 - reconstruction_loss: 860.0568" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m7/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 235ms/step - kl_loss: 4.4794e-04 - loss: 860.0480 - reconstruction_loss: 860.0475" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:38:37.027997: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng0{} for conv (f32[7,32,721,1440]{3,2,1,0}, u8[0]{0}) custom-call(f32[7,64,79,143]{3,2,1,0}, f32[64,32,11,11]{3,2,1,0}), window={size=11x11 stride=9x10}, dim_labels=bf01_oi01->bf01, custom_call_target=\"__cudnn$convBackwardInput\", backend_config={\"operation_queue_id\":\"0\",\"wait_on_operation_queues\":[],\"cudnn_conv_backend_config\":{\"activation_mode\":\"kNone\",\"conv_result_scale\":1,\"side_input_scale\":0,\"leakyrelu_alpha\":0}} is taking a while...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:38:43.915898: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 7.887961793s\n", + "Trying algorithm eng0{} for conv (f32[7,32,721,1440]{3,2,1,0}, u8[0]{0}) custom-call(f32[7,64,79,143]{3,2,1,0}, f32[64,32,11,11]{3,2,1,0}), window={size=11x11 stride=9x10}, dim_labels=bf01_oi01->bf01, custom_call_target=\"__cudnn$convBackwardInput\", backend_config={\"operation_queue_id\":\"0\",\"wait_on_operation_queues\":[],\"cudnn_conv_backend_config\":{\"activation_mode\":\"kNone\",\"conv_result_scale\":1,\"side_input_scale\":0,\"leakyrelu_alpha\":0}} is taking a while...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3s/step - kl_loss: 4.4780e-04 - loss: 860.0314 - reconstruction_loss: 860.0309 " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "I0000 00:00:1721918331.535562 82761 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_2', 136 bytes spill stores, 136 bytes spill loads\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:38:57.834935: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng0{} for conv (f32[16,32,721,1440]{3,2,1,0}, u8[0]{0}) custom-call(f32[16,64,79,143]{3,2,1,0}, f32[64,32,11,11]{3,2,1,0}), window={size=11x11 stride=9x10}, dim_labels=bf01_oi01->bf01, custom_call_target=\"__cudnn$convBackwardInput\", backend_config={\"operation_queue_id\":\"0\",\"wait_on_operation_queues\":[],\"cudnn_conv_backend_config\":{\"activation_mode\":\"kNone\",\"conv_result_scale\":1,\"side_input_scale\":0,\"leakyrelu_alpha\":0}} is taking a while...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:39:14.861863: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 18.026990723s\n", + "Trying algorithm eng0{} for conv (f32[16,32,721,1440]{3,2,1,0}, u8[0]{0}) custom-call(f32[16,64,79,143]{3,2,1,0}, f32[64,32,11,11]{3,2,1,0}), window={size=11x11 stride=9x10}, dim_labels=bf01_oi01->bf01, custom_call_target=\"__cudnn$convBackwardInput\", backend_config={\"operation_queue_id\":\"0\",\"wait_on_operation_queues\":[],\"cudnn_conv_backend_config\":{\"activation_mode\":\"kNone\",\"conv_result_scale\":1,\"side_input_scale\":0,\"leakyrelu_alpha\":0}} is taking a while...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m133s\u001b[0m 7s/step - kl_loss: 4.4769e-04 - loss: 860.0184 - reconstruction_loss: 860.0179 - val_kl_loss: 4.4777e-04 - val_loss: 859.8553 - val_reconstruction_loss: 859.8549\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 2/50\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "\u001b[1m1/8\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 342ms/step - kl_loss: 4.4777e-04 - loss: 859.7046 - reconstruction_loss: 859.7042" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m2/8\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 236ms/step - kl_loss: 4.4817e-04 - loss: 859.6471 - reconstruction_loss: 859.6466" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/8\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 236ms/step - 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reconstruction_loss: 859.7893" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m6/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 235ms/step - kl_loss: 5.0584e-04 - loss: 859.8082 - reconstruction_loss: 859.8077" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m7/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 235ms/step - kl_loss: 5.0589e-04 - loss: 859.8043 - reconstruction_loss: 859.8038" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 258ms/step - 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kl_loss: 5.0728e-04 - loss: 859.8211 - reconstruction_loss: 859.8206 - val_kl_loss: 5.0995e-04 - val_loss: 859.8673 - val_reconstruction_loss: 859.8668\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:40:05.914039: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng0{} for conv (f32[32,32,721,1440]{3,2,1,0}, u8[0]{0}) custom-call(f32[32,64,79,143]{3,2,1,0}, f32[64,32,11,11]{3,2,1,0}), window={size=11x11 stride=9x10}, dim_labels=bf01_oi01->bf01, custom_call_target=\"__cudnn$convBackwardInput\", backend_config={\"operation_queue_id\":\"0\",\"wait_on_operation_queues\":[],\"cudnn_conv_backend_config\":{\"activation_mode\":\"kNone\",\"conv_result_scale\":1,\"side_input_scale\":0,\"leakyrelu_alpha\":0}} is taking a while...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:40:40.965805: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 36.051839104s\n", + "Trying algorithm eng0{} for conv (f32[32,32,721,1440]{3,2,1,0}, u8[0]{0}) custom-call(f32[32,64,79,143]{3,2,1,0}, f32[64,32,11,11]{3,2,1,0}), window={size=11x11 stride=9x10}, dim_labels=bf01_oi01->bf01, custom_call_target=\"__cudnn$convBackwardInput\", backend_config={\"operation_queue_id\":\"0\",\"wait_on_operation_queues\":[],\"cudnn_conv_backend_config\":{\"activation_mode\":\"kNone\",\"conv_result_scale\":1,\"side_input_scale\":0,\"leakyrelu_alpha\":0}} is taking a while...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:35\u001b[0m 48s/step - kl_loss: 5.0865e-04 - loss: 860.0677 - reconstruction_loss: 860.0673" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m2/3\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - kl_loss: 5.0865e-04 - loss: 860.0721 - reconstruction_loss: 860.0717 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - kl_loss: 5.0865e-04 - loss: 860.1227 - reconstruction_loss: 860.1223" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m48s\u001b[0m 90ms/step - kl_loss: 5.0865e-04 - loss: 860.1481 - reconstruction_loss: 860.1476\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test loss: {'loss': 860.2235107421875, 'reconstruction_loss': 0.0005086498567834496, 'kl_loss': 860.2239990234375}\n", + "200320\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "!\r", + "If DVC froze, see `hardlink_lock` in <\u001b[36mhttps://man.dvc.org/config#core\u001b[39m>\r", + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[?25l\u001b[32m⠋\u001b[0m Checking graph\r\n", + "\u001b[?25h\r", + "\u001b[1A\u001b[2K\r", + "!\r", + " 0% Adding...| |0/1 [00:00\r", + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "!\r", + "Collecting |0.00 [00:00, ?entry/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "Collecting |11.0 [00:00, 102entry/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "Collecting |22.0 [00:00, 95.3entry/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "Collecting |23.0 [00:00, 91.1entry/s]\r\n", + "\r", + "!\r", + "Pushing\r\n", + "\r", + "!\u001b[A\r\n", + "\r", + " 0% Checking cache in '/aws-dvc-bucket/files/md5'| |0/? [00:00\r\n", + "Your name and email address were configured automatically based\r\n", + "on your username and hostname. Please check that they are accurate.\r\n", + "You can suppress this message by setting them explicitly. Run the\r\n", + "following command and follow the instructions in your editor to edit\r\n", + "your configuration file:\r\n", + "\r\n", + " git config --global --edit\r\n", + "\r\n", + "After doing this, you may fix the identity used for this commit with:\r\n", + "\r\n", + " git commit --amend --reset-author\r\n", + "\r\n", + " 1 file changed, 5 insertions(+)\r\n", + " create mode 100644 run_on_cluster/cvae-weather-ensemble/model_dir/history_200320.csv.dvc\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: Permanently added 'github.com,140.82.114.3' (ECDSA) to the list of known hosts.\r", + "\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enumerating objects: 10, done.\r\n", + "Counting objects: 10% (1/10)\r", + "Counting objects: 20% (2/10)\r", + "Counting objects: 30% (3/10)\r", + "Counting objects: 40% (4/10)\r", + "Counting objects: 50% (5/10)\r", + "Counting objects: 60% (6/10)\r", + "Counting objects: 70% (7/10)\r", + "Counting objects: 80% (8/10)\r", + "Counting objects: 90% (9/10)\r", + "Counting objects: 100% (10/10)\r", + "Counting objects: 100% (10/10), done.\r\n", + "Delta compression using up to 4 threads\r\n", + "Compressing objects: 16% (1/6)\r", + "Compressing objects: 33% (2/6)\r", + "Compressing objects: 50% (3/6)\r", + "Compressing objects: 66% (4/6)\r", + "Compressing objects: 83% (5/6)\r", + "Compressing objects: 100% (6/6)\r", + "Compressing objects: 100% (6/6), done.\r\n", + "Writing objects: 16% (1/6)\r", + "Writing objects: 33% (2/6)\r", + "Writing objects: 50% (3/6)\r", + "Writing objects: 66% (4/6)\r", + "Writing objects: 83% (5/6)\r", + "Writing objects: 100% (6/6)\r", + "Writing objects: 100% (6/6), 605 bytes | 605.00 KiB/s, done.\r\n", + "Total 6 (delta 4), reused 0 (delta 0), pack-reused 0\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "remote: Resolving deltas: 0% (0/4)\u001b[K\r", + "remote: Resolving deltas: 25% (1/4)\u001b[K\r", + "remote: Resolving deltas: 50% (2/4)\u001b[K\r", + "remote: Resolving deltas: 75% (3/4)\u001b[K\r", + "remote: Resolving deltas: 100% (4/4)\u001b[K\r", + "remote: Resolving deltas: 100% (4/4), completed with 4 local objects.\u001b[K\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "To github.com:oobielodan/parsl_mpi.git\r\n", + " 18e8707..82f1e98 main -> main\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "!\r", + "If DVC froze, see `hardlink_lock` in <\u001b[36mhttps://man.dvc.org/config#core\u001b[39m>\r", + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[?25l\u001b[32m⠋\u001b[0m Checking graph\r\n", + "\u001b[?25h\r", + "\u001b[1A\u001b[2K\r", + "!\r", + " 0% Adding...| |0/1 [00:00\r", + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "!\r", + "Collecting |0.00 [00:00, ?entry/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "Collecting |9.00 [00:00, 88.1entry/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "Collecting |18.0 [00:00, 85.5entry/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "Collecting |24.0 [00:00, 82.1entry/s]\r\n", + "\r", + "!\r", + "Pushing" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r\n", + "\r", + "!\u001b[A\r\n", + "\r", + " 0% Checking cache in '/aws-dvc-bucket/files/md5'| |0/? [00:00\r\n", + "Your name and email address were configured automatically based\r\n", + "on your username and hostname. Please check that they are accurate.\r\n", + "You can suppress this message by setting them explicitly. Run the\r\n", + "following command and follow the instructions in your editor to edit\r\n", + "your configuration file:\r\n", + "\r\n", + " git config --global --edit\r\n", + "\r\n", + "After doing this, you may fix the identity used for this commit with:\r\n", + "\r\n", + " git commit --amend --reset-author\r\n", + "\r\n", + " 1 file changed, 5 insertions(+)\r\n", + " create mode 100644 run_on_cluster/cvae-weather-ensemble/model_dir/vae.weights_200320.h5.dvc\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: Permanently added 'github.com,140.82.114.3' (ECDSA) to the list of known hosts.\r", + "\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enumerating objects: 10, done.\r\n", + "Counting objects: 10% (1/10)\r", + "Counting objects: 20% (2/10)\r", + "Counting objects: 30% (3/10)\r", + "Counting objects: 40% (4/10)\r", + "Counting objects: 50% (5/10)\r", + "Counting objects: 60% (6/10)\r", + "Counting objects: 70% (7/10)\r", + "Counting objects: 80% (8/10)\r", + "Counting objects: 90% (9/10)\r", + "Counting objects: 100% (10/10)\r", + "Counting objects: 100% (10/10), done.\r\n", + "Delta compression using up to 4 threads\r\n", + "Compressing objects: 16% (1/6)\r", + "Compressing objects: 33% (2/6)\r", + "Compressing objects: 50% (3/6)\r", + "Compressing objects: 66% (4/6)\r", + "Compressing objects: 83% (5/6)\r", + "Compressing objects: 100% (6/6)\r", + "Compressing objects: 100% (6/6), done.\r\n", + "Writing objects: 16% (1/6)\r", + "Writing objects: 33% (2/6)\r", + "Writing objects: 50% (3/6)\r", + "Writing objects: 66% (4/6)\r", + "Writing objects: 83% (5/6)\r", + "Writing objects: 100% (6/6)\r", + "Writing objects: 100% (6/6), 597 bytes | 597.00 KiB/s, done.\r\n", + "Total 6 (delta 4), reused 0 (delta 0), pack-reused 0\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "remote: Resolving deltas: 0% (0/4)\u001b[K\r", + "remote: Resolving deltas: 25% (1/4)\u001b[K\r", + "remote: Resolving deltas: 50% (2/4)\u001b[K\r", + "remote: Resolving deltas: 75% (3/4)\u001b[K\r", + "remote: Resolving deltas: 100% (4/4)\u001b[K\r", + "remote: Resolving deltas: 100% (4/4), completed with 4 local objects.\u001b[K\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "To github.com:oobielodan/parsl_mpi.git\r\n", + " 82f1e98..b816144 main -> main\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Memory usage after training: {'current': 6979584, 'peak': 18227546112}\n" + ] + } + ], + "source": [ + "# training\n", + "\n", + "num_files = 0\n", + "\n", + "# get wanted data -------------------------------------------------------------------------\n", + "for month in months:\n", + " for ensemble in ensembles:\n", + " download_file(year, month, day, ensemble, data_pdir)\n", + " convert_file(year, month, day, ensemble, data_dir)\n", + " subset_file(f'pres_msl_{year}{month}{day}00_{ensemble}.nc', data_dir)\n", + "\n", + " if f'pres_msl_{year}{month}{day}00_{ensemble}.nc' in os.listdir(data_dir):\n", + " num_files += 1\n", + "# ------------------------------------------------------------------------------------------ \n", + " \n", + "run_train(num_files, year + day, vae, data_dir, model_dir) # run training \n", + " \n", + "print(\"Memory usage after training:\", tf.config.experimental.get_memory_info('GPU:0'))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:cvae_env]", + "language": "python", + "name": "conda-env-cvae_env-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + }, + "papermill": { + "default_parameters": {}, + "duration": 457.414216, + "end_time": "2024-07-25T14:40:59.958587", + "environment_variables": {}, + "exception": null, + "input_path": "cvae_training.ipynb", + "output_path": "cvae_log.ipynb", + "parameters": { + "day": "20", + "year": "2003" + }, + "start_time": "2024-07-25T14:33:22.544371", + "version": "2.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/run_on_cluster/cvae-demos/weather-demo/cvae_runner_and_example.ipynb b/run_on_cluster/cvae-demos/weather-demo/cvae_runner_and_example.ipynb new file mode 100644 index 0000000..6f609cd --- /dev/null +++ b/run_on_cluster/cvae-demos/weather-demo/cvae_runner_and_example.ipynb @@ -0,0 +1,413 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# **CVAE Weather Ensemble Model**\n", + "\n", + "This notebook provides examples for working with weather data along with a section to launch online learning." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Libraries and Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import os, json\n", + "\n", + "import papermill as pm\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import tensorflow as tf\n", + "import netCDF4\n", + "import cartopy\n", + "\n", + "from tensorflow import keras\n", + "from keras import layers\n", + "from sklearn.model_selection import train_test_split \n", + "\n", + "print(\"TF version:\", tf.__version__)\n", + "print(\"GPU is\", \"available\" if tf.config.list_physical_devices('GPU') else \"NOT AVAILABLE\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# make needed directories\n", + "# use -p to make parent directories\n", + "!mkdir -p gefs_data/converted\n", + "!mkdir -p model_dir" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# DVC initialization and storage set up\n", + "!dvc init --subdir\n", + "!dvc remote add -d dvcstorage /aws-dvc-bucket" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# initial commit to git\n", + "!git add .\n", + "!git commit -m \"loaded dependencies, mkdir -p, DVC init\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Download and Convert Data\n", + "On my [first Google hit for GEFS](https://www.ncei.noaa.gov/products/weather-climate-models/global-ensemble-forecast), I clicked on [AWS Open Data Registry for GEFS](https://registry.opendata.aws/noaa-gefs-pds/) and selected [NOAA GEFS Re-forecast](https://registry.opendata.aws/noaa-gefs-reforecast/) which has no useage restrictions. The [GEFS Re-forecast data documentation](https://noaa-gefs-retrospective.s3.amazonaws.com/Description_of_reforecast_data.pdf) is very clear. The date of the initialization of the re-forecast is in the file name in the format YYYYMMDDHH. The c00, p01, p02, p03, p04 are the control and perturbation ensemble members (5 total)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from scripts.get_data import download_file\n", + "from scripts.get_data import convert_file\n", + "from scripts.get_data import remove_data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "data_pdir = \"./gefs_data\"\n", + "data_dir = \"./gefs_data/converted/\"\n", + "model_dir = './model_dir'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Examples" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# example parameters\n", + "ex_year = \"2018\"\n", + "ex_month = \"01\"\n", + "ex_day = \"01\"\n", + "ex_ensemble = \"c00\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# example for getting and converting files \n", + "download_file(ex_year, ex_month, ex_day, ex_ensemble, data_pdir)\n", + "convert_file(ex_year, ex_month, ex_day, ex_ensemble, data_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# example for loading data\n", + "dataset = netCDF4.Dataset(data_dir + f\"pres_msl_{ex_year}{ex_month}{ex_day}00_{ex_ensemble}.nc\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# example for simple data access\n", + "print(dataset) # look at data structure\n", + "print(dataset.variables.keys())\n", + "\n", + "for var in dataset.variables:\n", + " print(dataset.variables[var])\n", + " # print(dataset.variables[var][:]) # prints actual data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# example for plot\n", + "fig = plt.figure(figsize=(9,6))\n", + "ax = plt.axes(projection = cartopy.crs.LambertConformal())\n", + "\n", + "ax.add_feature(cartopy.feature.LAND)\n", + "ax.add_feature(cartopy.feature.OCEAN)\n", + "ax.add_feature(cartopy.feature.LAKES, alpha = 0.5)\n", + "ax.add_feature(cartopy.feature.STATES, edgecolor='grey')\n", + "ax.set_extent([-120, -73, 23, 50])\n", + "\n", + "plt.contour(\n", + " dataset.variables['lon'][:], # longitudes\n", + " dataset.variables['lat'][:], # latitudes\n", + " dataset.variables['msl'][0,:,:], # actual data\n", + " transform = cartopy.crs.PlateCarree()) #, levels=np.arange(30000,110000,20000))\n", + "\n", + "plt.title('GEFSv12 SLP 2019 01 10 0000 UTC Cycle')\n", + "plt.colorbar()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Execute Online Training\n", + "\n", + "*add a note about how datat is randomized, sorted, and stuff*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# clean up example data before executing online session\n", + "remove_data(data_pdir)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# parameters for pm -> change the lists to fit your needs\n", + "years = [\"2000\", \"2001\", \"2002\", \"2003\"]#, \"2004\", \"2005\", \"2006\"] # , \"2007\", \"2008\", \"2009\", \n", + " # \"2010\", \"2011\", \"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\"]\n", + "days = [\"01\", \"10\", \"20\"] # \"02\", \"03\", \"04\", \"05\", \"06\", \"07\", \"08\", \"09\", \"10\", \"11\", \"12\", \"13\", \"14\", \"15\", \n", + " # \"16\", \"17\", \"18\", \"19\", \"20\", \"21\", \"22\", \"23\", \"24\", \"25\", \"26\", \"27\", \"28\", \"29\", \"30\", \"31\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "for y in years:\n", + " for d in days:\n", + " pm.execute_notebook(\n", + " 'cvae_training.ipynb',\n", + " 'cvae_log.ipynb',\n", + " parameters = dict(year = y, day = d),\n", + " kernel_name = 'cvae_env' # this should be changed to whatever you chose to be during environment set up\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Try different filter sizes\n", + "# Aim for large initial filter > 200km scale (about 10)\n", + "# Aim for some, but minimal overlap in initial filter.\n", + "\n", + "# Aim for smallish second filter, but still try to reduce dimensionality\n", + "# to make dense network tractable later. No overlap (but no good\n", + "# reason why this is).\n", + "\n", + "# ----------------------Input: 721 x 1440--------------\n", + "\n", + "# For Lat = 721,\n", + "# K = 11 -> K_radius = 5.0 -> S = 9 -> H_out = 79.0\n", + "\n", + "# For Lon = 1440,\n", + "# K = 11 -> K_radius = 5.0 -> S = 10 -> H_out = 143.0\n", + "\n", + "# -----------------------Layer1: 79 x 143----------------\n", + "\n", + "# For Lat = 79,\n", + "# K = 5 -> K_radius = 2.0 -> S = 5 -> H_out = 15.0\n", + "\n", + "# For Lon = 143,\n", + "# K = 9 -> K_radius = 4.0 -> S = 9 -> H_out = 15.0\n", + "\n", + "'''\n", + "H_in = 1440\n", + "P = 0\n", + "K_list = [3, 5, 7, 9, 11, 13, 15] # Kernel size\n", + "S_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13] # Stride\n", + "\n", + "for K in K_list:\n", + " for S in S_list:\n", + " K_radius = np.floor(np.divide(K, 2)) # Half width of number of points around the central point\n", + " K_diameter = K - 1 # Number of points around the central point, ASSUMES K = ODD\n", + " # S = K # S = K is stride necessary to have non-overlapping filters\n", + " print('K = ' + str(K) + ' -> K_radius = ' + str(K_radius) + ' -> S = ' + str(S) + ' -> H_out = ' + str((H_in + (2 * P) - K_diameter) / S))\n", + " print('')\n", + "'''" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Display a grid of sampled digits" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# if latent_dim == 2:\n", + "# plot_latent_space(vae, path = os.path.join(model_dir, 'latent_space.png'))\n", + "\n", + "# # Generating new images\n", + "# codings = tf.random.normal(shape = [12, latent_dim])\n", + "# images = vae.decoder(codings).numpy()\n", + "# plot_images(images, 3, 4, path = os.path.join(model_dir, 'generated.png'))\n", + "\n", + "# # Semantic interpolation\n", + "# codings_grid = tf.reshape(codings, [1, 3, 4, latent_dim])\n", + "# larger_grid = tf.image.resize(codings_grid, size = [5, 7])\n", + "# interpolated_codings = tf.reshape(larger_grid, [-1, latent_dim])\n", + "# images = vae.decoder(interpolated_codings).numpy()\n", + "# plot_images(images, 5, 7, path = os.path.join(model_dir, 'interpolated.png'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# plot_latent_space(vae)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Display how the latent space clusters different digit classes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# def plot_label_clusters(vae, data, labels):\n", + "# # display a 2D plot of the digit classes in the latent space\n", + "# z_mean, _, _ = vae.encoder.predict(data)\n", + "# plt.figure(figsize=(12, 10))\n", + "# plt.scatter(z_mean[:, 0], z_mean[:, 1], c=labels)\n", + "# plt.colorbar()\n", + "# plt.xlabel(\"z[0]\")\n", + "# plt.ylabel(\"z[1]\")\n", + "# plt.show()\n", + "\n", + "\n", + "# (x_train, y_train), _ = keras.datasets.mnist.load_data()\n", + "# x_train = np.expand_dims(x_train, -1).astype(\"float32\") / 255\n", + "\n", + "# plot_label_clusters(vae, x_train, y_train)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:cvae_env]", + "language": "python", + "name": "conda-env-cvae_env-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/run_on_cluster/cvae-weather-ensemble/.ipynb_checkpoints/CVAE_training-checkpoint.ipynb b/run_on_cluster/cvae-demos/weather-demo/cvae_training.ipynb similarity index 89% rename from run_on_cluster/cvae-weather-ensemble/.ipynb_checkpoints/CVAE_training-checkpoint.ipynb rename to run_on_cluster/cvae-demos/weather-demo/cvae_training.ipynb index a18dbe8..414a099 100644 --- a/run_on_cluster/cvae-weather-ensemble/.ipynb_checkpoints/CVAE_training-checkpoint.ipynb +++ b/run_on_cluster/cvae-demos/weather-demo/cvae_training.ipynb @@ -5,7 +5,7 @@ "id": "f6226ca5-0869-4973-8369-3469cbdff43c", "metadata": {}, "source": [ - "# **CVAE_training**" + "# **CVAE Training**" ] }, { @@ -41,8 +41,18 @@ "id": "06a8b533-cdaa-4efd-938b-ef1d5a84338e", "metadata": {}, "source": [ - "# Download and Convert Data\n", - "On my [first Google hit for GEFS](https://www.ncei.noaa.gov/products/weather-climate-models/global-ensemble-forecast), I clicked on [AWS Open Data Registry for GEFS](https://registry.opendata.aws/noaa-gefs-pds/) and selected [NOAA GEFS Re-forecast](https://registry.opendata.aws/noaa-gefs-reforecast/) which has no useage restrictions. The [GEFS Re-forecast data documentation](https://noaa-gefs-retrospective.s3.amazonaws.com/Description_of_reforecast_data.pdf) is very clear and we're going to download two files, 57 MB each. The date of the initialization of the re-forecast is in the file name in the format YYYYMMDDHH. The c00, p01, p02, p03, p04 are the control and perturbation ensemble members (5 total)." + "# Download and Convert Data" + ] + }, + { + "cell_type": "markdown", + "id": "412796a9-48ba-4a38-b8ae-d1ef35f1fab5", + "metadata": {}, + "source": [ + "### How we cut down the data\n", + "\n", + "1. In the `subset_file` function from the `get_data` library, we used the `ncks -v -d ,,, infile.nc -O subset_infile.nc` command to grab every other time step from the original data file. \n", + "2. In the `load_data` function, we grab the subseted, converted files and concatenate them together to form one dataset before implementing min/max normalization scaling and casting the DataFrame to `float16`" ] }, { @@ -76,19 +86,16 @@ "from scripts.get_data import remove_data # removes all data\n", "\n", "# data loading\n", - "def load_data(data_dir): \n", - " for data_file in os.listdir(data_dir):\n", - " subset_data(data_file, data_dir)\n", - " \n", - " files = [f for f in os.listdir(data_dir) if ('subset' in f and 'tmp' not in f)]\n", + "def load_data(data_dir): \n", + " files = [f for f in os.listdir(data_dir) if ('subset' in f and 'tmp' not in f)] # get th list of subseted and converted files from the data directory\n", " \n", " all_data = ((np.expand_dims(\n", " np.concatenate(\n", - " [netCDF4.Dataset(data_dir + converted_file)['msl'][:] for converted_file in files]\n", + " [netCDF4.Dataset(data_dir + converted_file)['msl'][:] for converted_file in files] # grab the msl data from each of the files\n", " ),\n", " -1\n", - " ).astype(\"float32\") - 85000) / (110000 - 85000)).astype(\"float16\")\n", - " \n", + " ).astype(\"float32\") - 85000) / (110000 - 85000)).astype(\"float16\") # min/max normalization scaling and data casting\n", + " print(all_data)\n", " return all_data" ] }, @@ -161,14 +168,14 @@ " z_mean, z_log_var = inputs\n", " batch = tf.shape(z_mean)[0]\n", " dim = tf.shape(z_mean)[1]\n", - " epsilon = tf.keras.backend.random_normal(shape=(batch, dim))\n", + " epsilon = tf.keras.backend.random_normal(shape = (batch, dim))\n", " return z_mean + tf.exp(0.5 * z_log_var) * epsilon\n", "\n", "def build_encoder(latent_dim):\n", " encoder_inputs = keras.Input(shape=(721, 1440, 1))\n", " \n", - " x = layers.Conv2D(32, 11, activation = \"relu\", strides = [9, 10], padding = \"valid\")(encoder_inputs)\n", - " x = layers.Conv2D(64, [5,9], activation = \"relu\", strides = [5, 9], padding = \"valid\")(x)\n", + " x = layers.Conv2D(32, 11, activation=\"relu\", strides=[9, 10], padding=\"valid\")(encoder_inputs)\n", + " x = layers.Conv2D(64, [5,9], activation=\"relu\", strides=[5, 9], padding=\"valid\")(x)\n", " x = layers.Flatten()(x)\n", " x = layers.Dense(16, activation=\"relu\")(x)\n", " \n", @@ -176,13 +183,14 @@ " z_log_var = layers.Dense(latent_dim, name=\"z_log_var\")(x)\n", " z = Sampling()([z_mean, z_log_var])\n", " \n", - " encoder = keras.Model(encoder_inputs, [z_mean, z_log_var, z], name = \"encoder\")\n", + " encoder = keras.Model(encoder_inputs, [z_mean, z_log_var, z], name=\"encoder\")\n", " \n", " print(encoder.summary())\n", " return encoder\n", "\n", "def build_decoder(latent_dim):\n", " latent_inputs = keras.Input(shape=(latent_dim,))\n", + " \n", " x = layers.Dense(15 * 15 * 64, activation=\"relu\")(latent_inputs)\n", " x = layers.Reshape((15, 15, 64))(x)\n", " # FIXME - there is something wrong here, but at least there is a pattern.\n", @@ -190,10 +198,11 @@ " # one \"missing\" filter stamp/convolution because for both Conv2DTranspose\n", " # operations, output_padding is set to maximum it could be in both dims\n", " # (i.e. exactly one less than the stride of each filter).\n", - " x = layers.Conv2DTranspose(64, [5, 9], activation = \"relu\", strides = [5,9], padding = \"valid\", output_padding = [4, 8])(x)\n", - " x = layers.Conv2DTranspose(32, 11, activation = \"relu\", strides = [9,10], padding = \"valid\", output_padding = [8, 9])(x)\n", - " decoder_outputs = layers.Conv2DTranspose(1, 3, activation = \"sigmoid\", padding = \"same\")(x)\n", - " decoder = keras.Model(latent_inputs, decoder_outputs, name = \"decoder\")\n", + " x = layers.Conv2DTranspose(64, [5, 9], activation=\"relu\", strides=[5,9], padding=\"valid\", output_padding=[4, 8])(x)\n", + " x = layers.Conv2DTranspose(32, 11, activation=\"relu\", strides=[9,10], padding=\"valid\", output_padding=[8, 9])(x)\n", + " \n", + " decoder_outputs = layers.Conv2DTranspose(1, 3, activation=\"sigmoid\", padding=\"same\")(x)\n", + " decoder = keras.Model(latent_inputs, decoder_outputs, name=\"decoder\")\n", " \n", " print(decoder.summary())\n", " return decoder\n", @@ -309,7 +318,9 @@ " with open(os.path.join(model_dir, f'test_loss_{date}.json'), 'w') as json_file:\n", " json.dump(test_loss, json_file, indent = 4)\n", " \n", - " !sh scripts/run_dvcgit.sh model_dir/history_{date}.csv model_dir/vae.weights_{date}.h5 main f\"{date}\"\n", + " print(date)\n", + " !sh scripts/run_dvcgit.sh model_dir/history_{date}.csv f\"{date}\"\n", + " !sh scripts/run_dvcgit.sh model_dir/vae.weights_{date}.h5 f\"{date}\"\n", " !rm model_dir/vae.weights_{date}.h5 # delete copy\n", " \n", "def run_train(num_files, date, vae, data_dir, model_dir):\n", @@ -394,7 +405,8 @@ "for month in months:\n", " for ensemble in ensembles:\n", " download_file(year, month, day, ensemble, data_pdir)\n", - " convert_file(year, month, day, ensemble, data_pdir)\n", + " convert_file(year, month, day, ensemble, data_dir)\n", + " subset_file(f'pres_msl_{year}{month}{day}00_{ensemble}.nc', data_dir)\n", "\n", " if f'pres_msl_{year}{month}{day}00_{ensemble}.nc' in os.listdir(data_dir):\n", " num_files += 1\n", diff --git a/run_on_cluster/cvae-demos/weather-demo/scripts/.ipynb_checkpoints/batch_grib2nc-checkpoint.csh b/run_on_cluster/cvae-demos/weather-demo/scripts/.ipynb_checkpoints/batch_grib2nc-checkpoint.csh new file mode 100644 index 0000000..76e7e29 --- /dev/null +++ b/run_on_cluster/cvae-demos/weather-demo/scripts/.ipynb_checkpoints/batch_grib2nc-checkpoint.csh @@ -0,0 +1,14 @@ +#!/bin/tcsh -f + +#====================== +# Batch convert a bunch +# of grib2 files to .nc +# +# Needs CDO +#====================== + +foreach file ( ./gefs_data/pres_*.grib2 ) + echo Working on $file + set bn = `basename $file .grib2` + cdo -f nc copy $file ./gefs_data/converted/${bn}.nc +end diff --git a/run_on_cluster/cvae-demos/weather-demo/scripts/.ipynb_checkpoints/run_dvcgit-checkpoint.sh b/run_on_cluster/cvae-demos/weather-demo/scripts/.ipynb_checkpoints/run_dvcgit-checkpoint.sh new file mode 100644 index 0000000..9169085 --- /dev/null +++ b/run_on_cluster/cvae-demos/weather-demo/scripts/.ipynb_checkpoints/run_dvcgit-checkpoint.sh @@ -0,0 +1,21 @@ +#!/bin/bash + +# Start Conda and select env (you will need to cross-check the paths and env name) + +source ~/pw/.miniconda3c/etc/profile.d/conda.sh +conda activate cvae_env + +# Run the git commands using input from the command line; +# $1 stores the first entry on the command line that launches +# the shell script and so on. Add as many as you need. + +file_name=$1 +commit_message="$2" + +dvc add "$file_name" +git add .gitignore +git add "${file_name}.dvc" + +dvc push +git commit -m "$commit_message" +git push \ No newline at end of file diff --git a/run_on_cluster/cvae-demos/weather-demo/scripts/batch_grib2nc.csh b/run_on_cluster/cvae-demos/weather-demo/scripts/batch_grib2nc.csh new file mode 100644 index 0000000..76e7e29 --- /dev/null +++ b/run_on_cluster/cvae-demos/weather-demo/scripts/batch_grib2nc.csh @@ -0,0 +1,14 @@ +#!/bin/tcsh -f + +#====================== +# Batch convert a bunch +# of grib2 files to .nc +# +# Needs CDO +#====================== + +foreach file ( ./gefs_data/pres_*.grib2 ) + echo Working on $file + set bn = `basename $file .grib2` + cdo -f nc copy $file ./gefs_data/converted/${bn}.nc +end diff --git a/run_on_cluster/cvae-demos/weather-demo/scripts/cvae.py b/run_on_cluster/cvae-demos/weather-demo/scripts/cvae.py new file mode 100644 index 0000000..ef1b8fe --- /dev/null +++ b/run_on_cluster/cvae-demos/weather-demo/scripts/cvae.py @@ -0,0 +1,428 @@ +# https://keras.io/examples/generative/vae/ +import os, json +import netCDF4 +import glob + +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt + +import tensorflow as tf +from tensorflow import keras +from keras import layers + +from sklearn.model_selection import train_test_split + + +# model defintions ---------------------------------------------------------------------------------------------- + +class Sampling(layers.Layer): + """Uses (z_mean, z_log_var) to sample z, the vector encoding a digit.""" + + def call(self, inputs): + z_mean, z_log_var = inputs + batch = tf.shape(z_mean)[0] + dim = tf.shape(z_mean)[1] + epsilon = tf.keras.backend.random_normal(shape = (batch, dim)) + return z_mean + tf.exp(0.5 * z_log_var) * epsilon + +def build_encoder(latent_dim): + encoder_inputs = keras.Input(shape=(721, 1440, 1)) + + x = layers.Conv2D(32, 11, activation="relu", strides=[9, 10], padding="valid")(encoder_inputs) + x = layers.Conv2D(64, [5,9], activation="relu", strides=[5, 9], padding="valid")(x) + x = layers.Flatten()(x) + x = layers.Dense(16, activation="relu")(x) + + z_mean = layers.Dense(latent_dim, name="z_mean")(x) + z_log_var = layers.Dense(latent_dim, name="z_log_var")(x) + z = Sampling()([z_mean, z_log_var]) + + encoder = keras.Model(encoder_inputs, [z_mean, z_log_var, z], name="encoder") + + print(encoder.summary()) + return encoder + +def build_decoder(latent_dim): + latent_inputs = keras.Input(shape=(latent_dim,)) + + x = layers.Dense(15 * 15 * 64, activation="relu")(latent_inputs) + x = layers.Reshape((15, 15, 64))(x) + # FIXME - there is something wrong here, but at least there is a pattern. + # Using output_padding as a fudge factor -> it may be that there is exactly + # one "missing" filter stamp/convolution because for both Conv2DTranspose + # operations, output_padding is set to maximum it could be in both dims + # (i.e. exactly one less than the stride of each filter). + x = layers.Conv2DTranspose(64, [5, 9], activation="relu", strides=[5,9], padding="valid", output_padding=[4, 8])(x) + x = layers.Conv2DTranspose(32, 11, activation="relu", strides=[9,10], padding="valid", output_padding=[8, 9])(x) + + decoder_outputs = layers.Conv2DTranspose(1, 3, activation="sigmoid", padding="same")(x) + decoder = keras.Model(latent_inputs, decoder_outputs, name="decoder") + + print(decoder.summary()) + return decoder + +class VAE(keras.Model): + def __init__(self, encoder, decoder, **kwargs): + super(VAE, self).__init__(**kwargs) + self.encoder = encoder + self.decoder = decoder + self.total_loss_tracker = keras.metrics.Mean(name = "total_loss") + self.reconstruction_loss_tracker = keras.metrics.Mean(name = "reconstruction_loss") + self.kl_loss_tracker = keras.metrics.Mean(name = "kl_loss") + + @property + def metrics(self): + return [ + self.total_loss_tracker, + self.reconstruction_loss_tracker, + self.kl_loss_tracker, + ] + + def train_step(self, data): + + with tf.GradientTape() as tape: + z_mean, z_log_var, z = self.encoder(data) + reconstruction = self.decoder(z) + # FIXME: Normalize loss with the number of features (28 * 28) + n_features = 28 * 28 + reconstruction_loss = tf.reduce_mean( + tf.reduce_sum( + keras.losses.binary_crossentropy(data, reconstruction), axis = (1, 2) + ) + ) / n_features + kl_loss = -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var)) + kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis = 1)) / n_features + total_loss = (reconstruction_loss + kl_loss) + grads = tape.gradient(total_loss, self.trainable_weights) + self.optimizer.apply_gradients(zip(grads, self.trainable_weights)) + self.total_loss_tracker.update_state(total_loss) + self.reconstruction_loss_tracker.update_state(reconstruction_loss) + self.kl_loss_tracker.update_state(kl_loss) + + return { + "loss": self.total_loss_tracker.result(), + "reconstruction_loss": self.reconstruction_loss_tracker.result(), + "kl_loss": self.kl_loss_tracker.result(), + } + + # Needed to validate (validation loss) and to evaluate + def test_step(self, data): + if type(data) == tuple: + data, _ = data + + z_mean, z_log_var, z = self.encoder(data) + reconstruction = self.decoder(z) + # FIXME: Normalize loss with the number of features (28 * 28) + n_features = 28 * 28 + reconstruction_loss = tf.reduce_mean( + tf.reduce_sum( + keras.losses.binary_crossentropy(data, reconstruction), axis = (1, 2) + ) + ) / n_features + kl_loss = -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var)) + kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis = 1)) / n_features + total_loss = (reconstruction_loss + kl_loss) + # grads = tape.gradient(total_loss, self.trainable_weights) + # self.optimizer.apply_gradients(zip(grads, self.trainable_weights)) + self.total_loss_tracker.update_state(total_loss) + self.reconstruction_loss_tracker.update_state(reconstruction_loss) + self.kl_loss_tracker.update_state(kl_loss) + + return { + "loss": self.total_loss_tracker.result(), + "reconstruction_loss": self.reconstruction_loss_tracker.result(), + "kl_loss": self.kl_loss_tracker.result(), + } + +# --------------------------------------------------------------------------------------------------------------- + +# CNN require huge amounts of RAM, specially during training +# Computational requirements for a single CNN layer: +# - Number of parameters: (+1 is the bias term) +# (filter width * filter height * channels + 1 ) * feature maps +# - Number of float multiplications: +# feature maps * feature map width * feature map height * filter width * filfet height * channels +# ---> For the first layer feature map width and height are the inputs width and height divided by the strides!! +# - Output Memory: +# feature maps * feature map width * feature map height * 32 bits * training batch size +# ---> Reduce batch size to fir in memory! +# Memory for multiple layers: +# - During training: The sum of all the layers +# - During inference: Two consecutive layers + +# If you have memory issues: +# 1. Reuduce batch size +# 2. Increase stride +# 3. Remove layers +# 4. Use 16-bit floats +# 5. Distribute across multiple devices + +# Pooling layers: shrink the input image to reduce computational load, memory usage and number of parameters +# - Pooling neurons have no weights +# - keras.layers.MaxPool2D(pool_size=(2, 2), strides=None, padding='valid', data_format=None) +# - AvgPool2D +# - Only takes the maximum (or average, minimum, etc) value of the pooling_size window +# - If stride > 1 reduces the number of parameters. Stride defaults to pool_size +# - Introduces a level of invariance to small translations and some rotational and scale invariance +# - Invariance is desirable for classification but undersirable for semantic segmentation +# - Invariance is desirable if a small change in the inputs should lead to a small change on the outputs +# - MaxPool2D typically has better performance +# - Can also be applied to depth (channels) instead of spatial direction (see page 469) +# - GlobalAvgPool2D: Calculates the average of the entire feature map + +# Typical CNN architecture: +# - Convolutions(s) with same number of feature maps --> Pooling --> Convolution(s) --> Pooling --> repeat --> Flatten --> Fully connected layer +# - Image gets smaller (less width and height) and deeper (more feature maps) --> Dense --> Dropout --> Repeat +# - Avoid large kernels (window sizes)--> use more layers (except in first layer) +# - Number of repetitions is a hyperparameter to tune +# - Double number of filters after each pooling layer +# - Add keras.layers.Dropout(0.5) to reduce overfitting between dense layers + + +def calculate_final_shape(height, num_conv_2d_layers, stride): + for n in range(num_conv_2d_layers): + height = int(np.ceil(height / stride)) + return height + +# Only works for stride 2... +def calculate_output_paddings(height, num_conv_2d_layers, stride): + output_paddings = [] + for n in range(num_conv_2d_layers): + output_paddings.append(stride - (height % stride) - 1) + height = int(np.ceil(height / stride)) + + output_paddings.reverse() + return output_paddings + +# Works for stride 3 but may need extra work/testing +def _calculate_output_paddings(height, num_conv_layers, stride): + output_paddings = [] + encoder_heights = [] + decoder_heights = [] + output_paddings = [] + encoder_heights.append(height) + + for n in range(num_conv_layers): + height = int(np.ceil(height / stride)) + encoder_heights.append(height) + + encoder_heights.reverse() + + decoder_heights.append(height) + for n in range(num_conv_layers): + height = int(height * stride) + padding = height - encoder_heights[n+1] + print(padding) + #output_paddings.append(padding + stride - int(stride/2) - 2) + height -= padding + decoder_heights.append(height) + if padding == 0: + padding = 2 + elif padding == 2: + padding = 0 + output_paddings.append(padding) + + return output_paddings + +# Only works if latent_dim = 2 +def plot_latent_space(vae, digit_size, channels, n=30, figsize=15, show = False, path = ''): + # display a n*n 2D manifold of digits + #digit_size = 28 + scale = 1.0 + figure = np.zeros((digit_size * n, digit_size * n)) + # linearly spaced coordinates corresponding to the 2D plot + # of digit classes in the latent space + grid_x = np.linspace(-scale, scale, n) + grid_y = np.linspace(-scale, scale, n)[::-1] + + for i, yi in enumerate(grid_y): + for j, xi in enumerate(grid_x): + z_sample = np.array([[xi, yi]]) + x_decoded = vae.decoder.predict(z_sample) + digit = x_decoded[0].reshape(digit_size, digit_size, channels) + figure[ + i * digit_size : (i + 1) * digit_size, + j * digit_size : (j + 1) * digit_size, + ] = digit[:,:,0] + + plt.figure(figsize=(figsize, figsize)) + start_range = digit_size // 2 + end_range = n * digit_size + start_range + pixel_range = np.arange(start_range, end_range, digit_size) + sample_range_x = np.round(grid_x, 1) + sample_range_y = np.round(grid_y, 1) + plt.xticks(pixel_range, sample_range_x) + plt.yticks(pixel_range, sample_range_y) + plt.xlabel("z[0]") + plt.ylabel("z[1]") + plt.imshow(figure, cmap="Greys_r") + if show: + plt.show() + if path: + plt.savefig(path) + + +def plot_images(images, rows, columns, path = '', show = False): + fig = plt.figure(figsize = (10, 7)) + + for i, image in enumerate(images): + fig.add_subplot(rows, columns, i+1) + try: + plt.imshow(image, cmap = 'binary') + except: + plt.imshow(image.squeeze(), cmap = 'binary') + plt.axis('off') + + if show: + plt.show() + if path: + plt.savefig(path) + + + + +# Maybe reconstruction loss is big because it is not normalized? +if __name__ == '__main__': + latent_dim = 3 + train = True + model_dir = './digits' + model_dir = './cifar' + model_dir = './gefs' + n_conv_layers = 4 + stride = 2 + kernel_size = 3 + os.makedirs(model_dir, exist_ok = True) + + #data = load_fashion_mnist() + #data = load_digits_mnist() + #data = load_cifar10() + + # Batch size is too small! + #data = load_gefs_open_data_registry("/home/jovyan/work/pres_sfc_2019010100*") + #data = load_pkl('pres_sfc_2019100100_c00.nc.pkl') + #data = load_many_pkl('pres_msl_201*00_c00.nc.pkl') + + # WORKS FINE, but use selective loading, below, to avoid + # making data copies. + #data, data_min, data_max = load_many_grib('./gefs_data/pres_msl_*.grib2') + #data = load_pkl('gefs.pkl') + + # FIXME: Width and height need to be divisible by number of layers! + # + #print('Input summary:') + #print(data.shape) + + # When using a large data set, + # - mean takes a long time, + # - std takes more than 2x RAM + # - train_test_split makes RAM intensive data copies. + # Instead, selectively load different data sets. + #print('Mean: '+str(np.mean(data))) + # More than doubles RAM usage to find std. + #print('Std: '+str(np.std(data))) + #X_train, X_test = train_test_split(data) + #X_train, X_valid = train_test_split(X_train) + + # Load and then normalize all data based on + # same absolute min/max values. It looks like + # if data is not in np.float32, it will be internally + # converted to np.float32 in vae.fit, so there may be no + # way to cut memory useage there. + X_train, train_min, train_max = load_many_grib( + './gefs_data/pres_msl_201[78]*.grib2', + data_type=np.float32, + min_max_norm=False) + X_test, test_min, test_max = load_many_grib( + './gefs_data/pres_msl_20190[13579]*.grib2', + data_type=np.float32, + min_max_norm=False) + X_valid, valid_min, valid_max = load_many_grib( + './gefs_data/pres_msl_20190[2468]*.grib2', + data_type=np.float32, + min_max_norm=False) + + all_min = np.min([train_min,test_min,valid_min]) + all_max = np.max([train_max,test_max,valid_max]) + all_del = all_max - all_min + + print('Normalize X_train...') + X_train -= all_min + X_train /= all_del + + print('Normalize X_test...') + X_test -= all_min + X_test /= all_del + + print('Normalize X_valid...') + X_valid -= all_min + X_valid /= all_del + + print('Testing shape') + print(np.shape(X_test)) + + print('Training shape') + print(np.shape(X_train)) + + print('Validaiton shape') + print(np.shape(X_valid)) + + batch_size, height, width, channels = X_train.shape + # Plot some inputs + rows = 3 + columns = 4 + sample_input_images = X_train[0:rows*columns] + #plot_images(sample_input_images, rows, columns, path = os.path.join(model_dir, 'sample_input.png')) + + # More conv layers reduces the total parameters at first but then it does not! + encoder = build_encoder(latent_dim, height, width, channels, n_conv_layers, kernel_size, stride, base_filters = 16) + decoder = build_decoder(latent_dim, height, width, channels, n_conv_layers, kernel_size, stride, base_filters = 16) + vae = VAE(encoder, decoder, height*width) + #vae.compile(optimizer=keras.optimizers.Adam()) + vae.compile(optimizer = 'rmsprop') + if train: + early_stopping_cb = keras.callbacks.EarlyStopping(patience = 7, restore_best_weights = True) + history = vae.fit( + X_train, epochs = 5, batch_size = 8, + callbacks = [early_stopping_cb], + validation_data = (X_valid,) + ) + vae.save_weights(os.path.join(model_dir, 'vae')) + hist_pd = pd.DataFrame(history.history) + hist_pd.to_csv(os.path.join(model_dir, 'history.csv'), index = False) + test_loss = vae.evaluate(X_test) + test_loss = dict(zip(["loss", "reconstruction_loss", "kl_loss"], test_loss)) + print('Test loss:') + print(test_loss) + with open(os.path.join(model_dir, 'test_loss.json'), 'w') as json_file: + json.dump(test_loss, json_file, indent = 4) + + else: + vae.load_weights(os.path.join(model_dir, 'vae')) + + if latent_dim == 2: + # Only works if: + if height == width: + #plot_latent_space(vae, height, channels, path = os.path.join(model_dir, 'latent_space.png')) + print('Not plotting anything.') + + # Generating new images + codings = tf.random.normal(shape = [12, latent_dim]) + images = vae.decoder(codings).numpy() + #plot_images(images, 3, 4, path = os.path.join(model_dir, 'generated.png')) + + # Semantic interpolation + codings_grid = tf.reshape(codings, [1, 3, 4, latent_dim]) + larger_grid = tf.image.resize(codings_grid, size = [5, 7]) + interpolated_codings = tf.reshape(larger_grid, [-1, latent_dim]) + images = vae.decoder(interpolated_codings).numpy() + #plot_images(images, 5, 7, path = os.path.join(model_dir, 'interpolated.png')) + + # Encode / decode images + # FIXME: Maybe add a predict method? + # sample_output_images = vae.predict(sample_input_images) + z_mean, z_log_var, z = vae.encoder(sample_input_images) + sample_output_images = vae.decoder(z) + #plot_images(sample_output_images, rows, columns, path = os.path.join(model_dir, 'sample_output.png')) + + diff --git a/run_on_cluster/cvae-demos/weather-demo/scripts/get_data.py b/run_on_cluster/cvae-demos/weather-demo/scripts/get_data.py new file mode 100644 index 0000000..57d3543 --- /dev/null +++ b/run_on_cluster/cvae-demos/weather-demo/scripts/get_data.py @@ -0,0 +1,20 @@ +import os + +# download +def download_file(year, month, day, ensemble, data_predir): + if f'pres_msl_{year}{month}{day}00_{ensemble}.grib2' not in os.listdir(data_predir): + os.system(f'wget -q -P {data_predir} https://noaa-gefs-retrospective.s3.amazonaws.com/GEFSv12/reforecast/{year}/{year}{month}{day}00/{ensemble}/Days%3A1-10/pres_msl_{year}{month}{day}00_{ensemble}.grib2') + +# convert +def convert_file(year, month, day, ensemble, data_dir): + if f'pres_msl_{year}{month}{day}00_{ensemble}.grib2' in os.listdir('./gefs_data') and f'pres_msl_{year}{month}{day}00_{ensemble}.nc' not in os.listdir(data_dir): + os.system(f'cdo -f nc copy ./gefs_data/pres_msl_{year}{month}{day}00_{ensemble}.grib2 {data_dir}pres_msl_{year}{month}{day}00_{ensemble}.nc') + +# subset +def subset_file(data_file, data_dir): + if '.nc' in data_file and f'{data_dir}subset_{data_file}' not in os.listdir(data_dir): + os.system(f'ncks -v msl -d time,1,79,2 {data_dir}{data_file} -O {data_dir}subset_{data_file}') + +# delete (all) +def remove_data(data_predir): + os.system(f'find {data_predir} -type f -delete') \ No newline at end of file diff --git a/run_on_cluster/cvae-demos/weather-demo/scripts/run_dvcgit.sh b/run_on_cluster/cvae-demos/weather-demo/scripts/run_dvcgit.sh new file mode 100644 index 0000000..9169085 --- /dev/null +++ b/run_on_cluster/cvae-demos/weather-demo/scripts/run_dvcgit.sh @@ -0,0 +1,21 @@ +#!/bin/bash + +# Start Conda and select env (you will need to cross-check the paths and env name) + +source ~/pw/.miniconda3c/etc/profile.d/conda.sh +conda activate cvae_env + +# Run the git commands using input from the command line; +# $1 stores the first entry on the command line that launches +# the shell script and so on. Add as many as you need. + +file_name=$1 +commit_message="$2" + +dvc add "$file_name" +git add .gitignore +git add "${file_name}.dvc" + +dvc push +git commit -m "$commit_message" +git push \ No newline at end of file diff --git a/run_on_cluster/cvae-weather-ensemble/.dvc/.gitignore b/run_on_cluster/cvae-weather-ensemble/.dvc/.gitignore new file mode 100644 index 0000000..528f30c --- /dev/null +++ b/run_on_cluster/cvae-weather-ensemble/.dvc/.gitignore @@ -0,0 +1,3 @@ +/config.local +/tmp +/cache diff --git a/run_on_cluster/cvae-weather-ensemble/.dvc/config b/run_on_cluster/cvae-weather-ensemble/.dvc/config new file mode 100644 index 0000000..5caf6f0 --- /dev/null +++ b/run_on_cluster/cvae-weather-ensemble/.dvc/config @@ -0,0 +1,4 @@ +[core] + remote = dvcstorage +['remote "dvcstorage"'] + url = /aws-dvc-bucket diff --git a/run_on_cluster/cvae-weather-ensemble/.dvcignore b/run_on_cluster/cvae-weather-ensemble/.dvcignore new file mode 100644 index 0000000..5197305 --- /dev/null +++ b/run_on_cluster/cvae-weather-ensemble/.dvcignore @@ -0,0 +1,3 @@ +# Add patterns of files dvc should ignore, which could improve +# the performance. Learn more at +# https://dvc.org/doc/user-guide/dvcignore diff --git a/run_on_cluster/cvae-weather-ensemble/.ipynb_checkpoints/README-checkpoint.md b/run_on_cluster/cvae-weather-ensemble/.ipynb_checkpoints/README-checkpoint.md new file mode 100644 index 0000000..012b8a7 --- /dev/null +++ b/run_on_cluster/cvae-weather-ensemble/.ipynb_checkpoints/README-checkpoint.md @@ -0,0 +1,63 @@ +# CVAE Weather Ensemble Model + +This is the CVAE Weather Ensemble Model repository. The model leverages the power of Conditional Variational Autoencoders (CVAE) to provide an ensemble training system for weather predictions while also answering the big data problem. Its capability for online training allows for continuous learning from new data streams without compromising performance. That is, data used to train the model is processed in small batches, the model being retrained with each new batch. + +## Key Features + +* **Conditional Variational Autoencoder (CVAE):** Utilizes CVAE with Conv2d networks to model complex dependencies in weather data, enabling accurate probabilistic forecasts. +* **Online Training:** Implements a dynamic training approach that can adapt to real-time incoming data, ensuring up-to-date forecasts. +* **Data Version Control (DVC):** Manages datasets and model versions effectively with DVC, facilitating reproducibility and collaboration. + +## Getting Started + +To get started with the CVAE Weather Ensemble Model, follow the instructions below to set up your environment. + +### 1. Conda env setup +*The following is the list steps needed to be taken in order to set up a correct conda environment for running this notebook.* + +**Create Environment:** +```bash +source ~/pw/.miniconda3c/etc/profile.d/conda.sh +conda create --name python=3.9 +conda activate +``` + +**Install Pacakages:** + +The following packages need to be installed on top of a typical base Conda env. Install the packages in the following order so the environment solves correctly: +```bash +conda install -y -c conda-forge tensorflow +conda install -y -c conda-forge netCDF4 # For reading nc files +conda install -y -c conda-forge cartopy # For making maps +conda install -y -c conda-forge matplotlib +conda install -y -c conda-forge pandas +conda install -y -c conda-forge scikit-learn +conda install -y -c conda-forge papermill # For running online training +conda install -y -c conda-forge dvc # For dvc +conda install -y -c conda-forge nco +conda install -y -c conda-forge cdo # For converting grib to nc +``` +**Connect Notebook to Environment:** +```bash +conda install -y ipykernel +conda install -y requests +conda install -y -c anaconda jinja2 +python -m ipykernel install --user --name= --display-name "Python ()" +``` + +*`conda env update --file cvae_env.yaml --name ` can also be used to recreate the working conda environment used to build this model. Make sure to still run the first and last commands listed above when activating the environment* + +### 2. Open the `cvae_runner_and_example.ipynb`, `cvae_training.ipynb`, and `cvae_log.ipynb` notebooks + +When opening the notbooks make sure to set each of their kernel to the one just created. Save the notebook after this is done. You can also close out of the `log` and `training` notebooks if wanted. Note: in order to see an updated log when running the online trainging, the `log` notebook must be closed and opened again throughout the session. + +### 3. Continue to `cvae_runner_and_example.ipynb` and follow the instructions in the notebook + +## Needed Specifications +**GPU & Memory:** The program requires at least 32 G of RAM.\ +**Storage:** Mounted stoarge is required for dvc to run properly.\ +**GitHub:** A git repo that can be SSH into is also needed for dvc to run properly. SSH connection must be established on the terminal. + +## + +*This model is based on an [example](https://keras.io/examples/generative/vae/) from Keras.io by fchollet ([GitHub](https://github.com/keras-team/keras-io/blob/master/examples/generative/vae.py))* \ No newline at end of file diff --git a/run_on_cluster/cvae-weather-ensemble/.ipynb_checkpoints/cvae_assess-checkpoint.ipynb b/run_on_cluster/cvae-weather-ensemble/.ipynb_checkpoints/cvae_assess-checkpoint.ipynb new file mode 100644 index 0000000..8c8a521 --- /dev/null +++ b/run_on_cluster/cvae-weather-ensemble/.ipynb_checkpoints/cvae_assess-checkpoint.ipynb @@ -0,0 +1,476 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# **CVAE Assess on a Trained model**\n", + "The goal here is to use a trained CVAE model with new data to create synthetic ensemble members." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# imports\n", + "import tensorflow as tf\n", + "tf.compat.v1.enable_eager_execution()\n", + "\n", + "import os, json\n", + "import netCDF4\n", + "import cartopy\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from scipy.ndimage.filters import gaussian_filter as gf\n", + "\n", + "from tensorflow import keras\n", + "from keras import layers\n", + "import cProfile # For eager execution, https://www.tensorflow.org/guide/eager\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "from scripts.cvae import Sampling, build_encoder, calculate_final_shape, calculate_output_paddings\n", + "from scripts.cvae import build_decoder, VAE, plot_latent_space, plot_images\n", + "\n", + "from scripts.get_data import download_file\n", + "from scripts.get_data import convert_file\n", + "from scripts.get_data import subset_file\n", + "from scripts.get_data import remove_data # removes all data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# data loading\n", + "def load_data(data_dir): \n", + " files = [f for f in os.listdir(data_dir)] # if ('subset' in f and 'tmp' not in f)]\n", + " \n", + " all_data = ((np.expand_dims(\n", + " np.concatenate(\n", + " [netCDF4.Dataset(data_dir + converted_file)['msl'][:] for converted_file in files]\n", + " ),\n", + " -1\n", + " ).astype(\"float32\") - 85000) / (110000 - 85000)).astype(\"float16\")\n", + " \n", + " return all_data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "print(\"TF version:\", tf.__version__)\n", + "print(\"GPU is\", \"available\" if tf.config.list_physical_devices('GPU') else \"NOT AVAILABLE\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load and preprocess the input data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "data_pdir = \"./gefs_data\"\n", + "data_dir = \"./gefs_data/converted/\"\n", + "model_dir = './model_dir'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# example parameters\n", + "ex_year = \"2018\"\n", + "ex_month = \"01\"\n", + "ex_day = \"01\"\n", + "ex_ensemble = \"c00\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# example for getting and converting files \n", + "download_file(ex_year, ex_month, ex_day, ex_ensemble, data_pdir)\n", + "convert_file(ex_year, ex_month, ex_day, ex_ensemble, data_dir)\n", + "slp = load_data(data_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# look at data structure\n", + "print(np.shape(slp))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# grid point locations\n", + "lons = np.loadtxt('coordinates/lon.x')\n", + "lats = np.loadtxt('coordinates/lat.y')\n", + "\n", + "x, y = np.meshgrid(lons,lats)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# example for plot\n", + "def plot_map(images, num_levels, need_filter):\n", + " # plot set up\n", + " c = 'k'\n", + " fig = plt.figure(figsize = (9,6))\n", + " ax = plt.axes(projection = cartopy.crs.LambertConformal())\n", + " \n", + " # add features to the map\n", + " ax.add_feature(cartopy.feature.LAND)\n", + " ax.add_feature(cartopy.feature.OCEAN)\n", + " ax.add_feature(cartopy.feature.LAKES, alpha = 0.5)\n", + " ax.add_feature(cartopy.feature.STATES, edgecolor = 'grey')\n", + " \n", + " # plot each given image\n", + " for i, image in enumerate(images):\n", + " if i == 0: # print only first time step\n", + " if need_filter:\n", + " plt_points = gf(np.squeeze(image) * (110000 - 85000) + 85000, sigma = 0.5, mode = 'nearest')\n", + " print(i, \"filtered mean:\", np.mean(plt_points))\n", + " print(i, \"filtered std:\", np.std(plt_points))\n", + " c = 'r'\n", + " else:\n", + " plt_points = np.squeeze(image[:,:,0]) * (110000 - 85000) + 85000\n", + "\n", + " # contour levels\n", + " data_min = np.min(plt_points) # min level\n", + " data_max = np.max(plt_points) # max level\n", + "\n", + " # plotting the contour\n", + " plt.contour(x, y, plt_points,\n", + " transform = cartopy.crs.PlateCarree(),\n", + " levels = np.linspace(data_min, data_max, num_levels),\n", + " colors = c,\n", + " linewidths = 1) \n", + "\n", + " ax.set_extent([-150, -60, 20, 65]) # ax.set_extent([-120, -73, 23, 50])\n", + "\n", + " plt.title('GEFSv12 MSL 2017 01 01 0000 UTC')\n", + " # plt.colorbar()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "slp.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "plot_map(slp, 20, True)\n", + "# plot_map(slp, 20, False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load ML model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# key CVAE definition parameters\n", + "latent_dim = 2\n", + "batch_size, height, width, channels = slp.shape\n", + "\n", + "encoder = build_encoder(latent_dim)\n", + "decoder = build_decoder(latent_dim)\n", + "vae = VAE(encoder, decoder)\n", + "vae.compile(optimizer = 'rmsprop')\n", + "vae.load_weights(os.path.join('model_dir', 'vae.weights.h5'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "z_mean, z_log_var, z = vae.encoder((np.expand_dims(slp[0:79:2,:,:,:], -1)))\n", + "sample_output_images = vae.decoder(z)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# plt.scatter(z[:, 0], z[:, 1])\n", + "# plt.show()\n", + "\n", + "fig, ax = plt.subplots(1,1)\n", + "ax.plot(z[:,0],z[:,1],\"b-\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Encode, perturb, and decode" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "gf(np.squeeze(sample_output_images[0]) * (110000 - 85000) + 85000, [3,3], mode = 'constant')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# plot each decoded state\n", + "plot_map(sample_output_images, 20, True)\n", + "\n", + "# # plot original\n", + "# print('Filtering...')\n", + "# filtered = gf(np.squeeze(slp[0,:,:,0]) * (110000 - 85000) + 85000, [3,3], mode = 'constant')\n", + "\n", + "# # get the min and max of data\n", + "# data_min = np.min(filtered)\n", + "# data_max = np.max(filtered)\n", + "# # number of levels\n", + "# num_levels = 20\n", + "\n", + "# print('Contour plotting...')\n", + "# plt.contour(x, y, filtered,\n", + "# transform = cartopy.crs.PlateCarree(),\n", + "# levels = np.linspace(data_min, data_max, num_levels), # list of contour levels\n", + "# colors = 'k',\n", + "# linewidths = 2)\n", + "\n", + "# # ax.set_xlim([-150,-60])\n", + "# # ax.set_ylim([20,65])\n", + "# # plt.colorbar()\n", + "# plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Perturb current state" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "perturbed_images_high = vae.decoder(z_mean + z_log_var)\n", + "perturbed_images_low = vae.decoder(z_mean - z_log_var)\n", + "\n", + "print(tf.executing_eagerly())\n", + "# tf.compat.v1.enable_eager_execution()\n", + "\n", + "# Plot setup\n", + "fig = plt.figure(figsize=(9,6))\n", + "ax = plt.axes(projection = cartopy.crs.LambertConformal())\n", + "ax.add_feature(cartopy.feature.LAND)\n", + "ax.add_feature(cartopy.feature.OCEAN)\n", + "ax.add_feature(cartopy.feature.LAKES, alpha = 0.5)\n", + "ax.add_feature(cartopy.feature.STATES, edgecolor='grey')\n", + "\n", + "# Plot each one\n", + "for i, image in enumerate(perturbed_images_high):\n", + " if i == 0 :\n", + " print('Filtering...')\n", + " filtered = gf(np.squeeze(image)*120000/100, [3,3], mode='constant')\n", + " print('Contour plotting...')\n", + " plt.contour(x,y,filtered,\n", + " transform = cartopy.crs.PlateCarree(),\n", + " levels=[980,990,1000,1010,1015,1020,1025,1030,1035,1040,1045,1050],colors='r',linewidths=1) \n", + "\n", + "# Plot each one\n", + "for i, image in enumerate(perturbed_images_low):\n", + " if i == 0 :\n", + " print('Filtering...')\n", + " filtered = gf(np.squeeze(image)*120000/100, [3,3], mode='constant')\n", + " print('Contour plotting...')\n", + " plt.contour(x,y,filtered,\n", + " transform = cartopy.crs.PlateCarree(),\n", + " levels=[980,990,1000,1010,1015,1020,1025,1030,1035,1040,1045,1050],colors='b',linewidths=1) \n", + " \n", + "# Plot original\n", + "plt.contour(x,y,np.squeeze(slp[0,:,:,0])*120000/100,\n", + " transform = cartopy.crs.PlateCarree(),\n", + " levels=[980,990,1000,1010,1015,1020,1025,1030,1035,1040,1045,1050],colors='k',linewidths=2)\n", + "\n", + "plt.title('GEFSv12 Re-forecast SLP 990hPa 2018 01 10 0000 UTC Cycle')\n", + "#ax.set_extent([-120, -73, 23, 50])\n", + "ax.set_extent([-150, -60, 20, 65])\n", + "#plt.colorbar()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Generate totally random weather maps" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "codings = tf.random.normal(shape = [12, latent_dim])\n", + "images = vae.decoder(codings).numpy()\n", + "\n", + "# Plot setup\n", + "fig = plt.figure(figsize=(9,6))\n", + "ax = plt.axes(projection = cartopy.crs.LambertConformal())\n", + "ax.add_feature(cartopy.feature.LAND)\n", + "ax.add_feature(cartopy.feature.OCEAN)\n", + "ax.add_feature(cartopy.feature.LAKES, alpha = 0.5)\n", + "ax.add_feature(cartopy.feature.STATES, edgecolor='grey')\n", + "\n", + "# Plot each one\n", + "for i, image in enumerate(images):\n", + " print('Filtering...')\n", + " filtered = gf(np.squeeze(image)*120000/100, [5,5], mode='constant')\n", + " plt.contour(x,y,filtered,\n", + " transform = cartopy.crs.PlateCarree(),\n", + " levels=[970,980,990,1000,1010,1015,1020,1025,1030,1040,1050,1060],colors='k',linewidths=1)\n", + " \n", + "# plt.title('Random MSL pressure maps from ML model trained with GEFSv12 Re-forecast 2018-2019')\n", + "# ax.set_extent([-120, -73, 23, 50])\n", + "ax.set_extent([-150, -60, 20, 65])\n", + "# plt.colorbar()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "print(latent_dim)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:cvae_env]", + "language": "python", + "name": "conda-env-cvae_env-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/run_on_cluster/cvae-weather-ensemble/.ipynb_checkpoints/cvae_env-checkpoint.yaml b/run_on_cluster/cvae-weather-ensemble/.ipynb_checkpoints/cvae_env-checkpoint.yaml new file mode 100644 index 0000000..8ddf0b1 --- /dev/null +++ b/run_on_cluster/cvae-weather-ensemble/.ipynb_checkpoints/cvae_env-checkpoint.yaml @@ -0,0 +1,412 @@ +name: cvae_env +channels: + - anaconda + - conda-forge + - defaults +dependencies: + - _libgcc_mutex=0.1=conda_forge + - _openmp_mutex=4.5=2_gnu + - absl-py=2.1.0=pyhd8ed1ab_0 + - aiohttp=3.9.5=py39hd1e30aa_0 + - aiohttp-retry=2.8.3=pyhd8ed1ab_0 + - aiosignal=1.3.1=pyhd8ed1ab_0 + - alsa-lib=1.2.12=h4ab18f5_0 + - amqp=5.2.0=pyhd8ed1ab_1 + - annotated-types=0.7.0=pyhd8ed1ab_0 + - ansicolors=1.1.8=pyhd8ed1ab_0 + - antlr-python-runtime=4.9.3=pyhd8ed1ab_1 + - appdirs=1.4.4=pyh9f0ad1d_0 + - asttokens=2.0.5=pyhd3eb1b0_0 + - astunparse=1.6.3=pyhd8ed1ab_0 + - async-timeout=4.0.3=pyhd8ed1ab_0 + - asyncssh=2.14.1=pyhd8ed1ab_0 + - atk-1.0=2.38.0=h04ea711_2 + - atpublic=3.0.1=pyhd8ed1ab_0 + - attr=2.5.1=h166bdaf_1 + - attrs=23.2.0=pyh71513ae_0 + - backcall=0.2.0=pyhd3eb1b0_0 + - backports.zoneinfo=0.2.1=py39hf3d152e_8 + - billiard=4.2.0=py39hd1e30aa_0 + - blosc=1.21.6=hef167b5_0 + - brotli=1.1.0=hd590300_1 + - brotli-bin=1.1.0=hd590300_1 + - brotli-python=1.1.0=py39h3d6467e_1 + - bzip2=1.0.8=h4bc722e_7 + - c-ares=1.32.2=h4bc722e_0 + - ca-certificates=2024.7.4=hbcca054_0 + - cached-property=1.5.2=hd8ed1ab_1 + - cached_property=1.5.2=pyha770c72_1 + - cairo=1.18.0=hbb29018_2 + - cartopy=0.23.0=py39hfc16268_1 + - cdo=2.4.1=h9fe33b1_1 + - celery=5.3.5=pyhd8ed1ab_0 + - certifi=2024.7.4=pyhd8ed1ab_0 + - cffi=1.16.0=py39h7a31438_0 + - cftime=1.6.4=py39hd92a3bb_0 + - charset-normalizer=3.3.2=pyhd8ed1ab_0 + - click=8.1.7=unix_pyh707e725_0 + - click-didyoumean=0.3.1=pyhd8ed1ab_0 + - click-plugins=1.1.1=py_0 + - click-repl=0.3.0=pyhd8ed1ab_0 + - colorama=0.4.6=pyhd8ed1ab_0 + - comm=0.2.1=py39h06a4308_0 + - configobj=5.0.8=pyhd8ed1ab_0 + - contourpy=1.2.1=py39h7633fee_0 + - cryptography=42.0.8=py39h8169da8_0 + - cuda-crt-tools=12.5.82=ha770c72_0 + - cuda-cudart=12.5.82=he02047a_0 + - cuda-cudart_linux-64=12.5.82=h85509e4_0 + - cuda-nvcc-tools=12.5.82=hd3aeb46_0 + - cuda-nvrtc=12.5.82=he02047a_0 + - cuda-nvtx=12.5.82=he02047a_0 + - cuda-nvvm-tools=12.5.82=h59595ed_0 + - cuda-version=12.5=hd4f0392_3 + - cudnn=8.9.7.29=h092f7fd_3 + - cycler=0.12.1=pyhd8ed1ab_0 + - dbus=1.13.6=h5008d03_3 + - debugpy=1.6.7=py39h6a678d5_0 + - decorator=5.1.1=pyhd3eb1b0_0 + - dictdiffer=0.9.0=pyhd8ed1ab_0 + - diskcache=5.6.3=pyhd8ed1ab_0 + - distro=1.9.0=pyhd8ed1ab_0 + - dpath=2.2.0=pyha770c72_0 + - dulwich=0.22.1=py39ha68c5e3_0 + - dvc=3.51.2=pyhd8ed1ab_0 + - dvc-data=3.15.1=pyhd8ed1ab_1 + - dvc-http=2.32.0=pyhd8ed1ab_0 + - dvc-objects=5.1.0=pyhd8ed1ab_1 + - dvc-render=1.0.2=pyhd8ed1ab_0 + - dvc-studio-client=0.21.0=pyhd8ed1ab_0 + - dvc-task=0.4.0=pyhd8ed1ab_0 + - eccodes=2.36.0=h762793a_0 + - entrypoints=0.4=pyhd8ed1ab_0 + - esmf=8.6.1=nompi_h0a5817f_2 + - exceptiongroup=1.2.0=py39h06a4308_0 + - executing=0.8.3=pyhd3eb1b0_0 + - expat=2.6.2=h59595ed_0 + - fftw=3.3.10=nompi_hf1063bd_110 + - filelock=3.15.4=pyhd8ed1ab_0 + - findlibs=0.0.5=pyhd8ed1ab_0 + - flatbuffers=24.3.25=h59595ed_0 + - flatten-dict=0.4.2=pyhd8ed1ab_1 + - flufl.lock=7.1=pyhd8ed1ab_0 + - font-ttf-dejavu-sans-mono=2.37=hab24e00_0 + - font-ttf-inconsolata=3.000=h77eed37_0 + - font-ttf-source-code-pro=2.038=h77eed37_0 + - font-ttf-ubuntu=0.83=h77eed37_2 + - fontconfig=2.14.2=h14ed4e7_0 + - fonts-conda-ecosystem=1=0 + - fonts-conda-forge=1=0 + - fonttools=4.53.1=py39hcd6043d_0 + - freeglut=3.2.2=ha6d2627_3 + - freetype=2.12.1=h267a509_2 + - fribidi=1.0.10=h36c2ea0_0 + - frozenlist=1.4.1=py39hd1e30aa_0 + - fsspec=2024.6.1=pyhff2d567_0 + - funcy=2.0=pyhd8ed1ab_0 + - future=1.0.0=pyhd8ed1ab_0 + - gast=0.5.5=pyhd8ed1ab_0 + - gdk-pixbuf=2.42.12=hb9ae30d_0 + - geos=3.12.2=hac33072_0 + - gettext=0.22.5=h59595ed_2 + - gettext-tools=0.22.5=h59595ed_2 + - giflib=5.2.2=hd590300_0 + - gitdb=4.0.11=pyhd8ed1ab_0 + - gitpython=3.1.43=pyhd8ed1ab_0 + - glib=2.80.3=h8a4344b_1 + - glib-tools=2.80.3=h73ef956_1 + - google-pasta=0.2.0=pyh8c360ce_0 + - grandalf=0.7=pyhd8ed1ab_0 + - graphite2=1.3.13=h59595ed_1003 + - graphviz=11.0.0=hc68bbd7_0 + - grpcio=1.62.2=py39h174d805_0 + - gsl=2.7=he838d99_0 + - gst-plugins-base=1.24.5=hbaaba92_0 + - gstreamer=1.24.5=haf2f30d_0 + - gtk2=2.24.33=h280cfa0_4 + - gto=1.7.1=pyhd8ed1ab_1 + - gts=0.7.6=h977cf35_4 + - h2=4.1.0=pyhd8ed1ab_0 + - h5py=3.11.0=nompi_py39h24b94d4_102 + - harfbuzz=8.5.0=hfac3d4d_0 + - hdf4=4.2.15=h2a13503_7 + - hdf5=1.14.3=nompi_hdf9ad27_105 + - hpack=4.0.0=pyh9f0ad1d_0 + - hydra-core=1.3.2=pyhd8ed1ab_0 + - hyperframe=6.0.1=pyhd8ed1ab_0 + - icu=73.2=h59595ed_0 + - idna=3.7=pyhd8ed1ab_0 + - importlib-metadata=8.0.0=pyha770c72_0 + - importlib-resources=6.4.0=pyhd8ed1ab_0 + - importlib_metadata=8.0.0=hd8ed1ab_0 + - importlib_resources=6.4.0=pyhd8ed1ab_0 + - ipykernel=6.28.0=py39h06a4308_0 + - ipython=8.15.0=py39h06a4308_0 + - iterative-telemetry=0.0.8=pyhd8ed1ab_0 + - jasper=4.2.4=h536e39c_0 + - jedi=0.19.1=py39h06a4308_0 + - jinja2=3.1.2=py39h06a4308_0 + - joblib=1.4.2=pyhd8ed1ab_0 + - jsonschema=4.23.0=pyhd8ed1ab_0 + - jsonschema-specifications=2023.12.1=pyhd8ed1ab_0 + - jupyter_client=8.6.2=pyhd8ed1ab_0 + - jupyter_core=5.7.2=py39hf3d152e_0 + - keras=3.4.1=pyhd8ed1ab_2 + - keyutils=1.6.1=h166bdaf_0 + - kiwisolver=1.4.5=py39h7633fee_1 + - kombu=5.3.3=py39hf3d152e_0 + - krb5=1.21.3=h659f571_0 + - lame=3.100=h166bdaf_1003 + - lcms2=2.16=hb7c19ff_0 + - ld_impl_linux-64=2.38=h1181459_1 + - lerc=4.0.0=h27087fc_0 + - libabseil=20240116.2=cxx17_he02047a_1 + - libaec=1.1.3=h59595ed_0 + - libasprintf=0.22.5=h661eb56_2 + - libasprintf-devel=0.22.5=h661eb56_2 + - libblas=3.9.0=22_linux64_openblas + - libbrotlicommon=1.1.0=hd590300_1 + - libbrotlidec=1.1.0=hd590300_1 + - libbrotlienc=1.1.0=hd590300_1 + - libcap=2.69=h0f662aa_0 + - libcblas=3.9.0=22_linux64_openblas + - libclang-cpp15=15.0.7=default_h127d8a8_5 + - libclang13=18.1.8=default_h6ae225f_0 + - libcublas=12.5.3.2=he02047a_0 + - libcufft=11.2.3.61=he02047a_0 + - libcups=2.3.3=h4637d8d_4 + - libcurand=10.3.6.82=he02047a_0 + - libcurl=8.8.0=hca28451_1 + - libcusolver=11.6.3.83=he02047a_0 + - libcusparse=12.5.1.3=he02047a_0 + - libdeflate=1.20=hd590300_0 + - libedit=3.1.20191231=he28a2e2_2 + - libev=4.33=hd590300_2 + - libevent=2.1.12=hf998b51_1 + - libexpat=2.6.2=h59595ed_0 + - libffi=3.4.4=h6a678d5_1 + - libflac=1.4.3=h59595ed_0 + - libgcc-ng=14.1.0=h77fa898_0 + - libgcrypt=1.11.0=h4ab18f5_0 + - libgd=2.3.3=h119a65a_9 + - libgettextpo=0.22.5=h59595ed_2 + - libgettextpo-devel=0.22.5=h59595ed_2 + - libgfortran-ng=14.1.0=h69a702a_0 + - libgfortran5=14.1.0=hc5f4f2c_0 + - libgit2=1.8.1=he8d1d4c_1 + - libglib=2.80.3=h8a4344b_1 + - libglu=9.0.0=ha6d2627_1004 + - libgomp=14.1.0=h77fa898_0 + - libgpg-error=1.50=h4f305b6_0 + - libgrpc=1.62.2=h15f2491_0 + - libiconv=1.17=hd590300_2 + - libjpeg-turbo=3.0.0=hd590300_1 + - liblapack=3.9.0=22_linux64_openblas + - libllvm15=15.0.7=hb3ce162_4 + - libllvm18=18.1.8=h8b73ec9_1 + - libnetcdf=4.9.2=nompi_h135f659_114 + - libnghttp2=1.58.0=h47da74e_1 + - libnsl=2.0.1=hd590300_0 + - libnvjitlink=12.5.82=he02047a_0 + - libogg=1.3.5=h4ab18f5_0 + - libopenblas=0.3.27=pthreads_hac2b453_1 + - libopus=1.3.1=h7f98852_1 + - libpng=1.6.43=h2797004_0 + - libpq=16.3=ha72fbe1_0 + - libprotobuf=4.25.3=h08a7969_0 + - libre2-11=2023.09.01=h5a48ba9_2 + - librsvg=2.58.2=hf0cb8fb_0 + - libsndfile=1.2.2=hc60ed4a_1 + - libsodium=1.0.18=h36c2ea0_1 + - libsqlite=3.46.0=hde9e2c9_0 + - libssh2=1.11.0=h0841786_0 + - libstdcxx-ng=14.1.0=hc0a3c3a_0 + - libsystemd0=255=h3516f8a_1 + - libtiff=4.6.0=h1dd3fc0_3 + - libudunits2=2.2.28=h40f5838_3 + - libuuid=2.38.1=h0b41bf4_0 + - libvorbis=1.3.7=h9c3ff4c_0 + - libwebp=1.4.0=h2c329e2_0 + - libwebp-base=1.4.0=hd590300_0 + - libxcb=1.16=hd590300_0 + - libxcrypt=4.4.36=hd590300_1 + - libxkbcommon=1.7.0=h2c5496b_1 + - libxml2=2.12.7=h4c95cb1_3 + - libzip=1.10.1=h2629f0a_3 + - libzlib=1.3.1=h4ab18f5_1 + - lz4-c=1.9.4=hcb278e6_0 + - magics=4.15.4=h24e9adf_1 + - magics-python=1.5.8=pyhd8ed1ab_1 + - markdown=3.6=pyhd8ed1ab_0 + - markdown-it-py=3.0.0=pyhd8ed1ab_0 + - markupsafe=2.1.5=py39hd1e30aa_0 + - matplotlib=3.9.1=py39hf3d152e_0 + - matplotlib-base=3.9.1=py39hd75cb13_0 + - matplotlib-inline=0.1.6=py39h06a4308_0 + - mdurl=0.1.2=pyhd8ed1ab_0 + - ml_dtypes=0.3.2=py39hddac248_0 + - mpg123=1.32.6=h59595ed_0 + - multidict=6.0.5=py39hd1e30aa_0 + - munkres=1.1.4=pyh9f0ad1d_0 + - mysql-common=8.3.0=hf1915f5_4 + - mysql-libs=8.3.0=hca2cd23_4 + - namex=0.0.8=pyhd8ed1ab_0 + - nbclient=0.10.0=pyhd8ed1ab_0 + - nbformat=5.10.4=pyhd8ed1ab_0 + - nccl=2.22.3.1=hbc370b7_0 + - nco=5.2.6=hc167251_0 + - ncurses=6.4=h6a678d5_0 + - nest-asyncio=1.6.0=py39h06a4308_0 + - netcdf-fortran=4.6.1=nompi_h228c76a_104 + - netcdf4=1.7.1=nompi_py39hec96367_101 + - networkx=3.2.1=pyhd8ed1ab_0 + - nspr=4.35=h27087fc_0 + - nss=3.102=h593d115_0 + - numpy=1.26.4=py39h474f0d3_0 + - omegaconf=2.3.0=pyhd8ed1ab_0 + - openjpeg=2.5.2=h488ebb8_0 + - openssl=3.3.1=h4bc722e_2 + - opt_einsum=3.3.0=pyhc1e730c_2 + - optree=0.12.1=py39hedd12f3_0 + - orjson=3.10.6=py39h5cde264_0 + - packaging=24.1=pyhd8ed1ab_0 + - pandas=2.2.2=py39hfc16268_1 + - pango=1.54.0=h84a9a3c_0 + - papermill=2.6.0=pyhd8ed1ab_0 + - parso=0.8.3=pyhd3eb1b0_0 + - pathlib2=2.3.7.post1=py39hf3d152e_3 + - pathspec=0.12.1=pyhd8ed1ab_0 + - pcre2=10.44=h0f59acf_0 + - pexpect=4.8.0=pyhd3eb1b0_3 + - pickleshare=0.7.5=pyhd3eb1b0_1003 + - pillow=10.4.0=py39h16a7006_0 + - pip=24.0=py39h06a4308_0 + - pixman=0.43.2=h59595ed_0 + - pkgutil-resolve-name=1.3.10=pyhd8ed1ab_1 + - platformdirs=3.11.0=pyhd8ed1ab_0 + - ply=3.11=pyhd8ed1ab_2 + - proj=9.4.1=hb784bbd_0 + - prompt-toolkit=3.0.43=py39h06a4308_0 + - prompt_toolkit=3.0.43=hd8ed1ab_0 + - protobuf=4.25.3=py39h1be52a0_0 + - psutil=5.9.0=py39h5eee18b_0 + - pthread-stubs=0.4=h36c2ea0_1001 + - ptyprocess=0.7.0=pyhd3eb1b0_2 + - pulseaudio-client=17.0=hb77b528_0 + - pure_eval=0.2.2=pyhd3eb1b0_0 + - pycparser=2.22=pyhd8ed1ab_0 + - pydantic=2.8.2=pyhd8ed1ab_0 + - pydantic-core=2.20.1=py39h5cde264_0 + - pydot=3.0.1=py39hf3d152e_0 + - pygit2=1.15.1=py39hcd6043d_0 + - pygments=2.18.0=pyhd8ed1ab_0 + - pygtrie=2.5.0=pyhd8ed1ab_0 + - pyopenssl=24.0.0=pyhd8ed1ab_0 + - pyparsing=3.1.2=pyhd8ed1ab_0 + - pyproj=3.6.1=py39heb31609_7 + - pyqt=5.15.9=py39h52134e7_5 + - pyqt5-sip=12.12.2=py39h3d6467e_5 + - pyshp=2.3.1=pyhd8ed1ab_0 + - pysocks=1.7.1=pyha2e5f31_6 + - python=3.9.18=h0755675_1_cpython + - python-dateutil=2.9.0=pyhd8ed1ab_0 + - python-fastjsonschema=2.20.0=pyhd8ed1ab_0 + - python-flatbuffers=24.3.25=pyh59ac667_0 + - python-gssapi=1.8.3=py39h01551a1_0 + - python-tzdata=2024.1=pyhd8ed1ab_0 + - python_abi=3.9=4_cp39 + - pytz=2024.1=pyhd8ed1ab_0 + - pywin32-on-windows=0.1.0=pyh1179c8e_3 + - pyyaml=6.0.1=py39hd1e30aa_1 + - pyzmq=26.0.3=py39ha1047a2_0 + - qhull=2020.2=h434a139_5 + - qt-main=5.15.8=ha2b5568_22 + - re2=2023.09.01=h7f4b329_2 + - readline=8.2=h5eee18b_0 + - referencing=0.35.1=pyhd8ed1ab_0 + - requests=2.32.3=pyhd8ed1ab_0 + - rich=13.7.1=pyhd8ed1ab_0 + - rpds-py=0.19.0=py39h5cde264_0 + - ruamel.yaml=0.18.6=py39hd1e30aa_0 + - ruamel.yaml.clib=0.2.8=py39hd1e30aa_0 + - scikit-learn=1.5.1=py39hf7b0125_0 + - scipy=1.13.1=py39haf93ffa_0 + - scmrepo=3.3.6=pyhd8ed1ab_0 + - semver=3.0.2=pyhd8ed1ab_0 + - setuptools=69.5.1=py39h06a4308_0 + - shapely=2.0.5=py39h1254fa4_0 + - shellingham=1.5.4=pyhd8ed1ab_0 + - shortuuid=1.0.13=pyhd8ed1ab_0 + - shtab=1.7.1=pyhd8ed1ab_0 + - simplejson=3.19.2=py39hd1e30aa_0 + - sip=6.7.12=py39h3d6467e_0 + - six=1.16.0=pyh6c4a22f_0 + - smmap=5.0.0=pyhd8ed1ab_0 + - snappy=1.2.1=ha2e4443_0 + - sqlite=3.32.3=hcee41ef_1 + - sqltrie=0.11.0=pyhd8ed1ab_0 + - stack_data=0.2.0=pyhd3eb1b0_0 + - tabulate=0.9.0=pyhd8ed1ab_1 + - tempest-remap=2.2.0=h13910d2_3 + - tenacity=8.5.0=pyhd8ed1ab_0 + - tensorboard=2.16.2=pyhd8ed1ab_0 + - tensorboard-data-server=0.7.0=py39hd4f0224_1 + - tensorflow=2.16.2=cuda120py39h298b457_0 + - tensorflow-base=2.16.2=cuda120py39h1d778e6_0 + - tensorflow-estimator=2.16.2=cuda120py39h225b957_0 + - termcolor=2.4.0=pyhd8ed1ab_0 + - threadpoolctl=3.5.0=pyhc1e730c_0 + - tk=8.6.13=noxft_h4845f30_101 + - toml=0.10.2=pyhd8ed1ab_0 + - tomli=2.0.1=pyhd8ed1ab_0 + - tomlkit=0.13.0=pyha770c72_0 + - tornado=6.4.1=py39hd3abc70_0 + - tqdm=4.66.4=pyhd8ed1ab_0 + - traitlets=5.14.3=pyhd8ed1ab_0 + - typer=0.12.3=pyhd8ed1ab_0 + - typer-slim=0.12.3=pyhd8ed1ab_0 + - typer-slim-standard=0.12.3=hd8ed1ab_0 + - typing-extensions=4.12.2=hd8ed1ab_0 + - typing_extensions=4.12.2=pyha770c72_0 + - tzdata=2024a=h04d1e81_0 + - udunits2=2.2.28=h40f5838_3 + - unicodedata2=15.1.0=py39hd1e30aa_0 + - urllib3=2.2.2=pyhd8ed1ab_1 + - vine=5.1.0=pyhd8ed1ab_0 + - voluptuous=0.15.2=pyhd8ed1ab_0 + - wcwidth=0.2.5=pyhd3eb1b0_0 + - werkzeug=3.0.3=pyhd8ed1ab_0 + - wheel=0.43.0=py39h06a4308_0 + - wrapt=1.16.0=py39hd1e30aa_0 + - xcb-util=0.4.1=hb711507_2 + - xcb-util-image=0.4.0=hb711507_2 + - xcb-util-keysyms=0.4.1=hb711507_0 + - xcb-util-renderutil=0.3.10=hb711507_0 + - xcb-util-wm=0.4.2=hb711507_0 + - xkeyboard-config=2.42=h4ab18f5_0 + - xorg-fixesproto=5.0=h7f98852_1002 + - xorg-inputproto=2.3.2=h7f98852_1002 + - xorg-kbproto=1.0.7=h7f98852_1002 + - xorg-libice=1.1.1=hd590300_0 + - xorg-libsm=1.2.4=h7391055_0 + - xorg-libx11=1.8.9=hb711507_1 + - xorg-libxau=1.0.11=hd590300_0 + - xorg-libxdmcp=1.1.3=h7f98852_0 + - xorg-libxext=1.3.4=h0b41bf4_2 + - xorg-libxfixes=5.0.3=h7f98852_1004 + - xorg-libxi=1.7.10=h7f98852_0 + - xorg-libxrender=0.9.11=hd590300_0 + - xorg-renderproto=0.11.1=h7f98852_1002 + - xorg-xextproto=7.3.0=h0b41bf4_1003 + - xorg-xf86vidmodeproto=2.3.1=h7f98852_1002 + - xorg-xproto=7.0.31=h7f98852_1007 + - xz=5.4.6=h5eee18b_1 + - yaml=0.2.5=h7f98852_2 + - yarl=1.9.4=py39hd1e30aa_0 + - zc.lockfile=3.0.post1=pyhd8ed1ab_0 + - zeromq=4.3.5=h75354e8_4 + - zipp=3.19.2=pyhd8ed1ab_0 + - zlib=1.3.1=h4ab18f5_1 + - zstandard=0.23.0=py39h623c9ba_0 + - zstd=1.5.6=ha6fb4c9_0 +prefix: /home/lobielodan/pw/.miniconda3c/envs/cvae_env diff --git a/run_on_cluster/cvae-weather-ensemble/CVAE_log.ipynb b/run_on_cluster/cvae-weather-ensemble/.ipynb_checkpoints/cvae_log-checkpoint.ipynb similarity index 84% rename from run_on_cluster/cvae-weather-ensemble/CVAE_log.ipynb rename to run_on_cluster/cvae-weather-ensemble/.ipynb_checkpoints/cvae_log-checkpoint.ipynb index 92b2bca..49c5981 100644 --- a/run_on_cluster/cvae-weather-ensemble/CVAE_log.ipynb +++ b/run_on_cluster/cvae-weather-ensemble/.ipynb_checkpoints/cvae_log-checkpoint.ipynb @@ -5,10 +5,10 @@ "id": "f6226ca5-0869-4973-8369-3469cbdff43c", "metadata": { "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, + "duration": 0.002566, + "end_time": "2024-07-23T20:49:42.273369", + "exception": false, + "start_time": "2024-07-23T20:49:42.270803", "status": "completed" }, "tags": [] @@ -23,10 +23,10 @@ "id": "094dca89-dffa-4a56-8263-371fb372a2cb", "metadata": { "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, + "duration": 2.682325, + "end_time": "2024-07-23T20:49:44.962081", + "exception": false, + "start_time": "2024-07-23T20:49:42.279756", "status": "completed" }, "tags": [] @@ -57,10 +57,10 @@ "id": "06a8b533-cdaa-4efd-938b-ef1d5a84338e", "metadata": { "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, + "duration": 0.005176, + "end_time": "2024-07-23T20:49:44.969982", + "exception": false, + "start_time": "2024-07-23T20:49:44.964806", "status": "completed" }, "tags": [] @@ -76,17 +76,17 @@ "id": "4b08e8b0-f9fa-40b9-852b-8650e681cff2", "metadata": { "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, + "duration": 0.008429, + "end_time": "2024-07-23T20:49:44.983860", + "exception": false, + "start_time": "2024-07-23T20:49:44.975431", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ - "data_prefix = \"./gefs_data\"\n", + "data_pdir = \"./gefs_data\"\n", "data_dir = \"./gefs_data/converted/\"\n", "model_dir = './model_dir'" ] @@ -97,10 +97,10 @@ "id": "2ff4403c-9c45-4129-902b-93ebfe9749e3", "metadata": { "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, + "duration": 0.018122, + "end_time": "2024-07-23T20:49:45.004728", + "exception": false, + "start_time": "2024-07-23T20:49:44.986606", "status": "completed" }, "tags": [] @@ -109,21 +109,13 @@ "source": [ "# data definitions\n", "\n", - "# data download\n", - "def get_data(year, month, day, ensemble):\n", - " if f'pres_msl_{year}{month}{day}00_{ensemble}.grib2' not in os.listdir(data_prefix):\n", - " !wget -q -P {data_prefix} https://noaa-gefs-retrospective.s3.amazonaws.com/GEFSv12/reforecast/{year}/{year}{month}{day}00/{ensemble}/Days%3A1-10/pres_msl_{year}{month}{day}00_{ensemble}.grib2\n", - "\n", - "# data deletion\n", - "def remove_data():\n", - " !find {data_prefix} -type f -delete\n", + "from scripts.get_data import download_file\n", + "from scripts.get_data import convert_file\n", + "from scripts.get_data import subset_file\n", + "from scripts.get_data import remove_data # removes all data\n", "\n", "# data loading\n", - "def load_data(): \n", - " for f in os.listdir(data_dir):\n", - " if '.nc' in f:\n", - " os.system(f'ncks -v msl -d time,1,79,2 {data_dir}{f} -O {data_dir}subset_{f}')\n", - " \n", + "def load_data(data_dir): \n", " files = [f for f in os.listdir(data_dir) if ('subset' in f and 'tmp' not in f)]\n", " \n", " all_data = ((np.expand_dims(\n", @@ -141,10 +133,10 @@ "id": "d03ebb2a-bc7b-4f52-98d3-ec85c51c7a5f", "metadata": { "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, + "duration": 0.005869, + "end_time": "2024-07-23T20:49:45.012647", + "exception": false, + "start_time": "2024-07-23T20:49:45.006778", "status": "completed" }, "tags": [] @@ -200,10 +192,10 @@ "id": "f0144303-f1c9-4220-8178-3988d707688c", "metadata": { "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, + "duration": 0.021877, + "end_time": "2024-07-23T20:49:45.040347", + "exception": false, + "start_time": "2024-07-23T20:49:45.018470", "status": "completed" }, "tags": [] @@ -336,10 +328,10 @@ "id": "a61f638f-42a9-4d44-8f31-c6c42017a6bf", "metadata": { "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, + "duration": 0.028705, + "end_time": "2024-07-23T20:49:45.071202", + "exception": false, + "start_time": "2024-07-23T20:49:45.042497", "status": "completed" }, "tags": [] @@ -348,7 +340,7 @@ "source": [ "# training definitions\n", "\n", - "def train_model(X_train, X_test, X_valid, date, vae):\n", + "def train_model(X_train, X_test, X_valid, date, vae, model_dir):\n", " early_stopping_cb = keras.callbacks.EarlyStopping(patience = 5, restore_best_weights = True) # stops training early if the validation loss does not improve\n", " \n", " if os.path.exists(os.path.join(model_dir, 'vae.weights.h5')): # if the model has already been trained at least once, load that model\n", @@ -374,28 +366,21 @@ " with open(os.path.join(model_dir, f'test_loss_{date}.json'), 'w') as json_file:\n", " json.dump(test_loss, json_file, indent = 4)\n", " \n", - " !dvc add model_dir/history_{date}.csv\n", - " !git add .gitignore model_dir/history_{date}.csv.dvc\n", - " \n", - " !dvc add model_dir/vae.weights_{date}.h5\n", - " !git add .gitignore model_dir/vae.weights_{date}.h5.dvc\n", - " \n", - " !dvc push\n", - " !git commit -m {date}\n", - " !git push\n", - " \n", + " print(date)\n", + " !sh scripts/run_dvcgit.sh model_dir/history_{date}.csv f\"{date}\" >> dvc.log.tmp\n", + " !sh scripts/run_dvcgit.sh model_dir/vae.weights_{date}.h5 f\"{date}\" >> dvc.log.tmp\n", " !rm model_dir/vae.weights_{date}.h5 # delete copy\n", " \n", - "def run_train(num_files, date, vae):\n", - " slp = load_data() # load data\n", + "def run_train(num_files, date, vae, data_dir, model_dir):\n", + " slp = load_data(data_dir) # load data\n", " print(\"shape:\", np.shape(slp)) # verify data shape\n", " \n", " # split the data - y values are throw away\n", " X_train, X_test, y_train, y_test = train_test_split(slp[0:(num_files * 40 - 1), :, :, :], np.arange(0, num_files * 40 - 1), test_size = 0.2, random_state = 1)\n", " X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size = 0.25, random_state = 1) # 0.25 x 0.8 = 0.2\n", "\n", - " train_model(X_train, X_test, X_valid, date, vae)\n", - " remove_data()" + " train_model(X_train, X_test, X_valid, date, vae, model_dir)\n", + " remove_data(data_pdir)" ] }, { @@ -403,10 +388,10 @@ "id": "3defdd05-1774-4e56-bd64-2f760c004842", "metadata": { "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, + "duration": 0.005146, + "end_time": "2024-07-23T20:49:45.078958", + "exception": false, + "start_time": "2024-07-23T20:49:45.073812", "status": "completed" }, "tags": [] @@ -421,10 +406,10 @@ "id": "3dd6a16c-2ff9-42fe-bdcd-b8a970f88b9f", "metadata": { "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, + "duration": 0.577523, + "end_time": "2024-07-23T20:49:45.664344", + "exception": false, + "start_time": "2024-07-23T20:49:45.086821", "status": "completed" }, "tags": [] @@ -455,10 +440,10 @@ "id": "2c79096d-dee9-4ffc-9279-442e9232f20f", "metadata": { "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, + "duration": 0.012456, + "end_time": "2024-07-23T20:49:45.680375", + "exception": false, + "start_time": "2024-07-23T20:49:45.667919", "status": "completed" }, "tags": [ @@ -477,13 +462,13 @@ { "cell_type": "code", "execution_count": null, - "id": "aac9414d", + "id": "b75c1e82", "metadata": { "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, + "duration": 0.020528, + "end_time": "2024-07-23T20:49:45.704650", + "exception": false, + "start_time": "2024-07-23T20:49:45.684122", "status": "completed" }, "tags": [ @@ -494,7 +479,7 @@ "source": [ "# Parameters\n", "year = \"2000\"\n", - "days = \"00\"\n" + "day = \"03\"\n" ] }, { @@ -503,10 +488,10 @@ "id": "b952473d-6ec6-4581-8b49-d6d472b4e0cb", "metadata": { "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, + "duration": 281.785065, + "end_time": "2024-07-23T20:54:27.493035", + "exception": false, + "start_time": "2024-07-23T20:49:45.707970", "status": "completed" }, "tags": [] @@ -520,34 +505,18 @@ "# get wanted data -------------------------------------------------------------------------\n", "for month in months:\n", " for ensemble in ensembles:\n", - " get_data(year, month, day, ensemble)\n", + " download_file(year, month, day, ensemble, data_pdir)\n", + " convert_file(year, month, day, ensemble, data_dir)\n", + " subset_file(f'pres_msl_{year}{month}{day}00_{ensemble}.nc', data_dir)\n", "\n", - " if f'pres_msl_{year}{month}{day}00_{ensemble}.grib2' in os.listdir(data_prefix):\n", + " if f'pres_msl_{year}{month}{day}00_{ensemble}.nc' in os.listdir(data_dir):\n", " num_files += 1\n", "# ------------------------------------------------------------------------------------------ \n", - " \n", - "!csh batch_grib2nc.csh # convert files\n", - "run_train(num_files, day, vae) # run training \n", + " \n", + "run_train(num_files, year + day, vae, data_dir, model_dir) # run training \n", " \n", "print(\"Memory usage after training:\", tf.config.experimental.get_memory_info('GPU:0'))" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "aefcd075-13ea-43c7-8315-677f741cb094", - "metadata": { - "papermill": { - "duration": null, - "end_time": null, - "exception": null, - "start_time": null, - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [] } ], "metadata": { @@ -570,17 +539,17 @@ }, "papermill": { "default_parameters": {}, - "duration": 0.046609, - "end_time": "2024-07-19T17:19:12.297363", + "duration": null, + "end_time": null, "environment_variables": {}, "exception": null, "input_path": "CVAE_training.ipynb", "output_path": "CVAE_log.ipynb", "parameters": { - "days": "00", + "day": "03", "year": "2000" }, - "start_time": "2024-07-19T17:19:12.250754", + "start_time": "2024-07-23T20:49:41.510277", "version": "2.6.0" } }, diff --git a/run_on_cluster/cvae-weather-ensemble/.ipynb_checkpoints/cvae_runner_and_example-checkpoint.ipynb b/run_on_cluster/cvae-weather-ensemble/.ipynb_checkpoints/cvae_runner_and_example-checkpoint.ipynb new file mode 100644 index 0000000..6f609cd --- /dev/null +++ b/run_on_cluster/cvae-weather-ensemble/.ipynb_checkpoints/cvae_runner_and_example-checkpoint.ipynb @@ -0,0 +1,413 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# **CVAE Weather Ensemble Model**\n", + "\n", + "This notebook provides examples for working with weather data along with a section to launch online learning." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Libraries and Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import os, json\n", + "\n", + "import papermill as pm\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import tensorflow as tf\n", + "import netCDF4\n", + "import cartopy\n", + "\n", + "from tensorflow import keras\n", + "from keras import layers\n", + "from sklearn.model_selection import train_test_split \n", + "\n", + "print(\"TF version:\", tf.__version__)\n", + "print(\"GPU is\", \"available\" if tf.config.list_physical_devices('GPU') else \"NOT AVAILABLE\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# make needed directories\n", + "# use -p to make parent directories\n", + "!mkdir -p gefs_data/converted\n", + "!mkdir -p model_dir" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# DVC initialization and storage set up\n", + "!dvc init --subdir\n", + "!dvc remote add -d dvcstorage /aws-dvc-bucket" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# initial commit to git\n", + "!git add .\n", + "!git commit -m \"loaded dependencies, mkdir -p, DVC init\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Download and Convert Data\n", + "On my [first Google hit for GEFS](https://www.ncei.noaa.gov/products/weather-climate-models/global-ensemble-forecast), I clicked on [AWS Open Data Registry for GEFS](https://registry.opendata.aws/noaa-gefs-pds/) and selected [NOAA GEFS Re-forecast](https://registry.opendata.aws/noaa-gefs-reforecast/) which has no useage restrictions. The [GEFS Re-forecast data documentation](https://noaa-gefs-retrospective.s3.amazonaws.com/Description_of_reforecast_data.pdf) is very clear. The date of the initialization of the re-forecast is in the file name in the format YYYYMMDDHH. The c00, p01, p02, p03, p04 are the control and perturbation ensemble members (5 total)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from scripts.get_data import download_file\n", + "from scripts.get_data import convert_file\n", + "from scripts.get_data import remove_data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "data_pdir = \"./gefs_data\"\n", + "data_dir = \"./gefs_data/converted/\"\n", + "model_dir = './model_dir'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Examples" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# example parameters\n", + "ex_year = \"2018\"\n", + "ex_month = \"01\"\n", + "ex_day = \"01\"\n", + "ex_ensemble = \"c00\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# example for getting and converting files \n", + "download_file(ex_year, ex_month, ex_day, ex_ensemble, data_pdir)\n", + "convert_file(ex_year, ex_month, ex_day, ex_ensemble, data_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# example for loading data\n", + "dataset = netCDF4.Dataset(data_dir + f\"pres_msl_{ex_year}{ex_month}{ex_day}00_{ex_ensemble}.nc\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# example for simple data access\n", + "print(dataset) # look at data structure\n", + "print(dataset.variables.keys())\n", + "\n", + "for var in dataset.variables:\n", + " print(dataset.variables[var])\n", + " # print(dataset.variables[var][:]) # prints actual data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# example for plot\n", + "fig = plt.figure(figsize=(9,6))\n", + "ax = plt.axes(projection = cartopy.crs.LambertConformal())\n", + "\n", + "ax.add_feature(cartopy.feature.LAND)\n", + "ax.add_feature(cartopy.feature.OCEAN)\n", + "ax.add_feature(cartopy.feature.LAKES, alpha = 0.5)\n", + "ax.add_feature(cartopy.feature.STATES, edgecolor='grey')\n", + "ax.set_extent([-120, -73, 23, 50])\n", + "\n", + "plt.contour(\n", + " dataset.variables['lon'][:], # longitudes\n", + " dataset.variables['lat'][:], # latitudes\n", + " dataset.variables['msl'][0,:,:], # actual data\n", + " transform = cartopy.crs.PlateCarree()) #, levels=np.arange(30000,110000,20000))\n", + "\n", + "plt.title('GEFSv12 SLP 2019 01 10 0000 UTC Cycle')\n", + "plt.colorbar()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Execute Online Training\n", + "\n", + "*add a note about how datat is randomized, sorted, and stuff*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# clean up example data before executing online session\n", + "remove_data(data_pdir)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# parameters for pm -> change the lists to fit your needs\n", + "years = [\"2000\", \"2001\", \"2002\", \"2003\"]#, \"2004\", \"2005\", \"2006\"] # , \"2007\", \"2008\", \"2009\", \n", + " # \"2010\", \"2011\", \"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\"]\n", + "days = [\"01\", \"10\", \"20\"] # \"02\", \"03\", \"04\", \"05\", \"06\", \"07\", \"08\", \"09\", \"10\", \"11\", \"12\", \"13\", \"14\", \"15\", \n", + " # \"16\", \"17\", \"18\", \"19\", \"20\", \"21\", \"22\", \"23\", \"24\", \"25\", \"26\", \"27\", \"28\", \"29\", \"30\", \"31\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "for y in years:\n", + " for d in days:\n", + " pm.execute_notebook(\n", + " 'cvae_training.ipynb',\n", + " 'cvae_log.ipynb',\n", + " parameters = dict(year = y, day = d),\n", + " kernel_name = 'cvae_env' # this should be changed to whatever you chose to be during environment set up\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Try different filter sizes\n", + "# Aim for large initial filter > 200km scale (about 10)\n", + "# Aim for some, but minimal overlap in initial filter.\n", + "\n", + "# Aim for smallish second filter, but still try to reduce dimensionality\n", + "# to make dense network tractable later. No overlap (but no good\n", + "# reason why this is).\n", + "\n", + "# ----------------------Input: 721 x 1440--------------\n", + "\n", + "# For Lat = 721,\n", + "# K = 11 -> K_radius = 5.0 -> S = 9 -> H_out = 79.0\n", + "\n", + "# For Lon = 1440,\n", + "# K = 11 -> K_radius = 5.0 -> S = 10 -> H_out = 143.0\n", + "\n", + "# -----------------------Layer1: 79 x 143----------------\n", + "\n", + "# For Lat = 79,\n", + "# K = 5 -> K_radius = 2.0 -> S = 5 -> H_out = 15.0\n", + "\n", + "# For Lon = 143,\n", + "# K = 9 -> K_radius = 4.0 -> S = 9 -> H_out = 15.0\n", + "\n", + "'''\n", + "H_in = 1440\n", + "P = 0\n", + "K_list = [3, 5, 7, 9, 11, 13, 15] # Kernel size\n", + "S_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13] # Stride\n", + "\n", + "for K in K_list:\n", + " for S in S_list:\n", + " K_radius = np.floor(np.divide(K, 2)) # Half width of number of points around the central point\n", + " K_diameter = K - 1 # Number of points around the central point, ASSUMES K = ODD\n", + " # S = K # S = K is stride necessary to have non-overlapping filters\n", + " print('K = ' + str(K) + ' -> K_radius = ' + str(K_radius) + ' -> S = ' + str(S) + ' -> H_out = ' + str((H_in + (2 * P) - K_diameter) / S))\n", + " print('')\n", + "'''" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Display a grid of sampled digits" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# if latent_dim == 2:\n", + "# plot_latent_space(vae, path = os.path.join(model_dir, 'latent_space.png'))\n", + "\n", + "# # Generating new images\n", + "# codings = tf.random.normal(shape = [12, latent_dim])\n", + "# images = vae.decoder(codings).numpy()\n", + "# plot_images(images, 3, 4, path = os.path.join(model_dir, 'generated.png'))\n", + "\n", + "# # Semantic interpolation\n", + "# codings_grid = tf.reshape(codings, [1, 3, 4, latent_dim])\n", + "# larger_grid = tf.image.resize(codings_grid, size = [5, 7])\n", + "# interpolated_codings = tf.reshape(larger_grid, [-1, latent_dim])\n", + "# images = vae.decoder(interpolated_codings).numpy()\n", + "# plot_images(images, 5, 7, path = os.path.join(model_dir, 'interpolated.png'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# plot_latent_space(vae)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Display how the latent space clusters different digit classes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# def plot_label_clusters(vae, data, labels):\n", + "# # display a 2D plot of the digit classes in the latent space\n", + "# z_mean, _, _ = vae.encoder.predict(data)\n", + "# plt.figure(figsize=(12, 10))\n", + "# plt.scatter(z_mean[:, 0], z_mean[:, 1], c=labels)\n", + "# plt.colorbar()\n", + "# plt.xlabel(\"z[0]\")\n", + "# plt.ylabel(\"z[1]\")\n", + "# plt.show()\n", + "\n", + "\n", + "# (x_train, y_train), _ = keras.datasets.mnist.load_data()\n", + "# x_train = np.expand_dims(x_train, -1).astype(\"float32\") / 255\n", + "\n", + "# plot_label_clusters(vae, x_train, y_train)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:cvae_env]", + "language": "python", + "name": "conda-env-cvae_env-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/run_on_cluster/cvae-weather-ensemble/.ipynb_checkpoints/cvae_training-checkpoint.ipynb b/run_on_cluster/cvae-weather-ensemble/.ipynb_checkpoints/cvae_training-checkpoint.ipynb new file mode 100644 index 0000000..414a099 --- /dev/null +++ b/run_on_cluster/cvae-weather-ensemble/.ipynb_checkpoints/cvae_training-checkpoint.ipynb @@ -0,0 +1,442 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f6226ca5-0869-4973-8369-3469cbdff43c", + "metadata": {}, + "source": [ + "# **CVAE Training**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "094dca89-dffa-4a56-8263-371fb372a2cb", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import os, json\n", + "\n", + "import papermill as pm\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import tensorflow as tf\n", + "import netCDF4\n", + "import cartopy\n", + "\n", + "from tensorflow import keras\n", + "from keras import layers\n", + "from sklearn.model_selection import train_test_split \n", + "\n", + "print(\"TF version:\", tf.__version__)\n", + "print(\"GPU is\", \"available\" if tf.config.list_physical_devices('GPU') else \"NOT AVAILABLE\")" + ] + }, + { + "cell_type": "markdown", + "id": "06a8b533-cdaa-4efd-938b-ef1d5a84338e", + "metadata": {}, + "source": [ + "# Download and Convert Data" + ] + }, + { + "cell_type": "markdown", + "id": "412796a9-48ba-4a38-b8ae-d1ef35f1fab5", + "metadata": {}, + "source": [ + "### How we cut down the data\n", + "\n", + "1. In the `subset_file` function from the `get_data` library, we used the `ncks -v -d ,,, infile.nc -O subset_infile.nc` command to grab every other time step from the original data file. \n", + "2. In the `load_data` function, we grab the subseted, converted files and concatenate them together to form one dataset before implementing min/max normalization scaling and casting the DataFrame to `float16`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4b08e8b0-f9fa-40b9-852b-8650e681cff2", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "data_pdir = \"./gefs_data\"\n", + "data_dir = \"./gefs_data/converted/\"\n", + "model_dir = './model_dir'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ff4403c-9c45-4129-902b-93ebfe9749e3", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# data definitions\n", + "\n", + "from scripts.get_data import download_file\n", + "from scripts.get_data import convert_file\n", + "from scripts.get_data import subset_file\n", + "from scripts.get_data import remove_data # removes all data\n", + "\n", + "# data loading\n", + "def load_data(data_dir): \n", + " files = [f for f in os.listdir(data_dir) if ('subset' in f and 'tmp' not in f)] # get th list of subseted and converted files from the data directory\n", + " \n", + " all_data = ((np.expand_dims(\n", + " np.concatenate(\n", + " [netCDF4.Dataset(data_dir + converted_file)['msl'][:] for converted_file in files] # grab the msl data from each of the files\n", + " ),\n", + " -1\n", + " ).astype(\"float32\") - 85000) / (110000 - 85000)).astype(\"float16\") # min/max normalization scaling and data casting\n", + " print(all_data)\n", + " return all_data" + ] + }, + { + "cell_type": "markdown", + "id": "d03ebb2a-bc7b-4f52-98d3-ec85c51c7a5f", + "metadata": { + "tags": [] + }, + "source": [ + "# Neural Network Design\n", + "\n", + "We need to get to a small latent space. Conv2D networks are good because they help reduce the number of connections in a network in a meaningful way. I'm using terms as defined in [this definition of conv2D](https://towardsdatascience.com/conv2d-to-finally-understand-what-happens-in-the-forward-pass-1bbaafb0b148).\n", + "\n", + "**Definitions:**\n", + "K -> kernel size;\n", + "P -> padding;\n", + "S -> stride;\n", + "D -> Dilation;\n", + "G -> Groups\n", + "\n", + "**Filter options:**\n", + "Longitude is easy because it is large and even, so as long as you have an even stride, you get integer results when dividing.\n", + "e.g. lon 9: stride 4, lat 7: stride 5\n", + "\n", + "- Latitude - whole numbers occurr for P = 2 & K = 3 or K = 11.\n", + "- 11 grid points * 0.25 deg * 100 km/deg = 275 km filter window (a good scale for weather)\n", + "- 9 grid points * 0.25 deg * 100 km/deg = 225 km\n", + "- Longitude - whole numbers occur for P = 0 & K = 11 (nice match with Latitude), P = 1 & K = 3 or 13, P = 2 & K = 5.\n", + "\n", + "For a 5 x 7 filter with 3 stride (no overlap) and no padding:\n", + "- lat: (721 - 4) / 3 = 239 possible steps (good whole number!)\n", + "- lon: (1440 - 4) / 3 = 478.6666 possible steps\n", + "\n", + "## Load and Preprocess Training Data:\n", + "The standard way of manipulating arrays in Conv2D layers in TF is to use arrays in the shape:\n", + "`batch_size, height, width, channels = data.shape`\n", + "In our case, the the `batch_size` is the number of image frames (i.e. separate samples or rows in a `.csv` file), the `height` and `width` define the size of the image frame in number of pixels, and the `channels` are the number of layers in the frames. Typically, channels are color layers (e.g. RGB or CMYK) but in our case, we could use different metereological variables. However, for this first experiment, **we only need one channel** because we're only going to use mean sea level pressure (msl).\n", + "\n", + "## Build the Encoder:\n", + "GFS grids I have available here are at 0.25 degree resolution. I'm doing this as a \"worst case\" scenario since there are also 0.5 and 1.0 degree grids with lower resolution but I can't find that data quickly and don't know what's available.\n", + "\n", + "These 0.25 degree grids are 721 x 1440.\n", + "Each forecast file is 3 hourly for 10 days = 8 steps/forecast * 10 days = 80 \"frames\"\n", + "This demo is only using two forecasts from the control ensemble\n", + "(one launched Jan 01, 2019 and one launched Jan 02, 2019) -> this is only \n", + "a small subset of the variability possible in the model.\n", + "\n", + "This particular data set spans 2000-2019 and there are 5 ensemble members.\n", + "\n", + "## Build the Decoder:\n", + "With the 11 x 11 and 5 x 5 filters, non-overlapping stride, applied here, we have a final \"image\" size of 14 x 27 and 64 channels." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f0144303-f1c9-4220-8178-3988d707688c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# model defintions\n", + "\n", + "class Sampling(layers.Layer):\n", + " \"\"\"Uses (z_mean, z_log_var) to sample z, the vector encoding a digit.\"\"\"\n", + "\n", + " def call(self, inputs):\n", + " z_mean, z_log_var = inputs\n", + " batch = tf.shape(z_mean)[0]\n", + " dim = tf.shape(z_mean)[1]\n", + " epsilon = tf.keras.backend.random_normal(shape = (batch, dim))\n", + " return z_mean + tf.exp(0.5 * z_log_var) * epsilon\n", + "\n", + "def build_encoder(latent_dim):\n", + " encoder_inputs = keras.Input(shape=(721, 1440, 1))\n", + " \n", + " x = layers.Conv2D(32, 11, activation=\"relu\", strides=[9, 10], padding=\"valid\")(encoder_inputs)\n", + " x = layers.Conv2D(64, [5,9], activation=\"relu\", strides=[5, 9], padding=\"valid\")(x)\n", + " x = layers.Flatten()(x)\n", + " x = layers.Dense(16, activation=\"relu\")(x)\n", + " \n", + " z_mean = layers.Dense(latent_dim, name=\"z_mean\")(x)\n", + " z_log_var = layers.Dense(latent_dim, name=\"z_log_var\")(x)\n", + " z = Sampling()([z_mean, z_log_var])\n", + " \n", + " encoder = keras.Model(encoder_inputs, [z_mean, z_log_var, z], name=\"encoder\")\n", + " \n", + " print(encoder.summary())\n", + " return encoder\n", + "\n", + "def build_decoder(latent_dim):\n", + " latent_inputs = keras.Input(shape=(latent_dim,))\n", + " \n", + " x = layers.Dense(15 * 15 * 64, activation=\"relu\")(latent_inputs)\n", + " x = layers.Reshape((15, 15, 64))(x)\n", + " # FIXME - there is something wrong here, but at least there is a pattern.\n", + " # Using output_padding as a fudge factor -> it may be that there is exactly\n", + " # one \"missing\" filter stamp/convolution because for both Conv2DTranspose\n", + " # operations, output_padding is set to maximum it could be in both dims\n", + " # (i.e. exactly one less than the stride of each filter).\n", + " x = layers.Conv2DTranspose(64, [5, 9], activation=\"relu\", strides=[5,9], padding=\"valid\", output_padding=[4, 8])(x)\n", + " x = layers.Conv2DTranspose(32, 11, activation=\"relu\", strides=[9,10], padding=\"valid\", output_padding=[8, 9])(x)\n", + " \n", + " decoder_outputs = layers.Conv2DTranspose(1, 3, activation=\"sigmoid\", padding=\"same\")(x)\n", + " decoder = keras.Model(latent_inputs, decoder_outputs, name=\"decoder\")\n", + " \n", + " print(decoder.summary())\n", + " return decoder\n", + "\n", + "class VAE(keras.Model):\n", + " def __init__(self, encoder, decoder, **kwargs):\n", + " super(VAE, self).__init__(**kwargs)\n", + " self.encoder = encoder\n", + " self.decoder = decoder\n", + " self.total_loss_tracker = keras.metrics.Mean(name = \"total_loss\")\n", + " self.reconstruction_loss_tracker = keras.metrics.Mean(name = \"reconstruction_loss\")\n", + " self.kl_loss_tracker = keras.metrics.Mean(name = \"kl_loss\")\n", + "\n", + " @property\n", + " def metrics(self):\n", + " return [\n", + " self.total_loss_tracker,\n", + " self.reconstruction_loss_tracker,\n", + " self.kl_loss_tracker,\n", + " ]\n", + "\n", + " def train_step(self, data):\n", + " \n", + " with tf.GradientTape() as tape:\n", + " z_mean, z_log_var, z = self.encoder(data)\n", + " reconstruction = self.decoder(z)\n", + " # FIXME: Normalize loss with the number of features (28 * 28)\n", + " n_features = 28 * 28\n", + " reconstruction_loss = tf.reduce_mean(\n", + " tf.reduce_sum(\n", + " keras.losses.binary_crossentropy(data, reconstruction), axis = (1, 2)\n", + " )\n", + " ) / n_features\n", + " kl_loss = -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var))\n", + " kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis = 1)) / n_features\n", + " total_loss = (reconstruction_loss + kl_loss)\n", + " grads = tape.gradient(total_loss, self.trainable_weights)\n", + " self.optimizer.apply_gradients(zip(grads, self.trainable_weights))\n", + " self.total_loss_tracker.update_state(total_loss)\n", + " self.reconstruction_loss_tracker.update_state(reconstruction_loss)\n", + " self.kl_loss_tracker.update_state(kl_loss)\n", + " \n", + " return {\n", + " \"loss\": self.total_loss_tracker.result(),\n", + " \"reconstruction_loss\": self.reconstruction_loss_tracker.result(),\n", + " \"kl_loss\": self.kl_loss_tracker.result(),\n", + " }\n", + "\n", + " # Needed to validate (validation loss) and to evaluate\n", + " def test_step(self, data):\n", + " if type(data) == tuple:\n", + " data, _ = data\n", + " \n", + " z_mean, z_log_var, z = self.encoder(data)\n", + " reconstruction = self.decoder(z)\n", + " # FIXME: Normalize loss with the number of features (28 * 28)\n", + " n_features = 28 * 28\n", + " reconstruction_loss = tf.reduce_mean(\n", + " tf.reduce_sum(\n", + " keras.losses.binary_crossentropy(data, reconstruction), axis = (1, 2)\n", + " )\n", + " ) / n_features\n", + " kl_loss = -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var))\n", + " kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis = 1)) / n_features\n", + " total_loss = (reconstruction_loss + kl_loss)\n", + " # grads = tape.gradient(total_loss, self.trainable_weights)\n", + " # self.optimizer.apply_gradients(zip(grads, self.trainable_weights))\n", + " self.total_loss_tracker.update_state(total_loss)\n", + " self.reconstruction_loss_tracker.update_state(reconstruction_loss)\n", + " self.kl_loss_tracker.update_state(kl_loss)\n", + " \n", + " return {\n", + " \"loss\": self.total_loss_tracker.result(),\n", + " \"reconstruction_loss\": self.reconstruction_loss_tracker.result(),\n", + " \"kl_loss\": self.kl_loss_tracker.result(),\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a61f638f-42a9-4d44-8f31-c6c42017a6bf", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# training definitions\n", + "\n", + "def train_model(X_train, X_test, X_valid, date, vae, model_dir):\n", + " early_stopping_cb = keras.callbacks.EarlyStopping(patience = 5, restore_best_weights = True) # stops training early if the validation loss does not improve\n", + " \n", + " if os.path.exists(os.path.join(model_dir, 'vae.weights.h5')): # if the model has already been trained at least once, load that model\n", + " vae.load_weights(os.path.join(model_dir, 'vae.weights.h5'))\n", + "\n", + " history = vae.fit(\n", + " X_train, epochs = 50, batch_size = 40,\n", + " callbacks = [early_stopping_cb],\n", + " validation_data = (X_valid,)\n", + " )\n", + "\n", + " vae.save_weights(os.path.join(model_dir, 'vae.weights.h5')) # save model weights after training\n", + " !cp model_dir/vae.weights.h5 model_dir/vae.weights_{date}.h5 # make a copy to dvc save\n", + " \n", + " hist_pd = pd.DataFrame(history.history)\n", + " hist_pd.to_csv(os.path.join(model_dir, f'history_{date}.csv'), index = False)\n", + "\n", + " test_loss = vae.evaluate(X_test)\n", + " test_loss = dict(zip([\"loss\", \"reconstruction_loss\", \"kl_loss\"], test_loss))\n", + "\n", + " print('Test loss:', test_loss)\n", + "\n", + " with open(os.path.join(model_dir, f'test_loss_{date}.json'), 'w') as json_file:\n", + " json.dump(test_loss, json_file, indent = 4)\n", + " \n", + " print(date)\n", + " !sh scripts/run_dvcgit.sh model_dir/history_{date}.csv f\"{date}\"\n", + " !sh scripts/run_dvcgit.sh model_dir/vae.weights_{date}.h5 f\"{date}\"\n", + " !rm model_dir/vae.weights_{date}.h5 # delete copy\n", + " \n", + "def run_train(num_files, date, vae, data_dir, model_dir):\n", + " slp = load_data(data_dir) # load data\n", + " print(\"shape:\", np.shape(slp)) # verify data shape\n", + " \n", + " # split the data - y values are throw away\n", + " X_train, X_test, y_train, y_test = train_test_split(slp[0:(num_files * 40 - 1), :, :, :], np.arange(0, num_files * 40 - 1), test_size = 0.2, random_state = 1)\n", + " X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size = 0.25, random_state = 1) # 0.25 x 0.8 = 0.2\n", + "\n", + " train_model(X_train, X_test, X_valid, date, vae, model_dir)\n", + " remove_data(data_pdir)" + ] + }, + { + "cell_type": "markdown", + "id": "3defdd05-1774-4e56-bd64-2f760c004842", + "metadata": {}, + "source": [ + "# Train the VAE model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3dd6a16c-2ff9-42fe-bdcd-b8a970f88b9f", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# model build\n", + "\n", + "latent_dim = 2\n", + "\n", + "# build encoder\n", + "encoder = build_encoder(latent_dim)\n", + "print(\"Memory usage after building encoder:\", tf.config.experimental.get_memory_info('GPU:0'))\n", + "\n", + "# build decoder\n", + "decoder = build_decoder(latent_dim)\n", + "print(\"Memory usage after building decoder:\", tf.config.experimental.get_memory_info('GPU:0'))\n", + "\n", + "# build VAE (variational autoencoder)\n", + "vae = VAE(encoder, decoder)\n", + "vae.compile(optimizer = 'rmsprop') \n", + "print(\"Memory usage after building VAE:\", tf.config.experimental.get_memory_info('GPU:0'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2c79096d-dee9-4ffc-9279-442e9232f20f", + "metadata": { + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [ + "# parameter cell for pm \n", + "year = \"2018\"\n", + "day = \"01\"\n", + "months = [\"01\", \"02\", \"03\", \"04\", \"05\", \"06\", \"07\", \"08\", \"09\", \"10\", \"11\", \"12\"] \n", + "ensembles = [\"c00\"] #, \"p01\", \"p02\", \"p03\", \"p04\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b952473d-6ec6-4581-8b49-d6d472b4e0cb", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# training\n", + "\n", + "num_files = 0\n", + "\n", + "# get wanted data -------------------------------------------------------------------------\n", + "for month in months:\n", + " for ensemble in ensembles:\n", + " download_file(year, month, day, ensemble, data_pdir)\n", + " convert_file(year, month, day, ensemble, data_dir)\n", + " subset_file(f'pres_msl_{year}{month}{day}00_{ensemble}.nc', data_dir)\n", + "\n", + " if f'pres_msl_{year}{month}{day}00_{ensemble}.nc' in os.listdir(data_dir):\n", + " num_files += 1\n", + "# ------------------------------------------------------------------------------------------ \n", + " \n", + "run_train(num_files, year + day, vae, data_dir, model_dir) # run training \n", + " \n", + "print(\"Memory usage after training:\", tf.config.experimental.get_memory_info('GPU:0'))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:cvae_env]", + "language": "python", + "name": "conda-env-cvae_env-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/run_on_cluster/cvae-weather-ensemble/.ipynb_checkpoints/dvc.log-checkpoint.tmp b/run_on_cluster/cvae-weather-ensemble/.ipynb_checkpoints/dvc.log-checkpoint.tmp new file mode 100644 index 0000000..e02e73c --- /dev/null +++ b/run_on_cluster/cvae-weather-ensemble/.ipynb_checkpoints/dvc.log-checkpoint.tmp @@ -0,0 +1,50 @@ + +To track the changes with git, run: + + git add model_dir/.gitignore model_dir/history_200001.csv.dvc + +To enable auto staging, run: + + dvc config core.autostage true +1 file pushed +On branch main +Your branch is up to date with 'origin/main'. + +Changes not staged for commit: + (use "git add ..." to update what will be committed) + (use "git restore ..." to discard changes in working directory) + modified: CVAE_example.ipynb + modified: CVAE_log.ipynb + +Untracked files: + (use "git add ..." to include in what will be committed) + dvc.log.tmp + gefs_data/ + model_dir/ + +no changes added to commit (use "git add" and/or "git commit -a") + +To track the changes with git, run: + + git add model_dir/vae.weights_200001.h5.dvc model_dir/.gitignore + +To enable auto staging, run: + + dvc config core.autostage true +1 file pushed +On branch main +Your branch is up to date with 'origin/main'. + +Changes not staged for commit: + (use "git add ..." to update what will be committed) + (use "git restore ..." to discard changes in working directory) + modified: CVAE_example.ipynb + modified: CVAE_log.ipynb + +Untracked files: + (use "git add ..." to include in what will be committed) + dvc.log.tmp + gefs_data/ + model_dir/ + +no changes added to commit (use "git add" and/or "git commit -a") diff --git a/run_on_cluster/cvae-weather-ensemble/CVAE.ipynb b/run_on_cluster/cvae-weather-ensemble/CVAE.ipynb deleted file mode 100644 index e1e0ea0..0000000 --- a/run_on_cluster/cvae-weather-ensemble/CVAE.ipynb +++ /dev/null @@ -1,1070 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# **Demo using CVAE on CORE2 data**\n", - "This is based on an example from Keras.io by fchollet. Please see last cell for original code and links.\n", - "\n", - "# Conda env Setup\n", - "The following is the list steps needed to be taken in order to set up a correct conda environment for running this notebook.\n", - "\n", - "**Create Environment:**\n", - "```bash\n", - "source ~/pw/.miniconda3c/etc/profile.d/conda.sh\n", - "conda create --name NAME python=3.9\n", - "conda activate NAME\n", - "```\n", - "\n", - "**Install Pacakages:** \n", - "\n", - "The following packages need to be installed on top of a typical base Conda env. Install the packages in the following order so the environment solves correctly:\n", - "```bash\n", - "conda install -y -c conda-forge tensorflow\n", - "conda install -y -c conda-forge netCDF4 # For reading nc files\n", - "conda install -y -c conda-forge cartopy # For making maps\n", - "conda install -y -c conda-forge matplotlib\n", - "conda install -y -c conda-forge pandas\n", - "conda install -y -c conda-forge scikit-learn\n", - "conda install -y -c conda-forge papermill\n", - "conda install -y -c conda-forge cdo # For converting grib to nc\n", - "```\n", - "**Connect Notebook to Environment:**\n", - "```bash\n", - "conda install -y ipykernel\n", - "conda install -y requests\n", - "conda install -y -c anaconda jinja2\n", - "python -m ipykernel install --user --name=NAME --display-name \"Python (NAME)\"\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Libraries and Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import os, json, CVAE_helpers\n", - "\n", - "import papermill as pm\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import tensorflow as tf\n", - "import netCDF4\n", - "import cartopy\n", - "\n", - "from tensorflow import keras\n", - "from keras import layers\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "print(\"TF version:\", tf.__version__)\n", - "print(\"GPU is\", \"available\" if tf.config.list_physical_devices('GPU') else \"NOT AVAILABLE\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# make needed directories\n", - "!mkdir gefs_data\n", - "!mkdir gefs_data/converted\n", - "\n", - "data_prefix = \"./gefs_data\"\n", - "data_dir = \"/home/lobielodan/parsl_mpi/run_on_cluster/cvae-weather-ensemble/gefs_data/converted/\" # change to match your own directory" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Download and Convert Data\n", - "On my [first Google hit for GEFS](https://www.ncei.noaa.gov/products/weather-climate-models/global-ensemble-forecast), I clicked on [AWS Open Data Registry for GEFS](https://registry.opendata.aws/noaa-gefs-pds/) and selected [NOAA GEFS Re-forecast](https://registry.opendata.aws/noaa-gefs-reforecast/) which has no useage restrictions. The [GEFS Re-forecast data documentation](https://noaa-gefs-retrospective.s3.amazonaws.com/Description_of_reforecast_data.pdf) is very clear and we're going to download two files, 57 MB each. The date of the initialization of the re-forecast is in the file name in the format YYYYMMDDHH. The c00, p01, p02, p03, p04 are the control and perturbation ensemble members (5 total)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# data download\n", - "def get_data(year, month, day, ensembles):\n", - " num_files = 0\n", - " \n", - " for ensemble in ensembles:\n", - " if f'pres_msl_{year}{month}{day}00_{ensemble}.grib2' not in os.listdir(data_prefix):\n", - " !wget -q -P {data_prefix} https://noaa-gefs-retrospective.s3.amazonaws.com/GEFSv12/reforecast/{year}/{year}{month}{day}00/{ensemble}/Days%3A1-10/pres_msl_{year}{month}{day}00_{ensemble}.grib2\n", - " num_files += 1\n", - " \n", - " return num_files" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# delete all files\n", - "def remove_data():\n", - " !find {data_prefix} -type f -delete" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Examples:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# example for getting and converting files -> change the lists to fit your needs\n", - "year_e = \"2018\"\n", - "month_e = \"01\" \n", - "day_e = \"01\"\n", - "ensembles_e = [\"c00\", \"p01\"]\n", - "\n", - "num_files = get_data(year_e, month_e, day_e, ensembles_e)\n", - "!csh batch_grib2nc.csh" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# example for loading data\n", - "dataset = netCDF4.Dataset(data_dir + \"pres_msl_2018010100_c00.nc\")\n", - "dataset2 = netCDF4.Dataset(data_dir + \"pres_msl_2018010100_p01.nc\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# example for simple data access\n", - "print(dataset) # look at data structure\n", - "print(dataset.variables.keys())\n", - "\n", - "for var in dataset.variables:\n", - " print(dataset.variables[var])\n", - " # print(dataset.variables[var][:]) # prints actual data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# example for plot\n", - "fig = plt.figure(figsize=(9,6))\n", - "ax = plt.axes(projection = cartopy.crs.LambertConformal())\n", - "\n", - "ax.add_feature(cartopy.feature.LAND)\n", - "ax.add_feature(cartopy.feature.OCEAN)\n", - "ax.add_feature(cartopy.feature.LAKES, alpha = 0.5)\n", - "ax.add_feature(cartopy.feature.STATES, edgecolor='grey')\n", - "ax.set_extent([-120, -73, 23, 50])\n", - "\n", - "plt.contour(\n", - " dataset.variables['lon'][:], # longitudes\n", - " dataset.variables['lat'][:], # latitudes\n", - " dataset.variables['msl'][0,:,:], # actual data\n", - " transform = cartopy.crs.PlateCarree()) #, levels=np.arange(30000,110000,20000))\n", - "\n", - "plt.title('GEFSv12 SLP 2019 01 10 0000 UTC Cycle')\n", - "plt.colorbar()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Neural Network Design\n", - "\n", - "We need to get to a small latent space. Conv2D networks are good because they help reduce the number of connections in a network in a meaningful way. I'm using terms as defined in [this definition of conv2D](https://towardsdatascience.com/conv2d-to-finally-understand-what-happens-in-the-forward-pass-1bbaafb0b148).\n", - "\n", - "**Definitions:**\n", - "K -> kernel size;\n", - "P -> padding;\n", - "S -> stride;\n", - "D -> Dilation;\n", - "G -> Groups\n", - "\n", - "**Filter options:**\n", - "Longitude is easy because it is large and even, so as long as you have an even stride, you get integer results when dividing.\n", - "e.g. lon 9: stride 4, lat 7: stride 5\n", - "\n", - "- Latitude - whole numbers occurr for P = 2 & K = 3 or K = 11.\n", - "- 11 grid points * 0.25 deg * 100 km/deg = 275 km filter window (a good scale for weather)\n", - "- 9 grid points * 0.25 deg * 100 km/deg = 225 km\n", - "- Longitude - whole numbers occur for P = 0 & K = 11 (nice match with Latitude), P = 1 & K = 3 or 13, P = 2 & K = 5.\n", - "\n", - "For a 5 x 7 filter with 3 stride (no overlap) and no padding:\n", - "- lat: (721 - 4) / 3 = 239 possible steps (good whole number!)\n", - "- lon: (1440 - 4) / 3 = 478.6666 possible steps" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Try different filter sizes\n", - "# Aim for large initial filter > 200km scale (about 10)\n", - "# Aim for some, but minimal overlap in initial filter.\n", - "\n", - "# Aim for smallish second filter, but still try to reduce dimensionality\n", - "# to make dense network tractable later. No overlap (but no good\n", - "# reason why this is).\n", - "\n", - "# ----------------------Input: 721 x 1440--------------\n", - "\n", - "# For Lat = 721,\n", - "# K = 11 -> K_radius = 5.0 -> S = 9 -> H_out = 79.0\n", - "\n", - "# For Lon = 1440,\n", - "# K = 11 -> K_radius = 5.0 -> S = 10 -> H_out = 143.0\n", - "\n", - "# -----------------------Layer1: 79 x 143----------------\n", - "\n", - "# For Lat = 79,\n", - "# K = 5 -> K_radius = 2.0 -> S = 5 -> H_out = 15.0\n", - "\n", - "# For Lon = 143,\n", - "# K = 9 -> K_radius = 4.0 -> S = 9 -> H_out = 15.0\n", - "\n", - "H_in = 1440\n", - "P = 0\n", - "K_list = [3, 5, 7, 9, 11, 13, 15] # Kernel size\n", - "S_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13] # Stride\n", - "\n", - "for K in K_list:\n", - " for S in S_list:\n", - " K_radius = np.floor(np.divide(K, 2)) # Half width of number of points around the central point\n", - " K_diameter = K - 1 # Number of points around the central point, ASSUMES K = ODD\n", - " # S = K # S = K is stride necessary to have non-overlapping filters\n", - " print('K = ' + str(K) + ' -> K_radius = ' + str(K_radius) + ' -> S = ' + str(S) + ' -> H_out = ' + str((H_in + (2 * P) - K_diameter) / S))\n", - " print('')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create Sampling Layer:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "class Sampling(layers.Layer):\n", - " \"\"\"Uses (z_mean, z_log_var) to sample z, the vector encoding a digit.\"\"\"\n", - "\n", - " def call(self, inputs):\n", - " z_mean, z_log_var = inputs\n", - " batch = tf.shape(z_mean)[0]\n", - " dim = tf.shape(z_mean)[1]\n", - " epsilon = tf.keras.backend.random_normal(shape=(batch, dim))\n", - " return z_mean + tf.exp(0.5 * z_log_var) * epsilon" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Build the Encoder:\n", - "GFS grids I have available here are at 0.25 degree resolution. I'm doing this as a \"worst case\" scenario since there are also 0.5 and 1.0 degree grids with lower resolution but I can't find that data quickly and don't know what's available.\n", - "\n", - "These 0.25 degree grids are 721 x 1440.\n", - "Each forecast file is 3 hourly for 10 days = 8 steps/forecast * 10 days = 80 \"frames\"\n", - "This demo is only using two forecasts from the control ensemble\n", - "(one launched Jan 01, 2019 and one launched Jan 02, 2019) -> this is only \n", - "a small subset of the variability possible in the model.\n", - "\n", - "This particular data set spans 2000-2019 and there are 5 ensemble members." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "print(\"Memory usage before designing the neural network:\", tf.config.experimental.get_memory_info('GPU:0'))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def build_encoder(latent_dim):\n", - " encoder_inputs = keras.Input(shape=(721, 1440, 1))\n", - " \n", - " x = layers.Conv2D(32, 11, activation = \"relu\", strides = [9, 10], padding = \"valid\")(encoder_inputs)\n", - " x = layers.Conv2D(64, [5,9], activation = \"relu\", strides = [5, 9], padding = \"valid\")(x)\n", - " x = layers.Flatten()(x)\n", - " x = layers.Dense(16, activation=\"relu\")(x)\n", - " \n", - " z_mean = layers.Dense(latent_dim, name=\"z_mean\")(x)\n", - " z_log_var = layers.Dense(latent_dim, name=\"z_log_var\")(x)\n", - " z = Sampling()([z_mean, z_log_var])\n", - " \n", - " encoder = keras.Model(encoder_inputs, [z_mean, z_log_var, z], name = \"encoder\")\n", - " \n", - " print(encoder.summary())\n", - " return encoder" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Build the Decoder:\n", - "\n", - "With the 11 x 11 and 5 x 5 filters, non-overlapping stride, applied here, we have a final \"image\" size of 14 x 27 and 64 channels." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Why 7 x 7 x 64?\n", - "# 7 x 7 \"image\" remaining after two conv2D operations x 64 filters/channels" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def build_decoder(latent_dim):\n", - " latent_inputs = keras.Input(shape=(latent_dim,))\n", - " x = layers.Dense(15 * 15 * 64, activation=\"relu\")(latent_inputs)\n", - " x = layers.Reshape((15, 15, 64))(x)\n", - " # FIXME - there is something wrong here, but at least there is a pattern.\n", - " # Using output_padding as a fudge factor -> it may be that there is exactly\n", - " # one \"missing\" filter stamp/convolution because for both Conv2DTranspose\n", - " # operations, output_padding is set to maximum it could be in both dims\n", - " # (i.e. exactly one less than the stride of each filter).\n", - " x = layers.Conv2DTranspose(64, [5, 9], activation = \"relu\", strides = [5,9], padding = \"valid\", output_padding = [4, 8])(x)\n", - " x = layers.Conv2DTranspose(32, 11, activation = \"relu\", strides = [9,10], padding = \"valid\", output_padding = [8, 9])(x)\n", - " decoder_outputs = layers.Conv2DTranspose(1, 3, activation = \"sigmoid\", padding = \"same\")(x)\n", - " decoder = keras.Model(latent_inputs, decoder_outputs, name = \"decoder\")\n", - " \n", - " print(decoder.summary())\n", - " return decoder" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define the VAE as a `Model` with a Custom `train_step`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "class VAE(keras.Model):\n", - " def __init__(self, encoder, decoder, **kwargs):\n", - " super(VAE, self).__init__(**kwargs)\n", - " self.encoder = encoder\n", - " self.decoder = decoder\n", - " self.total_loss_tracker = keras.metrics.Mean(name = \"total_loss\")\n", - " self.reconstruction_loss_tracker = keras.metrics.Mean(name = \"reconstruction_loss\")\n", - " self.kl_loss_tracker = keras.metrics.Mean(name = \"kl_loss\")\n", - "\n", - " @property\n", - " def metrics(self):\n", - " return [\n", - " self.total_loss_tracker,\n", - " self.reconstruction_loss_tracker,\n", - " self.kl_loss_tracker,\n", - " ]\n", - "\n", - " def train_step(self, data):\n", - " \n", - " with tf.GradientTape() as tape:\n", - " z_mean, z_log_var, z = self.encoder(data)\n", - " reconstruction = self.decoder(z)\n", - " # FIXME: Normalize loss with the number of features (28 * 28)\n", - " n_features = 28 * 28\n", - " reconstruction_loss = tf.reduce_mean(\n", - " tf.reduce_sum(\n", - " keras.losses.binary_crossentropy(data, reconstruction), axis = (1, 2)\n", - " )\n", - " ) / n_features\n", - " kl_loss = -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var))\n", - " kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis = 1)) / n_features\n", - " total_loss = (reconstruction_loss + kl_loss)\n", - " grads = tape.gradient(total_loss, self.trainable_weights)\n", - " self.optimizer.apply_gradients(zip(grads, self.trainable_weights))\n", - " self.total_loss_tracker.update_state(total_loss)\n", - " self.reconstruction_loss_tracker.update_state(reconstruction_loss)\n", - " self.kl_loss_tracker.update_state(kl_loss)\n", - " \n", - " return {\n", - " \"loss\": self.total_loss_tracker.result(),\n", - " \"reconstruction_loss\": self.reconstruction_loss_tracker.result(),\n", - " \"kl_loss\": self.kl_loss_tracker.result(),\n", - " }\n", - "\n", - " # Needed to validate (validation loss) and to evaluate\n", - " def test_step(self, data):\n", - " if type(data) == tuple:\n", - " data, _ = data\n", - " \n", - " z_mean, z_log_var, z = self.encoder(data)\n", - " reconstruction = self.decoder(z)\n", - " # FIXME: Normalize loss with the number of features (28 * 28)\n", - " n_features = 28 * 28\n", - " reconstruction_loss = tf.reduce_mean(\n", - " tf.reduce_sum(\n", - " keras.losses.binary_crossentropy(data, reconstruction), axis = (1, 2)\n", - " )\n", - " ) / n_features\n", - " kl_loss = -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var))\n", - " kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis = 1)) / n_features\n", - " total_loss = (reconstruction_loss + kl_loss)\n", - " # grads = tape.gradient(total_loss, self.trainable_weights)\n", - " # self.optimizer.apply_gradients(zip(grads, self.trainable_weights))\n", - " self.total_loss_tracker.update_state(total_loss)\n", - " self.reconstruction_loss_tracker.update_state(reconstruction_loss)\n", - " self.kl_loss_tracker.update_state(kl_loss)\n", - " \n", - " return {\n", - " \"loss\": self.total_loss_tracker.result(),\n", - " \"reconstruction_loss\": self.reconstruction_loss_tracker.result(),\n", - " \"kl_loss\": self.kl_loss_tracker.result(),\n", - " }" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Helper Functions:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Only works if latent_dim = 2\n", - "def plot_latent_space(vae, n = 30, figsize = 15, show = False, path = ''):\n", - " # display a n*n 2D manifold of digits\n", - " digit_size = 28\n", - " scale = 1.0\n", - " figure = np.zeros((digit_size * n, digit_size * n))\n", - " # linearly spaced coordinates corresponding to the 2D plot\n", - " # of digit classes in the latent space\n", - " grid_x = np.linspace(-scale, scale, n)\n", - " grid_y = np.linspace(-scale, scale, n)[::-1]\n", - "\n", - " for i, yi in enumerate(grid_y):\n", - " for j, xi in enumerate(grid_x):\n", - " z_sample = np.array([[xi, yi]])\n", - " x_decoded = vae.decoder.predict(z_sample)\n", - " digit = x_decoded[0].reshape(digit_size, digit_size)\n", - " figure[\n", - " i * digit_size : (i + 1) * digit_size,\n", - " j * digit_size : (j + 1) * digit_size,\n", - " ] = digit\n", - "\n", - " plt.figure(figsize=(figsize, figsize))\n", - " start_range = digit_size // 2\n", - " end_range = n * digit_size + start_range\n", - " pixel_range = np.arange(start_range, end_range, digit_size)\n", - " sample_range_x = np.round(grid_x, 1)\n", - " sample_range_y = np.round(grid_y, 1)\n", - " plt.xticks(pixel_range, sample_range_x)\n", - " plt.yticks(pixel_range, sample_range_y)\n", - " plt.xlabel(\"z[0]\")\n", - " plt.ylabel(\"z[1]\")\n", - " plt.imshow(figure, cmap=\"Greys_r\")\n", - " if show:\n", - " plt.show()\n", - " if path:\n", - " plt.savefig(path)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def plot_images(images, rows, columns, show = False, path = ''):\n", - " fig = plt.figure(figsize = (10, 7))\n", - " \n", - " for i, image in enumerate(images):\n", - " fig.add_subplot(rows, columns, i+1)\n", - " plt.imshow(image, cmap = 'binary')\n", - " plt.axis('off')\n", - "\n", - " if show:\n", - " plt.show()\n", - " if path:\n", - " plt.savefig(path)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def train_model(X_train, X_test, X_valid, date):\n", - " early_stopping_cb = keras.callbacks.EarlyStopping(patience = 5, restore_best_weights = True) # stops training early if the validation loss does not improve\n", - "\n", - " if os.path.exists(os.path.join(model_dir, 'vae.weights.h5')): # if the model has already been trained at least once, load that model\n", - " vae.load_weights(os.path.join(model_dir, 'vae.weights.h5'))\n", - " reloaded = tf.saved_model.load(model_dir)\n", - "\n", - " history = vae.fit(\n", - " X_train, epochs = 50, batch_size = 40,\n", - " callbacks = [early_stopping_cb],\n", - " validation_data = (X_valid,)\n", - " )\n", - "\n", - " vae.save_weights(os.path.join(model_dir, 'vae.weights.h5')) # save model weights after training\n", - " tf.saved_model.save(vae, model_dir)\n", - "\n", - " hist_pd = pd.DataFrame(history.history)\n", - " hist_pd.to_csv(os.path.join(model_dir, f'history_{date}.csv'), index = False)\n", - "\n", - " test_loss = vae.evaluate(X_test)\n", - " test_loss = dict(zip([\"loss\", \"reconstruction_loss\", \"kl_loss\"], test_loss))\n", - "\n", - " print('Test loss:', test_loss)\n", - "\n", - " with open(os.path.join(model_dir, f'test_loss_{date}.json'), 'w') as json_file:\n", - " json.dump(test_loss, json_file, indent = 4)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Train the VAE" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "print(\"Memory usage before computation:\", tf.config.experimental.get_memory_info('GPU:0'))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "latent_dim = 2\n", - "train = True\n", - "model_dir = './model_dir'\n", - "\n", - "os.makedirs(model_dir, exist_ok = True)\n", - "\n", - "# build encoder\n", - "encoder = build_encoder(latent_dim)\n", - "print(\"Memory usage after building encoder:\", tf.config.experimental.get_memory_info('GPU:0'))\n", - "\n", - "# build decoder\n", - "decoder = build_decoder(latent_dim)\n", - "print(\"Memory usage after building decoder:\", tf.config.experimental.get_memory_info('GPU:0'))\n", - "\n", - "# build VAE (variational autoencoder)\n", - "vae = VAE(encoder, decoder)\n", - "vae.compile(optimizer = 'rmsprop') \n", - "# vae.compile(optimizer = keras.optimizers.Adam())\n", - "print(\"Memory usage after building VAE:\", tf.config.experimental.get_memory_info('GPU:0'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load and Preprocess Training Data:\n", - "The standard way of manipulating arrays in Conv2D layers in TF is to use arrays in the shape:\n", - "`batch_size, height, width, channels = data.shape`\n", - "In our case, the the `batch_size` is the number of image frames (i.e. separate samples or rows in a `.csv` file), the `height` and `width` define the size of the image frame in number of pixels, and the `channels` are the number of layers in the frames. Typically, channels are color layers (e.g. RGB or CMYK) but in our case, we could use different metereological variables. However, for this first experiment, **we only need one channel** because we're only going to use mean sea level pressure (msl)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# used MNIST data preproc as template for this definition\n", - "def load_data(): \n", - " files = os.listdir(data_dir)\n", - " files = [f for f in files if '.nc' in f]\n", - " \n", - " all_data = np.expand_dims(\n", - " np.concatenate(\n", - " [netCDF4.Dataset(data_dir + converted_file)['msl'][:] for converted_file in files]\n", - " ),\n", - " -1\n", - " ).astype(\"float32\") / 110000\n", - " return all_data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run the Model:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def run_train(num_files, date):\n", - " slp = load_data() # load data\n", - " print(\"shape:\", np.shape(slp)) # verify data shape\n", - " \n", - " # split the data - y values are throw away\n", - " X_train, X_test, y_train, y_test = train_test_split(slp[0:(num_files * 80 - 1), :, :, :], np.arange(0, num_files * 80 - 1), test_size = 0.2, random_state = 1)\n", - " X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size = 0.25, random_state = 1) # 0.25 x 0.8 = 0.2\n", - "\n", - " train_model(X_train, X_test, X_valid, date)\n", - " remove_data()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "parameters" - ] - }, - "outputs": [], - "source": [ - "# parameter cell for pm\n", - "year = \"2018\"\n", - "month = \"01\"\n", - "day = \"01\"\n", - "count = 1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "ensembles = [\"c00\", \"p01\", \"p02\", \"p03\", \"p04\"]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# run training\n", - "num_files = get_data(year, month, day, ensembles)\n", - "!csh batch_grib2nc.csh\n", - "\n", - "date = year + month + day\n", - "run_train(num_files, date)\n", - " \n", - "print(\"Memory usage after training:\", tf.config.experimental.get_memory_info('GPU:0'))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# if latent_dim == 2:\n", - "# plot_latent_space(vae, path = os.path.join(model_dir, 'latent_space.png'))\n", - "\n", - "# # Generating new images\n", - "# codings = tf.random.normal(shape = [12, latent_dim])\n", - "# images = vae.decoder(codings).numpy()\n", - "# plot_images(images, 3, 4, path = os.path.join(model_dir, 'generated.png'))\n", - "\n", - "# # Semantic interpolation\n", - "# codings_grid = tf.reshape(codings, [1, 3, 4, latent_dim])\n", - "# larger_grid = tf.image.resize(codings_grid, size = [5, 7])\n", - "# interpolated_codings = tf.reshape(larger_grid, [-1, latent_dim])\n", - "# images = vae.decoder(interpolated_codings).numpy()\n", - "# plot_images(images, 5, 7, path = os.path.join(model_dir, 'interpolated.png'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Display a grid of sampled digits" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# plot_latent_space(vae)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Display how the latent space clusters different digit classes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# def plot_label_clusters(vae, data, labels):\n", - "# # display a 2D plot of the digit classes in the latent space\n", - "# z_mean, _, _ = vae.encoder.predict(data)\n", - "# plt.figure(figsize=(12, 10))\n", - "# plt.scatter(z_mean[:, 0], z_mean[:, 1], c=labels)\n", - "# plt.colorbar()\n", - "# plt.xlabel(\"z[0]\")\n", - "# plt.ylabel(\"z[1]\")\n", - "# plt.show()\n", - "\n", - "\n", - "# (x_train, y_train), _ = keras.datasets.mnist.load_data()\n", - "# x_train = np.expand_dims(x_train, -1).astype(\"float32\") / 255\n", - "\n", - "# plot_label_clusters(vae, x_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# Original code\n", - "CVAE example from [Keras.io](https://keras.io/examples/generative/vae/), [github repo](https://github.com/keras-team/keras-io/blob/master/examples/generative/vae.py).\n", - "\n", - "```python\n", - "\"\"\"\n", - "Title: Variational AutoEncoder\n", - "Author: [fchollet](https://twitter.com/fchollet)\n", - "Date created: 2020/05/03\n", - "Last modified: 2020/05/03\n", - "Description: Convolutional Variational AutoEncoder (VAE) trained on MNIST digits.\n", - "\"\"\"\n", - "\n", - "\"\"\"\n", - "## Setup\n", - "\"\"\"\n", - "\n", - "import numpy as np\n", - "import tensorflow as tf\n", - "from tensorflow import keras\n", - "from tensorflow.keras import layers\n", - "\n", - "\"\"\"\n", - "## Create a sampling layer\n", - "\"\"\"\n", - "\n", - "\n", - "class Sampling(layers.Layer):\n", - " \"\"\"Uses (z_mean, z_log_var) to sample z, the vector encoding a digit.\"\"\"\n", - "\n", - " def call(self, inputs):\n", - " z_mean, z_log_var = inputs\n", - " batch = tf.shape(z_mean)[0]\n", - " dim = tf.shape(z_mean)[1]\n", - " epsilon = tf.keras.backend.random_normal(shape=(batch, dim))\n", - " return z_mean + tf.exp(0.5 * z_log_var) * epsilon\n", - "\n", - "\n", - "\"\"\"\n", - "## Build the encoder\n", - "\"\"\"\n", - "\n", - "latent_dim = 2\n", - "\n", - "encoder_inputs = keras.Input(shape=(28, 28, 1))\n", - "x = layers.Conv2D(32, 3, activation=\"relu\", strides=2, padding=\"same\")(encoder_inputs)\n", - "x = layers.Conv2D(64, 3, activation=\"relu\", strides=2, padding=\"same\")(x)\n", - "x = layers.Flatten()(x)\n", - "x = layers.Dense(16, activation=\"relu\")(x)\n", - "z_mean = layers.Dense(latent_dim, name=\"z_mean\")(x)\n", - "z_log_var = layers.Dense(latent_dim, name=\"z_log_var\")(x)\n", - "z = Sampling()([z_mean, z_log_var])\n", - "encoder = keras.Model(encoder_inputs, [z_mean, z_log_var, z], name=\"encoder\")\n", - "encoder.summary()\n", - "\n", - "\"\"\"\n", - "## Build the decoder\n", - "\"\"\"\n", - "\n", - "latent_inputs = keras.Input(shape=(latent_dim,))\n", - "x = layers.Dense(7 * 7 * 64, activation=\"relu\")(latent_inputs)\n", - "x = layers.Reshape((7, 7, 64))(x)\n", - "x = layers.Conv2DTranspose(64, 3, activation=\"relu\", strides=2, padding=\"same\")(x)\n", - "x = layers.Conv2DTranspose(32, 3, activation=\"relu\", strides=2, padding=\"same\")(x)\n", - "decoder_outputs = layers.Conv2DTranspose(1, 3, activation=\"sigmoid\", padding=\"same\")(x)\n", - "decoder = keras.Model(latent_inputs, decoder_outputs, name=\"decoder\")\n", - "decoder.summary()\n", - "\n", - "\"\"\"\n", - "## Define the VAE as a `Model` with a custom `train_step`\n", - "\"\"\"\n", - "\n", - "\n", - "class VAE(keras.Model):\n", - " def __init__(self, encoder, decoder, **kwargs):\n", - " super(VAE, self).__init__(**kwargs)\n", - " self.encoder = encoder\n", - " self.decoder = decoder\n", - " self.total_loss_tracker = keras.metrics.Mean(name=\"total_loss\")\n", - " self.reconstruction_loss_tracker = keras.metrics.Mean(\n", - " name=\"reconstruction_loss\"\n", - " )\n", - " self.kl_loss_tracker = keras.metrics.Mean(name=\"kl_loss\")\n", - "\n", - " @property\n", - " def metrics(self):\n", - " return [\n", - " self.total_loss_tracker,\n", - " self.reconstruction_loss_tracker,\n", - " self.kl_loss_tracker,\n", - " ]\n", - "\n", - " def train_step(self, data):\n", - " with tf.GradientTape() as tape:\n", - " z_mean, z_log_var, z = self.encoder(data)\n", - " reconstruction = self.decoder(z)\n", - " reconstruction_loss = tf.reduce_mean(\n", - " tf.reduce_sum(\n", - " keras.losses.binary_crossentropy(data, reconstruction), axis=(1, 2)\n", - " )\n", - " )\n", - " kl_loss = -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var))\n", - " kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis=1))\n", - " total_loss = reconstruction_loss + kl_loss\n", - " grads = tape.gradient(total_loss, self.trainable_weights)\n", - " self.optimizer.apply_gradients(zip(grads, self.trainable_weights))\n", - " self.total_loss_tracker.update_state(total_loss)\n", - " self.reconstruction_loss_tracker.update_state(reconstruction_loss)\n", - " self.kl_loss_tracker.update_state(kl_loss)\n", - " return {\n", - " \"loss\": self.total_loss_tracker.result(),\n", - " \"reconstruction_loss\": self.reconstruction_loss_tracker.result(),\n", - " \"kl_loss\": self.kl_loss_tracker.result(),\n", - " }\n", - "\n", - "\n", - "\"\"\"\n", - "## Train the VAE\n", - "\"\"\"\n", - "\n", - "(x_train, _), (x_test, _) = keras.datasets.mnist.load_data()\n", - "mnist_digits = np.concatenate([x_train, x_test], axis=0)\n", - "mnist_digits = np.expand_dims(mnist_digits, -1).astype(\"float32\") / 255\n", - "\n", - "vae = VAE(encoder, decoder)\n", - "vae.compile(optimizer=keras.optimizers.Adam())\n", - "vae.fit(mnist_digits, epochs=30, batch_size=128)\n", - "\n", - "\"\"\"\n", - "## Display a grid of sampled digits\n", - "\"\"\"\n", - "\n", - "import matplotlib.pyplot as plt\n", - "\n", - "\n", - "def plot_latent_space(vae, n=30, figsize=15):\n", - " # display a n*n 2D manifold of digits\n", - " digit_size = 28\n", - " scale = 1.0\n", - " figure = np.zeros((digit_size * n, digit_size * n))\n", - " # linearly spaced coordinates corresponding to the 2D plot\n", - " # of digit classes in the latent space\n", - " grid_x = np.linspace(-scale, scale, n)\n", - " grid_y = np.linspace(-scale, scale, n)[::-1]\n", - "\n", - " for i, yi in enumerate(grid_y):\n", - " for j, xi in enumerate(grid_x):\n", - " z_sample = np.array([[xi, yi]])\n", - " x_decoded = vae.decoder.predict(z_sample)\n", - " digit = x_decoded[0].reshape(digit_size, digit_size)\n", - " figure[\n", - " i * digit_size : (i + 1) * digit_size,\n", - " j * digit_size : (j + 1) * digit_size,\n", - " ] = digit\n", - "\n", - " plt.figure(figsize=(figsize, figsize))\n", - " start_range = digit_size // 2\n", - " end_range = n * digit_size + start_range\n", - " pixel_range = np.arange(start_range, end_range, digit_size)\n", - " sample_range_x = np.round(grid_x, 1)\n", - " sample_range_y = np.round(grid_y, 1)\n", - " plt.xticks(pixel_range, sample_range_x)\n", - " plt.yticks(pixel_range, sample_range_y)\n", - " plt.xlabel(\"z[0]\")\n", - " plt.ylabel(\"z[1]\")\n", - " plt.imshow(figure, cmap=\"Greys_r\")\n", - " plt.show()\n", - "\n", - "\n", - "plot_latent_space(vae)\n", - "\n", - "\"\"\"\n", - "## Display how the latent space clusters different digit classes\n", - "\"\"\"\n", - "\n", - "\n", - "def plot_label_clusters(vae, data, labels):\n", - " # display a 2D plot of the digit classes in the latent space\n", - " z_mean, _, _ = vae.encoder.predict(data)\n", - " plt.figure(figsize=(12, 10))\n", - " plt.scatter(z_mean[:, 0], z_mean[:, 1], c=labels)\n", - " plt.colorbar()\n", - " plt.xlabel(\"z[0]\")\n", - " plt.ylabel(\"z[1]\")\n", - " plt.show()\n", - "\n", - "\n", - "(x_train, y_train), _ = keras.datasets.mnist.load_data()\n", - "x_train = np.expand_dims(x_train, -1).astype(\"float32\") / 255\n", - "\n", - "plot_label_clusters(vae, x_train, y_train)\n", - "```" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:cvae_env]", - "language": "python", - "name": "conda-env-cvae_env-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/run_on_cluster/cvae-weather-ensemble/README.md b/run_on_cluster/cvae-weather-ensemble/README.md index b24348a..3782c3b 100644 --- a/run_on_cluster/cvae-weather-ensemble/README.md +++ b/run_on_cluster/cvae-weather-ensemble/README.md @@ -1,98 +1,66 @@ -# VM setup -1. Testing whether I can use a lower cost VM (n1-standard-4) to `conda install` and pre-download data and save data on image and then reboot image as a higher cost (a2-highgpu-1g) for the actual training. This seems to work BUT may require reinstalling Linux headers when going from low->high cost and then rebooting the VM, e.g.: -```bash -# Booted as a2-highgpu-1g after run as n1-standard-4 -> error message. -sudo apt-get install linux-headers-4.19.0-18-cloud-amd64 -reboot -``` -See `/var/log/apt/history.log` for history of install requests (includes the tcsh install in #4 below). Also, note that while it is possible to switch between n1-standards and a1-highgpu, you **cannot** switch between e1 class machines and a1 machines. - -2. Testing whether I can use pygrib as a direct load from grib -> YES, but need to manually add time axis and concatenate time steps within files in addition to concatenating time steps between files. See load_many_grib in main_cvae.py. - -3. Using the c1-deeplearning-tf-2-5-cu110-v20210714-debian-10 image as a starting point. It has conda and I will attempt to install default pygrib and parsl into the base conda environment. It appears that all the other key packages are already installed (numpy, pandas, matplotlib, json, os, scikit-learn, tensorflow, pickle, glob). - -4. Actual steps executed on an n1-standard-4: -```bash -conda install -c conda-forge pygrib # Could not solve env before package download but installed anyway. -conda install -c conda-forge parsl # Installed without issue (downgrading sqlalchemy) -sudo apt-get install tcsh # Used for some of the batch scripts in this dir, could be bypassed. -``` - -5. Testing data loading: -```python -import numpy as np -import pygrib -file_name = "./gefs_data/pres_msl_2019110100_c00.grib2" -file_data = pygrib.open(file_name) -msl = file_data.select(name='Mean sea level pressure') -data = msl[0].values -np.shape(data) -(721, 1440) -``` +# CVAE Weather Ensemble Model -6. Testing the use of smaller data types. Please see [this blog](https://towardsdatascience.com/memory-efficient-data-science-types-53423d48ba1d) for a great intro to which data types are supported and how. In a nutshell, float16 is dangerous to use because it has inconsistent support. Float64 is the default numpy type, float32 should work easily with `data.astype(np.float32)`. The most commonly supported ints are int8 and int32, but there is no mention of the reliability of int16, which is what I want to use. +This is the CVAE Weather Ensemble Model repository. The model leverages the power of Conditional Variational Autoencoders (CVAE) to provide an ensemble training system for weather predictions while also answering the big data problem. Its capability for online training allows for continuous learning from new data streams without compromising performance. That is, data used to train the model is processed in small batches, the model being retrained with each new batch. -# Data sizes: +## Key Features -1. Each GEFS 2D field with 80 time steps (10 days at 3 hours) is 57MB in grib2 -2. Converted to netcdf, the field with 80 time steps is 317MB -3. A whole year in NetCDF is 12 months x 3 fields/month * 317MB = 11GB -4. Converted to pkl, each file is 634MB x 36 = 22GB -5. We need to figure out a robust and efficient way to go from grib2 to load to memory. Currently, this multistage approach is slow and takes up a lot of space. +* **Conditional Variational Autoencoder (CVAE):** Utilizes CVAE with Conv2d networks to model complex dependencies in weather data, enabling accurate probabilistic forecasts. +* **Online Training:** Implements a dynamic training approach that can adapt to real-time incoming data, ensuring up-to-date forecasts. +* **Data Version Control (DVC):** Manages datasets and model versions effectively with DVC, facilitating reproducibility and collaboration. -# Pipeline summary: +## Getting Started -1. wget to download from NOAA's S3 bucket -2. cdo to convert to nc (`apt-get install cdo`) -3. pyferret to load as numpy array (`conda install -y -c conda-forge pyferret`) -4. save as pkl for use outside of pyferret container b/c pyferret will not install in Conda (like pynio and cfgrib) +To get started with the CVAE Weather Ensemble Model, follow the instructions below to set up your environment. -This means ignore `batch_grib2pkl.csh` because pynio/cfgrib don't work reliably. +### 1. Conda env setup +*The following is the list steps needed to be taken in order to set up a correct conda environment for running this notebook.* -# Tensorflow: - -1. docker pull tensorflow/tensorflow -2. Run this container, e.g. sudo docker run -it -v/home/sfgary/sbir-learnerworks-doc/NOAA_JTTI:/tmp/noaa tensorflow:tensorflow /bin/bash -3. pip install pandas -4. pip install scikit-learn -5. pip install matplotlib - -# Grids +**Create Environment:** +```bash +source ~/pw/.miniconda3c/etc/profile.d/conda.sh +conda create --name python=3.9 +conda activate +``` -Since we have broken away from grib/netcdf, we need to explicitly -bring back the grid information. Here, I used ncdump -h on the lon -and lat variables in .nc files, manually removed the header, and -then used the following text pipeline to make into a column vector -in a text file: +**Install Pacakages:** -cat lon.txt | tr -d '}' | tr -d ';' | tr -d '\n' | tr -d ' ' | tr ',' '\n' > lon.x -cat lat.txt | tr -d '}' | tr -d ';' | tr -d '\n' | tr -d ' ' | tr ',' '\n' > lat.y +The following packages need to be installed on top of a typical base Conda env. Install the packages in the following order so the environment solves correctly: +```bash +conda install -y -c conda-forge tensorflow +conda install -y -c conda-forge netCDF4 # For reading nc files +conda install -y -c conda-forge cartopy # For making maps +conda install -y -c conda-forge matplotlib +conda install -y -c conda-forge pandas +conda install -y -c conda-forge scikit-learn +conda install -y -c conda-forge papermill # For running online training +conda install -y -c conda-forge dvc # For dvc +conda install -y -c conda-forge nco +conda install -y -c conda-forge cdo # For converting grib to nc +``` +**Connect Notebook to Environment:** +```bash +conda install -y ipykernel +conda install -y requests +conda install -y -c anaconda jinja2 +python -m ipykernel install --user --name= --display-name "Python ()" +``` -# Training the ML model +*`conda env update --file cvae_env.yaml --name ` can also be used to recreate the working conda environment used to build this model. After this executes, run the following:* +* *`source ~/pw/.miniconda3c/etc/profile.d/conda.sh`* +* *`conda activate `* +* *`python -m ipykernel install --user --name= --display-name "Python ()"`* -For two years of data (36 files each year, each file has 80 3h time steps = 10 days) -(Used 2018 and 2019) +### 2. Open the `cvae_runner_and_example.ipynb`, `cvae_training.ipynb`, and `cvae_log.ipynb` notebooks -Input summary: -(5760, 721, 1440, 1) -Mean: 0.9185315259371455 -Std: 0.012157332330270077 -Testing shape -(1440, 721, 1440, 1) -Training shape -(3240, 721, 1440, 1) -Validaiton shape -(1080, 721, 1440, 1) +When opening the notbooks make sure to set each of their kernel to the one just created. Save the notebook after this is done. You can also close out of the `log` and `training` notebooks if wanted. Note: in order to see an updated log when running the online trainging, the `log` notebook must be closed and opened again throughout the session. -Takes about 10 mins per epoch, uses ~128GB RAM, linear scaling wrt batch size seems -confirmed wrt previous runs. Running on 32CPU, all utilized but at around 50%. +### 3. Continue to `cvae_runner_and_example.ipynb` and follow the instructions in the notebook -Training time: -Start: 2021-11-14 15:16:14 -End: +## Needed Specifications +**GPU & Memory:** The program requires at least 32 G of RAM.\ +**Storage:** Mounted stoarge is required for dvc to run properly.\ +**GitHub:** A git repo that can be SSH into is also needed for dvc to run properly. SSH connection must be established on the terminal. -# Using the ML model +## -The model weights are in ./gefs/vae.data-00000-of-000001. The Python/Tensorflow -code that defines the model needs to be loaded first and then the weights are -loaded. \ No newline at end of file +*This model is based on an [example](https://keras.io/examples/generative/vae/) from Keras.io by fchollet ([GitHub](https://github.com/keras-team/keras-io/blob/master/examples/generative/vae.py))* diff --git a/run_on_cluster/cvae-weather-ensemble/__pycache__/cvae.cpython-39.pyc b/run_on_cluster/cvae-weather-ensemble/__pycache__/cvae.cpython-39.pyc new file mode 100644 index 0000000..db07c9b Binary files /dev/null and b/run_on_cluster/cvae-weather-ensemble/__pycache__/cvae.cpython-39.pyc differ diff --git a/run_on_cluster/cvae-weather-ensemble/coordinates/lat.y b/run_on_cluster/cvae-weather-ensemble/coordinates/lat.y new file mode 100644 index 0000000..d26fdeb --- /dev/null +++ b/run_on_cluster/cvae-weather-ensemble/coordinates/lat.y @@ -0,0 +1,721 @@ +90 +89.75 +89.5 +89.25 +89 +88.75 +88.5 +88.25 +88 +87.75 +87.5 +87.25 +87 +86.75 +86.5 +86.25 +86 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"79ae6efb-c776-4f57-853a-a7123c612918", + "metadata": {}, + "source": [ + "# Variational AutoEncoder example\n", + "This notebook is a copy of [Francois Chollet's example](https://keras.io/examples/generative/vae/) but modified for online learning. In particular, some goals are:\n", + "1. test computational performance scaling (CPUs, GPUs)\n", + "2. test model performance while saving and fitting again (in particular, adding more data instead of having all the data available at once).\n", + "3. integrations with DVC\n", + "\n", + "## Environment set up\n", + "Based on the [Keras](https://keras.io/getting_started/) and [Tensorflow](https://www.tensorflow.org/install) instructions. We need Keras 3+ to be able to run this example. A simple `conda install tensorflow` will install TF 2.12 and Keras 2. I also tried using Python 3.11, but it appears that there may be some issues with underlying C libraries, so use Python 3.9:\n", + "+ `conda create --name tf-cpu-py39 python=3.9`\n", + "\n", + "The following are required for connecting the kernel to a notebook:\n", + "+ `conda install -q -y requests`\n", + "+ `conda install -q -y ipykernel`\n", + "+ `conda install -q -y -c anaconda jinja2`\n", + "+ `conda install -q -y pandas matplotlib`\n", + "\n", + "Ensure pip is up to date, then install TF:\n", + "+ `pip install --upgrade pip`\n", + "+ `pip install tensorflow`\n", + "+ `pip install tensorboard-plugin-profile`\n", + "\n", + "## Performance results\n", + "\n", + "4 CPUs -> 40 ms/step, ~22s per epoch\n", + "\n", + "8 CPUs -> 24 ms/step, ~13s per epoch\n", + "\n", + "T4 GPU -> ???" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "4dcc2c80-8eb8-46d0-888c-b70d26e71f61", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ[\"KERAS_BACKEND\"] = \"tensorflow\"\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import tensorflow as tf\n", + "import keras\n", + "from keras import ops\n", + "from keras import layers" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "19dea6e9-69e5-4cee-a546-a9db7792978b", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "model_dir = './model_d'\n", + "os.makedirs(model_dir, exist_ok = True)" + ] + }, + { + "cell_type": "markdown", + "id": "c9583330-6383-440f-acc5-ce631cf9e27a", + "metadata": {}, + "source": [ + "## Create sampling layer" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "013491ac-f39f-44bb-9883-7a6cfcac1f2c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "class Sampling(layers.Layer):\n", + " \"\"\"Uses (z_mean, z_log_var) to sample z, the vector encoding a digit.\"\"\"\n", + "\n", + " def __init__(self, **kwargs):\n", + " super().__init__(**kwargs)\n", + " self.seed_generator = keras.random.SeedGenerator(1337)\n", + "\n", + " def call(self, inputs):\n", + " z_mean, z_log_var = inputs\n", + " batch = ops.shape(z_mean)[0]\n", + " dim = ops.shape(z_mean)[1]\n", + " epsilon = keras.random.normal(shape=(batch, dim), seed=self.seed_generator)\n", + " return z_mean + ops.exp(0.5 * z_log_var) * epsilon" + ] + }, + { + "cell_type": "markdown", + "id": "de4536fc-e359-49fb-bc52-54b9ff244d46", + "metadata": { + "tags": [] + }, + "source": [ + "## Build the encoder" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "bc70b33c-f2bd-41fb-83f4-90ac1c4afef7", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Model: \"encoder\"\n",
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┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)        ┃ Output Shape      ┃    Param # ┃ Connected to      ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
+       "│ input_layer_2       │ (None, 28, 28, 1) │          0 │ -                 │\n",
+       "│ (InputLayer)        │                   │            │                   │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ conv2d_2 (Conv2D)   │ (None, 14, 14,    │        320 │ input_layer_2[0]… │\n",
+       "│                     │ 32)               │            │                   │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ conv2d_3 (Conv2D)   │ (None, 7, 7, 64)  │     18,496 │ conv2d_2[0][0]    │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ flatten_1 (Flatten) │ (None, 3136)      │          0 │ conv2d_3[0][0]    │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ dense_2 (Dense)     │ (None, 16)        │     50,192 │ flatten_1[0][0]   │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ z_mean (Dense)      │ (None, 2)         │         34 │ dense_2[0][0]     │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ z_log_var (Dense)   │ (None, 2)         │         34 │ dense_2[0][0]     │\n",
+       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
+       "│ sampling_1          │ (None, 2)         │          0 │ z_mean[0][0],     │\n",
+       "│ (Sampling)          │                   │            │ z_log_var[0][0]   │\n",
+       "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
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Model: \"decoder\"\n",
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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ input_layer_3 (InputLayer)      │ (None, 2)              │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_3 (Dense)                 │ (None, 3136)           │         9,408 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ reshape_1 (Reshape)             │ (None, 7, 7, 64)       │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_transpose_3              │ (None, 14, 14, 64)     │        36,928 │\n",
+       "│ (Conv2DTranspose)               │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_transpose_4              │ (None, 28, 28, 32)     │        18,464 │\n",
+       "│ (Conv2DTranspose)               │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_transpose_5              │ (None, 28, 28, 1)      │           289 │\n",
+       "│ (Conv2DTranspose)               │                        │               │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
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+       "
\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "latent_inputs = keras.Input(shape=(latent_dim,))\n", + "x = layers.Dense(7 * 7 * 64, activation=\"relu\")(latent_inputs)\n", + "x = layers.Reshape((7, 7, 64))(x)\n", + "x = layers.Conv2DTranspose(64, 3, activation=\"relu\", strides=2, padding=\"same\")(x)\n", + "x = layers.Conv2DTranspose(32, 3, activation=\"relu\", strides=2, padding=\"same\")(x)\n", + "decoder_outputs = layers.Conv2DTranspose(1, 3, activation=\"sigmoid\", padding=\"same\")(x)\n", + "decoder = keras.Model(latent_inputs, decoder_outputs, name=\"decoder\")\n", + "decoder.summary()" + ] + }, + { + "cell_type": "markdown", + "id": "85405b6c-a092-49ad-9358-5e0c38622c59", + "metadata": {}, + "source": [ + "## Define the VAE as a `Model` with a custom `train_step`" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "9d2ef055-a698-4458-8b30-8671d3975195", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "class VAE(keras.Model):\n", + " def __init__(self, encoder, decoder, **kwargs):\n", + " super().__init__(**kwargs)\n", + " self.encoder = encoder\n", + " self.decoder = decoder\n", + " self.total_loss_tracker = keras.metrics.Mean(name=\"total_loss\")\n", + " self.reconstruction_loss_tracker = keras.metrics.Mean(\n", + " name=\"reconstruction_loss\"\n", + " )\n", + " self.kl_loss_tracker = keras.metrics.Mean(name=\"kl_loss\")\n", + "\n", + " @property\n", + " def metrics(self):\n", + " return [\n", + " self.total_loss_tracker,\n", + " self.reconstruction_loss_tracker,\n", + " self.kl_loss_tracker,\n", + " ]\n", + "\n", + " def train_step(self, data):\n", + " with tf.GradientTape() as tape:\n", + " z_mean, z_log_var, z = self.encoder(data)\n", + " reconstruction = self.decoder(z)\n", + " reconstruction_loss = ops.mean(\n", + " ops.sum(\n", + " keras.losses.binary_crossentropy(data, reconstruction),\n", + " axis=(1, 2),\n", + " )\n", + " )\n", + " kl_loss = -0.5 * (1 + z_log_var - ops.square(z_mean) - ops.exp(z_log_var))\n", + " kl_loss = ops.mean(ops.sum(kl_loss, axis=1))\n", + " total_loss = reconstruction_loss + kl_loss\n", + " grads = tape.gradient(total_loss, self.trainable_weights)\n", + " self.optimizer.apply_gradients(zip(grads, self.trainable_weights))\n", + " self.total_loss_tracker.update_state(total_loss)\n", + " self.reconstruction_loss_tracker.update_state(reconstruction_loss)\n", + " self.kl_loss_tracker.update_state(kl_loss)\n", + " return {\n", + " \"loss\": self.total_loss_tracker.result(),\n", + " \"reconstruction_loss\": self.reconstruction_loss_tracker.result(),\n", + " \"kl_loss\": self.kl_loss_tracker.result(),\n", + " }" + ] + }, + { + "cell_type": "markdown", + "id": "a3f662b7-a8c8-4ae4-9f60-b6de69e0ddef", + "metadata": {}, + "source": [ + "## Train the VAE" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "79963607-9800-4363-b0b0-280593559307", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/30\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "I0000 00:00:1720618381.795883 32629 service.cc:145] XLA service 0x148bd8011380 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", + "I0000 00:00:1720618381.796498 32629 service.cc:153] StreamExecutor device (0): NVIDIA A10G, Compute Capability 8.6\n", + "2024-07-10 13:33:01.850597: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n", + "2024-07-10 13:33:02.127441: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:465] Loaded cuDNN version 8907\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m 60/547\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - kl_loss: 3.1316 - loss: 425.5349 - reconstruction_loss: 422.4033" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "I0000 00:00:1720618387.474749 32629 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 13ms/step - kl_loss: 3.5617 - loss: 255.2414 - reconstruction_loss: 251.6797\n", + "Epoch 2/30\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - kl_loss: 5.0034 - loss: 169.6936 - reconstruction_loss: 164.6902\n", + "Epoch 3/30\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - kl_loss: 5.2097 - loss: 162.7446 - reconstruction_loss: 157.5348\n", + "Epoch 4/30\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - kl_loss: 5.4643 - loss: 159.5264 - reconstruction_loss: 154.0621\n", + "Epoch 5/30\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - kl_loss: 5.5665 - loss: 157.9381 - reconstruction_loss: 152.3717\n", + "Epoch 6/30\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - kl_loss: 5.6762 - loss: 156.2339 - reconstruction_loss: 150.5576\n", + "Epoch 7/30\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - kl_loss: 5.7715 - loss: 155.3824 - reconstruction_loss: 149.6108\n", + "Epoch 8/30\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - kl_loss: 5.8016 - loss: 154.6804 - reconstruction_loss: 148.8788\n", + "Epoch 9/30\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - kl_loss: 5.8492 - loss: 154.1813 - reconstruction_loss: 148.3321\n", + "Epoch 10/30\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - kl_loss: 5.9050 - loss: 153.6074 - reconstruction_loss: 147.7023\n", + "Epoch 11/30\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - kl_loss: 5.9285 - loss: 152.8266 - reconstruction_loss: 146.8981\n", + "Epoch 12/30\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - kl_loss: 5.9797 - loss: 152.7235 - reconstruction_loss: 146.7438\n", + "Epoch 13/30\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - kl_loss: 6.0067 - loss: 152.5982 - reconstruction_loss: 146.5916\n", + "Epoch 14/30\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - kl_loss: 6.0405 - loss: 151.8119 - reconstruction_loss: 145.7714\n", + "Epoch 15/30\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - kl_loss: 6.0761 - loss: 151.6286 - reconstruction_loss: 145.5525\n", + "Epoch 16/30\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - kl_loss: 6.0581 - loss: 151.1353 - reconstruction_loss: 145.0772\n", + "Epoch 17/30\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - kl_loss: 6.1340 - loss: 150.5918 - reconstruction_loss: 144.4578\n", + "Epoch 18/30\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - kl_loss: 6.1266 - loss: 150.9010 - reconstruction_loss: 144.7745\n", + "Epoch 19/30\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - kl_loss: 6.1328 - loss: 150.9442 - reconstruction_loss: 144.8113\n", + "Epoch 20/30\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - kl_loss: 6.1450 - loss: 150.5881 - reconstruction_loss: 144.4432\n", + "Epoch 21/30\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - kl_loss: 6.1489 - loss: 150.6388 - reconstruction_loss: 144.4899\n", + "Epoch 22/30\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - kl_loss: 6.1670 - loss: 150.1352 - reconstruction_loss: 143.9682\n", + "Epoch 23/30\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - kl_loss: 6.1987 - loss: 149.7347 - reconstruction_loss: 143.5359\n", + "Epoch 24/30\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - kl_loss: 6.2256 - loss: 149.6280 - reconstruction_loss: 143.4025\n", + "Epoch 25/30\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - kl_loss: 6.1774 - loss: 149.6085 - reconstruction_loss: 143.4310\n", + "Epoch 26/30\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - kl_loss: 6.2158 - loss: 149.5078 - reconstruction_loss: 143.2921\n", + "Epoch 27/30\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - kl_loss: 6.2223 - loss: 149.4093 - reconstruction_loss: 143.1869\n", + "Epoch 28/30\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - kl_loss: 6.2567 - loss: 149.5168 - reconstruction_loss: 143.2601\n", + "Epoch 29/30\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - kl_loss: 6.2480 - loss: 149.2111 - reconstruction_loss: 142.9631\n", + "Epoch 30/30\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - kl_loss: 6.2669 - loss: 149.0203 - reconstruction_loss: 142.7534\n" + ] + } + ], + "source": [ + "(x_train, _), (x_test, _) = keras.datasets.mnist.load_data()\n", + "mnist_digits = np.concatenate([x_train, x_test], axis=0)\n", + "mnist_digits = np.expand_dims(mnist_digits, -1).astype(\"float32\") / 255\n", + "\n", + "vae = VAE(encoder, decoder)\n", + "vae.compile(optimizer=keras.optimizers.Adam())\n", + "history = vae.fit(mnist_digits, epochs=30, batch_size=128)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "6bac5ae4-6740-4486-ae1b-5a5ae986dfc4", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "hist_pd = pd.DataFrame(history.history)\n", + "hist_pd.to_csv(os.path.join(model_dir, f'history.csv'), index = False)" + ] + }, + { + "cell_type": "markdown", + "id": "7494fca6-d4b7-4ef3-91a5-4f1422526fd5", + "metadata": { + "tags": [] + }, + "source": [ + "## Display a grid of reconstructed digits in the latent space" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "a7d63bff-e2c6-40a1-a8f8-d375b008557e", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Matplotlib is building the font cache; this may take a moment.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "def plot_latent_space(vae, n=30, figsize=15):\n", + " # display a n*n 2D manifold of digits\n", + " digit_size = 28\n", + " scale = 1.0\n", + " figure = np.zeros((digit_size * n, digit_size * n))\n", + " # linearly spaced coordinates corresponding to the 2D plot\n", + " # of digit classes in the latent space\n", + " grid_x = np.linspace(-scale, scale, n)\n", + " grid_y = np.linspace(-scale, scale, n)[::-1]\n", + "\n", + " for i, yi in enumerate(grid_y):\n", + " for j, xi in enumerate(grid_x):\n", + " z_sample = np.array([[xi, yi]])\n", + " x_decoded = vae.decoder.predict(z_sample, verbose=0)\n", + " digit = x_decoded[0].reshape(digit_size, digit_size)\n", + " figure[\n", + " i * digit_size : (i + 1) * digit_size,\n", + " j * digit_size : (j + 1) * digit_size,\n", + " ] = digit\n", + "\n", + " plt.figure(figsize=(figsize, figsize))\n", + " start_range = digit_size // 2\n", + " end_range = n * digit_size + start_range\n", + " pixel_range = np.arange(start_range, end_range, digit_size)\n", + " sample_range_x = np.round(grid_x, 1)\n", + " sample_range_y = np.round(grid_y, 1)\n", + " plt.xticks(pixel_range, sample_range_x)\n", + " plt.yticks(pixel_range, sample_range_y)\n", + " plt.xlabel(\"z[0]\")\n", + " plt.ylabel(\"z[1]\")\n", + " plt.imshow(figure, cmap=\"Greys_r\")\n", + " plt.show()\n", + "\n", + "\n", + "plot_latent_space(vae)" + ] + }, + { + "cell_type": "markdown", + "id": "fbc89d55-5962-463f-ab7f-f0332e2e3430", + "metadata": {}, + "source": [ + "## Display how the latent space clusters digits" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "68dabfbc-7024-4831-8432-6682c42dab7f", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_label_clusters(vae, data, labels):\n", + " # display a 2D plot of the digit classes in the latent space\n", + " z_mean, _, _ = vae.encoder.predict(data, verbose=0)\n", + " plt.figure(figsize=(12, 10))\n", + " plt.scatter(z_mean[:, 0], z_mean[:, 1], c=labels)\n", + " plt.colorbar()\n", + " plt.xlabel(\"z[0]\")\n", + " plt.ylabel(\"z[1]\")\n", + " plt.show()\n", + "\n", + "\n", + "(x_train, y_train), _ = keras.datasets.mnist.load_data()\n", + "x_train = np.expand_dims(x_train, -1).astype(\"float32\") / 255\n", + "\n", + "plot_label_clusters(vae, x_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "612e7ebb-9f92-44e5-8e4c-dede651f0d31", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:cvae_env]", + "language": "python", + "name": "conda-env-cvae_env-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.19" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/run_on_cluster/cvae-weather-ensemble/cvae-digits-examples/plot_of_loss - one_number_example.png b/run_on_cluster/cvae-weather-ensemble/cvae-digits-examples/plots/plot_of_loss - one_number_example.png similarity index 100% rename from run_on_cluster/cvae-weather-ensemble/cvae-digits-examples/plot_of_loss - one_number_example.png rename to run_on_cluster/cvae-weather-ensemble/cvae-digits-examples/plots/plot_of_loss - one_number_example.png diff --git a/run_on_cluster/cvae-weather-ensemble/cvae-digits-examples/plot_of_loss - original_example.png b/run_on_cluster/cvae-weather-ensemble/cvae-digits-examples/plots/plot_of_loss - original_example.png similarity index 100% rename from run_on_cluster/cvae-weather-ensemble/cvae-digits-examples/plot_of_loss - original_example.png rename to run_on_cluster/cvae-weather-ensemble/cvae-digits-examples/plots/plot_of_loss - original_example.png diff --git a/run_on_cluster/cvae-weather-ensemble/cvae-digits-examples/plot_of_loss - saving_weights_example.png b/run_on_cluster/cvae-weather-ensemble/cvae-digits-examples/plots/plot_of_loss - saving_weights_example.png similarity index 100% rename from run_on_cluster/cvae-weather-ensemble/cvae-digits-examples/plot_of_loss - saving_weights_example.png rename to run_on_cluster/cvae-weather-ensemble/cvae-digits-examples/plots/plot_of_loss - saving_weights_example.png diff --git a/run_on_cluster/cvae-weather-ensemble/cvae_assess.ipynb b/run_on_cluster/cvae-weather-ensemble/cvae_assess.ipynb new file mode 100644 index 0000000..8c8a521 --- /dev/null +++ b/run_on_cluster/cvae-weather-ensemble/cvae_assess.ipynb @@ -0,0 +1,476 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# **CVAE Assess on a Trained model**\n", + "The goal here is to use a trained CVAE model with new data to create synthetic ensemble members." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# imports\n", + "import tensorflow as tf\n", + "tf.compat.v1.enable_eager_execution()\n", + "\n", + "import os, json\n", + "import netCDF4\n", + "import cartopy\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from scipy.ndimage.filters import gaussian_filter as gf\n", + "\n", + "from tensorflow import keras\n", + "from keras import layers\n", + "import cProfile # For eager execution, https://www.tensorflow.org/guide/eager\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "from scripts.cvae import Sampling, build_encoder, calculate_final_shape, calculate_output_paddings\n", + "from scripts.cvae import build_decoder, VAE, plot_latent_space, plot_images\n", + "\n", + "from scripts.get_data import download_file\n", + "from scripts.get_data import convert_file\n", + "from scripts.get_data import subset_file\n", + "from scripts.get_data import remove_data # removes all data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# data loading\n", + "def load_data(data_dir): \n", + " files = [f for f in os.listdir(data_dir)] # if ('subset' in f and 'tmp' not in f)]\n", + " \n", + " all_data = ((np.expand_dims(\n", + " np.concatenate(\n", + " [netCDF4.Dataset(data_dir + converted_file)['msl'][:] for converted_file in files]\n", + " ),\n", + " -1\n", + " ).astype(\"float32\") - 85000) / (110000 - 85000)).astype(\"float16\")\n", + " \n", + " return all_data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "print(\"TF version:\", tf.__version__)\n", + "print(\"GPU is\", \"available\" if tf.config.list_physical_devices('GPU') else \"NOT AVAILABLE\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load and preprocess the input data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "data_pdir = \"./gefs_data\"\n", + "data_dir = \"./gefs_data/converted/\"\n", + "model_dir = './model_dir'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# example parameters\n", + "ex_year = \"2018\"\n", + "ex_month = \"01\"\n", + "ex_day = \"01\"\n", + "ex_ensemble = \"c00\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# example for getting and converting files \n", + "download_file(ex_year, ex_month, ex_day, ex_ensemble, data_pdir)\n", + "convert_file(ex_year, ex_month, ex_day, ex_ensemble, data_dir)\n", + "slp = load_data(data_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# look at data structure\n", + "print(np.shape(slp))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# grid point locations\n", + "lons = np.loadtxt('coordinates/lon.x')\n", + "lats = np.loadtxt('coordinates/lat.y')\n", + "\n", + "x, y = np.meshgrid(lons,lats)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# example for plot\n", + "def plot_map(images, num_levels, need_filter):\n", + " # plot set up\n", + " c = 'k'\n", + " fig = plt.figure(figsize = (9,6))\n", + " ax = plt.axes(projection = cartopy.crs.LambertConformal())\n", + " \n", + " # add features to the map\n", + " ax.add_feature(cartopy.feature.LAND)\n", + " ax.add_feature(cartopy.feature.OCEAN)\n", + " ax.add_feature(cartopy.feature.LAKES, alpha = 0.5)\n", + " ax.add_feature(cartopy.feature.STATES, edgecolor = 'grey')\n", + " \n", + " # plot each given image\n", + " for i, image in enumerate(images):\n", + " if i == 0: # print only first time step\n", + " if need_filter:\n", + " plt_points = gf(np.squeeze(image) * (110000 - 85000) + 85000, sigma = 0.5, mode = 'nearest')\n", + " print(i, \"filtered mean:\", np.mean(plt_points))\n", + " print(i, \"filtered std:\", np.std(plt_points))\n", + " c = 'r'\n", + " else:\n", + " plt_points = np.squeeze(image[:,:,0]) * (110000 - 85000) + 85000\n", + "\n", + " # contour levels\n", + " data_min = np.min(plt_points) # min level\n", + " data_max = np.max(plt_points) # max level\n", + "\n", + " # plotting the contour\n", + " plt.contour(x, y, plt_points,\n", + " transform = cartopy.crs.PlateCarree(),\n", + " levels = np.linspace(data_min, data_max, num_levels),\n", + " colors = c,\n", + " linewidths = 1) \n", + "\n", + " ax.set_extent([-150, -60, 20, 65]) # ax.set_extent([-120, -73, 23, 50])\n", + "\n", + " plt.title('GEFSv12 MSL 2017 01 01 0000 UTC')\n", + " # plt.colorbar()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "slp.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "plot_map(slp, 20, True)\n", + "# plot_map(slp, 20, False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load ML model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# key CVAE definition parameters\n", + "latent_dim = 2\n", + "batch_size, height, width, channels = slp.shape\n", + "\n", + "encoder = build_encoder(latent_dim)\n", + "decoder = build_decoder(latent_dim)\n", + "vae = VAE(encoder, decoder)\n", + "vae.compile(optimizer = 'rmsprop')\n", + "vae.load_weights(os.path.join('model_dir', 'vae.weights.h5'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "z_mean, z_log_var, z = vae.encoder((np.expand_dims(slp[0:79:2,:,:,:], -1)))\n", + "sample_output_images = vae.decoder(z)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# plt.scatter(z[:, 0], z[:, 1])\n", + "# plt.show()\n", + "\n", + "fig, ax = plt.subplots(1,1)\n", + "ax.plot(z[:,0],z[:,1],\"b-\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Encode, perturb, and decode" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "gf(np.squeeze(sample_output_images[0]) * (110000 - 85000) + 85000, [3,3], mode = 'constant')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# plot each decoded state\n", + "plot_map(sample_output_images, 20, True)\n", + "\n", + "# # plot original\n", + "# print('Filtering...')\n", + "# filtered = gf(np.squeeze(slp[0,:,:,0]) * (110000 - 85000) + 85000, [3,3], mode = 'constant')\n", + "\n", + "# # get the min and max of data\n", + "# data_min = np.min(filtered)\n", + "# data_max = np.max(filtered)\n", + "# # number of levels\n", + "# num_levels = 20\n", + "\n", + "# print('Contour plotting...')\n", + "# plt.contour(x, y, filtered,\n", + "# transform = cartopy.crs.PlateCarree(),\n", + "# levels = np.linspace(data_min, data_max, num_levels), # list of contour levels\n", + "# colors = 'k',\n", + "# linewidths = 2)\n", + "\n", + "# # ax.set_xlim([-150,-60])\n", + "# # ax.set_ylim([20,65])\n", + "# # plt.colorbar()\n", + "# plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Perturb current state" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "perturbed_images_high = vae.decoder(z_mean + z_log_var)\n", + "perturbed_images_low = vae.decoder(z_mean - z_log_var)\n", + "\n", + "print(tf.executing_eagerly())\n", + "# tf.compat.v1.enable_eager_execution()\n", + "\n", + "# Plot setup\n", + "fig = plt.figure(figsize=(9,6))\n", + "ax = plt.axes(projection = cartopy.crs.LambertConformal())\n", + "ax.add_feature(cartopy.feature.LAND)\n", + "ax.add_feature(cartopy.feature.OCEAN)\n", + "ax.add_feature(cartopy.feature.LAKES, alpha = 0.5)\n", + "ax.add_feature(cartopy.feature.STATES, edgecolor='grey')\n", + "\n", + "# Plot each one\n", + "for i, image in enumerate(perturbed_images_high):\n", + " if i == 0 :\n", + " print('Filtering...')\n", + " filtered = gf(np.squeeze(image)*120000/100, [3,3], mode='constant')\n", + " print('Contour plotting...')\n", + " plt.contour(x,y,filtered,\n", + " transform = cartopy.crs.PlateCarree(),\n", + " levels=[980,990,1000,1010,1015,1020,1025,1030,1035,1040,1045,1050],colors='r',linewidths=1) \n", + "\n", + "# Plot each one\n", + "for i, image in enumerate(perturbed_images_low):\n", + " if i == 0 :\n", + " print('Filtering...')\n", + " filtered = gf(np.squeeze(image)*120000/100, [3,3], mode='constant')\n", + " print('Contour plotting...')\n", + " plt.contour(x,y,filtered,\n", + " transform = cartopy.crs.PlateCarree(),\n", + " levels=[980,990,1000,1010,1015,1020,1025,1030,1035,1040,1045,1050],colors='b',linewidths=1) \n", + " \n", + "# Plot original\n", + "plt.contour(x,y,np.squeeze(slp[0,:,:,0])*120000/100,\n", + " transform = cartopy.crs.PlateCarree(),\n", + " levels=[980,990,1000,1010,1015,1020,1025,1030,1035,1040,1045,1050],colors='k',linewidths=2)\n", + "\n", + "plt.title('GEFSv12 Re-forecast SLP 990hPa 2018 01 10 0000 UTC Cycle')\n", + "#ax.set_extent([-120, -73, 23, 50])\n", + "ax.set_extent([-150, -60, 20, 65])\n", + "#plt.colorbar()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Generate totally random weather maps" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "codings = tf.random.normal(shape = [12, latent_dim])\n", + "images = vae.decoder(codings).numpy()\n", + "\n", + "# Plot setup\n", + "fig = plt.figure(figsize=(9,6))\n", + "ax = plt.axes(projection = cartopy.crs.LambertConformal())\n", + "ax.add_feature(cartopy.feature.LAND)\n", + "ax.add_feature(cartopy.feature.OCEAN)\n", + "ax.add_feature(cartopy.feature.LAKES, alpha = 0.5)\n", + "ax.add_feature(cartopy.feature.STATES, edgecolor='grey')\n", + "\n", + "# Plot each one\n", + "for i, image in enumerate(images):\n", + " print('Filtering...')\n", + " filtered = gf(np.squeeze(image)*120000/100, [5,5], mode='constant')\n", + " plt.contour(x,y,filtered,\n", + " transform = cartopy.crs.PlateCarree(),\n", + " levels=[970,980,990,1000,1010,1015,1020,1025,1030,1040,1050,1060],colors='k',linewidths=1)\n", + " \n", + "# plt.title('Random MSL pressure maps from ML model trained with GEFSv12 Re-forecast 2018-2019')\n", + "# ax.set_extent([-120, -73, 23, 50])\n", + "ax.set_extent([-150, -60, 20, 65])\n", + "# plt.colorbar()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "print(latent_dim)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:cvae_env]", + "language": "python", + "name": "conda-env-cvae_env-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/run_on_cluster/cvae-weather-ensemble/cvae_env.yaml b/run_on_cluster/cvae-weather-ensemble/cvae_env.yaml new file mode 100644 index 0000000..8ddf0b1 --- /dev/null +++ b/run_on_cluster/cvae-weather-ensemble/cvae_env.yaml @@ -0,0 +1,412 @@ +name: cvae_env +channels: + - anaconda + - conda-forge + - defaults +dependencies: + - _libgcc_mutex=0.1=conda_forge + - _openmp_mutex=4.5=2_gnu + - absl-py=2.1.0=pyhd8ed1ab_0 + - aiohttp=3.9.5=py39hd1e30aa_0 + - aiohttp-retry=2.8.3=pyhd8ed1ab_0 + - aiosignal=1.3.1=pyhd8ed1ab_0 + - alsa-lib=1.2.12=h4ab18f5_0 + - amqp=5.2.0=pyhd8ed1ab_1 + - annotated-types=0.7.0=pyhd8ed1ab_0 + - ansicolors=1.1.8=pyhd8ed1ab_0 + - antlr-python-runtime=4.9.3=pyhd8ed1ab_1 + - appdirs=1.4.4=pyh9f0ad1d_0 + - asttokens=2.0.5=pyhd3eb1b0_0 + - astunparse=1.6.3=pyhd8ed1ab_0 + - async-timeout=4.0.3=pyhd8ed1ab_0 + - asyncssh=2.14.1=pyhd8ed1ab_0 + - atk-1.0=2.38.0=h04ea711_2 + - atpublic=3.0.1=pyhd8ed1ab_0 + - attr=2.5.1=h166bdaf_1 + - attrs=23.2.0=pyh71513ae_0 + - backcall=0.2.0=pyhd3eb1b0_0 + - backports.zoneinfo=0.2.1=py39hf3d152e_8 + - billiard=4.2.0=py39hd1e30aa_0 + - blosc=1.21.6=hef167b5_0 + - brotli=1.1.0=hd590300_1 + - brotli-bin=1.1.0=hd590300_1 + - brotli-python=1.1.0=py39h3d6467e_1 + - bzip2=1.0.8=h4bc722e_7 + - c-ares=1.32.2=h4bc722e_0 + - ca-certificates=2024.7.4=hbcca054_0 + - cached-property=1.5.2=hd8ed1ab_1 + - cached_property=1.5.2=pyha770c72_1 + - cairo=1.18.0=hbb29018_2 + - cartopy=0.23.0=py39hfc16268_1 + - cdo=2.4.1=h9fe33b1_1 + - celery=5.3.5=pyhd8ed1ab_0 + - certifi=2024.7.4=pyhd8ed1ab_0 + - cffi=1.16.0=py39h7a31438_0 + - cftime=1.6.4=py39hd92a3bb_0 + - charset-normalizer=3.3.2=pyhd8ed1ab_0 + - click=8.1.7=unix_pyh707e725_0 + - click-didyoumean=0.3.1=pyhd8ed1ab_0 + - click-plugins=1.1.1=py_0 + - click-repl=0.3.0=pyhd8ed1ab_0 + - colorama=0.4.6=pyhd8ed1ab_0 + - comm=0.2.1=py39h06a4308_0 + - configobj=5.0.8=pyhd8ed1ab_0 + - contourpy=1.2.1=py39h7633fee_0 + - cryptography=42.0.8=py39h8169da8_0 + - cuda-crt-tools=12.5.82=ha770c72_0 + - cuda-cudart=12.5.82=he02047a_0 + - cuda-cudart_linux-64=12.5.82=h85509e4_0 + - cuda-nvcc-tools=12.5.82=hd3aeb46_0 + - cuda-nvrtc=12.5.82=he02047a_0 + - cuda-nvtx=12.5.82=he02047a_0 + - cuda-nvvm-tools=12.5.82=h59595ed_0 + - cuda-version=12.5=hd4f0392_3 + - cudnn=8.9.7.29=h092f7fd_3 + - cycler=0.12.1=pyhd8ed1ab_0 + - dbus=1.13.6=h5008d03_3 + - debugpy=1.6.7=py39h6a678d5_0 + - decorator=5.1.1=pyhd3eb1b0_0 + - dictdiffer=0.9.0=pyhd8ed1ab_0 + - diskcache=5.6.3=pyhd8ed1ab_0 + - distro=1.9.0=pyhd8ed1ab_0 + - dpath=2.2.0=pyha770c72_0 + - dulwich=0.22.1=py39ha68c5e3_0 + - dvc=3.51.2=pyhd8ed1ab_0 + - dvc-data=3.15.1=pyhd8ed1ab_1 + - dvc-http=2.32.0=pyhd8ed1ab_0 + - dvc-objects=5.1.0=pyhd8ed1ab_1 + - dvc-render=1.0.2=pyhd8ed1ab_0 + - dvc-studio-client=0.21.0=pyhd8ed1ab_0 + - dvc-task=0.4.0=pyhd8ed1ab_0 + - eccodes=2.36.0=h762793a_0 + - entrypoints=0.4=pyhd8ed1ab_0 + - esmf=8.6.1=nompi_h0a5817f_2 + - exceptiongroup=1.2.0=py39h06a4308_0 + - executing=0.8.3=pyhd3eb1b0_0 + - expat=2.6.2=h59595ed_0 + - fftw=3.3.10=nompi_hf1063bd_110 + - filelock=3.15.4=pyhd8ed1ab_0 + - findlibs=0.0.5=pyhd8ed1ab_0 + - flatbuffers=24.3.25=h59595ed_0 + - flatten-dict=0.4.2=pyhd8ed1ab_1 + - flufl.lock=7.1=pyhd8ed1ab_0 + - font-ttf-dejavu-sans-mono=2.37=hab24e00_0 + - font-ttf-inconsolata=3.000=h77eed37_0 + - 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shtab=1.7.1=pyhd8ed1ab_0 + - simplejson=3.19.2=py39hd1e30aa_0 + - sip=6.7.12=py39h3d6467e_0 + - six=1.16.0=pyh6c4a22f_0 + - smmap=5.0.0=pyhd8ed1ab_0 + - snappy=1.2.1=ha2e4443_0 + - sqlite=3.32.3=hcee41ef_1 + - sqltrie=0.11.0=pyhd8ed1ab_0 + - stack_data=0.2.0=pyhd3eb1b0_0 + - tabulate=0.9.0=pyhd8ed1ab_1 + - tempest-remap=2.2.0=h13910d2_3 + - tenacity=8.5.0=pyhd8ed1ab_0 + - tensorboard=2.16.2=pyhd8ed1ab_0 + - tensorboard-data-server=0.7.0=py39hd4f0224_1 + - tensorflow=2.16.2=cuda120py39h298b457_0 + - tensorflow-base=2.16.2=cuda120py39h1d778e6_0 + - tensorflow-estimator=2.16.2=cuda120py39h225b957_0 + - termcolor=2.4.0=pyhd8ed1ab_0 + - threadpoolctl=3.5.0=pyhc1e730c_0 + - tk=8.6.13=noxft_h4845f30_101 + - toml=0.10.2=pyhd8ed1ab_0 + - tomli=2.0.1=pyhd8ed1ab_0 + - tomlkit=0.13.0=pyha770c72_0 + - tornado=6.4.1=py39hd3abc70_0 + - tqdm=4.66.4=pyhd8ed1ab_0 + - traitlets=5.14.3=pyhd8ed1ab_0 + - typer=0.12.3=pyhd8ed1ab_0 + - typer-slim=0.12.3=pyhd8ed1ab_0 + - typer-slim-standard=0.12.3=hd8ed1ab_0 + - typing-extensions=4.12.2=hd8ed1ab_0 + - typing_extensions=4.12.2=pyha770c72_0 + - tzdata=2024a=h04d1e81_0 + - udunits2=2.2.28=h40f5838_3 + - unicodedata2=15.1.0=py39hd1e30aa_0 + - urllib3=2.2.2=pyhd8ed1ab_1 + - vine=5.1.0=pyhd8ed1ab_0 + - voluptuous=0.15.2=pyhd8ed1ab_0 + - wcwidth=0.2.5=pyhd3eb1b0_0 + - werkzeug=3.0.3=pyhd8ed1ab_0 + - wheel=0.43.0=py39h06a4308_0 + - wrapt=1.16.0=py39hd1e30aa_0 + - xcb-util=0.4.1=hb711507_2 + - xcb-util-image=0.4.0=hb711507_2 + - xcb-util-keysyms=0.4.1=hb711507_0 + - xcb-util-renderutil=0.3.10=hb711507_0 + - xcb-util-wm=0.4.2=hb711507_0 + - xkeyboard-config=2.42=h4ab18f5_0 + - xorg-fixesproto=5.0=h7f98852_1002 + - xorg-inputproto=2.3.2=h7f98852_1002 + - xorg-kbproto=1.0.7=h7f98852_1002 + - xorg-libice=1.1.1=hd590300_0 + - xorg-libsm=1.2.4=h7391055_0 + - xorg-libx11=1.8.9=hb711507_1 + - xorg-libxau=1.0.11=hd590300_0 + - xorg-libxdmcp=1.1.3=h7f98852_0 + - xorg-libxext=1.3.4=h0b41bf4_2 + - xorg-libxfixes=5.0.3=h7f98852_1004 + - xorg-libxi=1.7.10=h7f98852_0 + - xorg-libxrender=0.9.11=hd590300_0 + - xorg-renderproto=0.11.1=h7f98852_1002 + - xorg-xextproto=7.3.0=h0b41bf4_1003 + - xorg-xf86vidmodeproto=2.3.1=h7f98852_1002 + - xorg-xproto=7.0.31=h7f98852_1007 + - xz=5.4.6=h5eee18b_1 + - yaml=0.2.5=h7f98852_2 + - yarl=1.9.4=py39hd1e30aa_0 + - zc.lockfile=3.0.post1=pyhd8ed1ab_0 + - zeromq=4.3.5=h75354e8_4 + - zipp=3.19.2=pyhd8ed1ab_0 + - zlib=1.3.1=h4ab18f5_1 + - zstandard=0.23.0=py39h623c9ba_0 + - zstd=1.5.6=ha6fb4c9_0 +prefix: /home/lobielodan/pw/.miniconda3c/envs/cvae_env diff --git a/run_on_cluster/cvae-weather-ensemble/cvae_log.ipynb b/run_on_cluster/cvae-weather-ensemble/cvae_log.ipynb new file mode 100644 index 0000000..99920ca --- /dev/null +++ b/run_on_cluster/cvae-weather-ensemble/cvae_log.ipynb @@ -0,0 +1,3027 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f6226ca5-0869-4973-8369-3469cbdff43c", + "metadata": { + "papermill": { + "duration": 0.002878, + "end_time": "2024-07-25T14:33:23.439471", + "exception": false, + "start_time": "2024-07-25T14:33:23.436593", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# **CVAE Training**" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "094dca89-dffa-4a56-8263-371fb372a2cb", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-25T14:33:23.450909Z", + "iopub.status.busy": "2024-07-25T14:33:23.450607Z", + "iopub.status.idle": "2024-07-25T14:33:26.110522Z", + "shell.execute_reply": "2024-07-25T14:33:26.109977Z" + }, + "papermill": { + "duration": 2.66821, + "end_time": "2024-07-25T14:33:26.111708", + "exception": false, + "start_time": "2024-07-25T14:33:23.443498", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:33:24.314602: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:10575] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2024-07-25 14:33:24.314670: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:479] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2024-07-25 14:33:24.316256: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1442] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2024-07-25 14:33:24.322678: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TF version: 2.16.2\n", + "GPU is available\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:33:26.057549: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2024-07-25 14:33:26.103956: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2024-07-25 14:33:26.105981: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n" + ] + } + ], + "source": [ + "import os, json\n", + "\n", + "import papermill as pm\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import tensorflow as tf\n", + "import netCDF4\n", + "import cartopy\n", + "\n", + "from tensorflow import keras\n", + "from keras import layers\n", + "from sklearn.model_selection import train_test_split \n", + "\n", + "print(\"TF version:\", tf.__version__)\n", + "print(\"GPU is\", \"available\" if tf.config.list_physical_devices('GPU') else \"NOT AVAILABLE\")" + ] + }, + { + "cell_type": "markdown", + "id": "06a8b533-cdaa-4efd-938b-ef1d5a84338e", + "metadata": { + "papermill": { + "duration": 0.004051, + "end_time": "2024-07-25T14:33:26.118482", + "exception": false, + "start_time": "2024-07-25T14:33:26.114431", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Download and Convert Data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4b08e8b0-f9fa-40b9-852b-8650e681cff2", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-25T14:33:26.129663Z", + "iopub.status.busy": "2024-07-25T14:33:26.129164Z", + "iopub.status.idle": "2024-07-25T14:33:26.132328Z", + "shell.execute_reply": "2024-07-25T14:33:26.131850Z" + }, + "papermill": { + "duration": 0.009123, + "end_time": "2024-07-25T14:33:26.133395", + "exception": false, + "start_time": "2024-07-25T14:33:26.124272", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "data_pdir = \"./gefs_data\"\n", + "data_dir = \"./gefs_data/converted/\"\n", + "model_dir = './model_dir'" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2ff4403c-9c45-4129-902b-93ebfe9749e3", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-25T14:33:26.141466Z", + "iopub.status.busy": "2024-07-25T14:33:26.141114Z", + "iopub.status.idle": "2024-07-25T14:33:26.151581Z", + "shell.execute_reply": "2024-07-25T14:33:26.151111Z" + }, + "papermill": { + "duration": 0.017279, + "end_time": "2024-07-25T14:33:26.152714", + "exception": false, + "start_time": "2024-07-25T14:33:26.135435", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# data definitions\n", + "\n", + "from scripts.get_data import download_file\n", + "from scripts.get_data import convert_file\n", + "from scripts.get_data import subset_file\n", + "from scripts.get_data import remove_data # removes all data\n", + "\n", + "# data loading\n", + "def load_data(data_dir): \n", + " files = [f for f in os.listdir(data_dir) if ('subset' in f and 'tmp' not in f)]\n", + " \n", + " all_data = ((np.expand_dims(\n", + " np.concatenate(\n", + " [netCDF4.Dataset(data_dir + converted_file)['msl'][:] for converted_file in files]\n", + " ),\n", + " -1\n", + " ).astype(\"float32\") - 85000) / (110000 - 85000)).astype(\"float16\")\n", + " \n", + " return all_data" + ] + }, + { + "cell_type": "markdown", + "id": "d03ebb2a-bc7b-4f52-98d3-ec85c51c7a5f", + "metadata": { + "papermill": { + "duration": 0.005869, + "end_time": "2024-07-25T14:33:26.160673", + "exception": false, + "start_time": "2024-07-25T14:33:26.154804", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Neural Network Design\n", + "\n", + "We need to get to a small latent space. Conv2D networks are good because they help reduce the number of connections in a network in a meaningful way. I'm using terms as defined in [this definition of conv2D](https://towardsdatascience.com/conv2d-to-finally-understand-what-happens-in-the-forward-pass-1bbaafb0b148).\n", + "\n", + "**Definitions:**\n", + "K -> kernel size;\n", + "P -> padding;\n", + "S -> stride;\n", + "D -> Dilation;\n", + "G -> Groups\n", + "\n", + "**Filter options:**\n", + "Longitude is easy because it is large and even, so as long as you have an even stride, you get integer results when dividing.\n", + "e.g. lon 9: stride 4, lat 7: stride 5\n", + "\n", + "- Latitude - whole numbers occurr for P = 2 & K = 3 or K = 11.\n", + "- 11 grid points * 0.25 deg * 100 km/deg = 275 km filter window (a good scale for weather)\n", + "- 9 grid points * 0.25 deg * 100 km/deg = 225 km\n", + "- Longitude - whole numbers occur for P = 0 & K = 11 (nice match with Latitude), P = 1 & K = 3 or 13, P = 2 & K = 5.\n", + "\n", + "For a 5 x 7 filter with 3 stride (no overlap) and no padding:\n", + "- lat: (721 - 4) / 3 = 239 possible steps (good whole number!)\n", + "- lon: (1440 - 4) / 3 = 478.6666 possible steps\n", + "\n", + "## Load and Preprocess Training Data:\n", + "The standard way of manipulating arrays in Conv2D layers in TF is to use arrays in the shape:\n", + "`batch_size, height, width, channels = data.shape`\n", + "In our case, the the `batch_size` is the number of image frames (i.e. separate samples or rows in a `.csv` file), the `height` and `width` define the size of the image frame in number of pixels, and the `channels` are the number of layers in the frames. Typically, channels are color layers (e.g. RGB or CMYK) but in our case, we could use different metereological variables. However, for this first experiment, **we only need one channel** because we're only going to use mean sea level pressure (msl).\n", + "\n", + "## Build the Encoder:\n", + "GFS grids I have available here are at 0.25 degree resolution. I'm doing this as a \"worst case\" scenario since there are also 0.5 and 1.0 degree grids with lower resolution but I can't find that data quickly and don't know what's available.\n", + "\n", + "These 0.25 degree grids are 721 x 1440.\n", + "Each forecast file is 3 hourly for 10 days = 8 steps/forecast * 10 days = 80 \"frames\"\n", + "This demo is only using two forecasts from the control ensemble\n", + "(one launched Jan 01, 2019 and one launched Jan 02, 2019) -> this is only \n", + "a small subset of the variability possible in the model.\n", + "\n", + "This particular data set spans 2000-2019 and there are 5 ensemble members.\n", + "\n", + "## Build the Decoder:\n", + "With the 11 x 11 and 5 x 5 filters, non-overlapping stride, applied here, we have a final \"image\" size of 14 x 27 and 64 channels." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f0144303-f1c9-4220-8178-3988d707688c", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-25T14:33:26.171963Z", + "iopub.status.busy": "2024-07-25T14:33:26.171654Z", + "iopub.status.idle": "2024-07-25T14:33:26.189049Z", + "shell.execute_reply": "2024-07-25T14:33:26.188536Z" + }, + "papermill": { + "duration": 0.024702, + "end_time": "2024-07-25T14:33:26.190177", + "exception": false, + "start_time": "2024-07-25T14:33:26.165475", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# model defintions\n", + "\n", + "class Sampling(layers.Layer):\n", + " \"\"\"Uses (z_mean, z_log_var) to sample z, the vector encoding a digit.\"\"\"\n", + "\n", + " def call(self, inputs):\n", + " z_mean, z_log_var = inputs\n", + " batch = tf.shape(z_mean)[0]\n", + " dim = tf.shape(z_mean)[1]\n", + " epsilon = tf.keras.backend.random_normal(shape=(batch, dim))\n", + " return z_mean + tf.exp(0.5 * z_log_var) * epsilon\n", + "\n", + "def build_encoder(latent_dim):\n", + " encoder_inputs = keras.Input(shape=(721, 1440, 1))\n", + " \n", + " x = layers.Conv2D(32, 11, activation = \"relu\", strides = [9, 10], padding = \"valid\")(encoder_inputs)\n", + " x = layers.Conv2D(64, [5,9], activation = \"relu\", strides = [5, 9], padding = \"valid\")(x)\n", + " x = layers.Flatten()(x)\n", + " x = layers.Dense(16, activation=\"relu\")(x)\n", + " \n", + " z_mean = layers.Dense(latent_dim, name=\"z_mean\")(x)\n", + " z_log_var = layers.Dense(latent_dim, name=\"z_log_var\")(x)\n", + " z = Sampling()([z_mean, z_log_var])\n", + " \n", + " encoder = keras.Model(encoder_inputs, [z_mean, z_log_var, z], name = \"encoder\")\n", + " \n", + " print(encoder.summary())\n", + " return encoder\n", + "\n", + "def build_decoder(latent_dim):\n", + " latent_inputs = keras.Input(shape=(latent_dim,))\n", + " x = layers.Dense(15 * 15 * 64, activation=\"relu\")(latent_inputs)\n", + " x = layers.Reshape((15, 15, 64))(x)\n", + " # FIXME - there is something wrong here, but at least there is a pattern.\n", + " # Using output_padding as a fudge factor -> it may be that there is exactly\n", + " # one \"missing\" filter stamp/convolution because for both Conv2DTranspose\n", + " # operations, output_padding is set to maximum it could be in both dims\n", + " # (i.e. exactly one less than the stride of each filter).\n", + " x = layers.Conv2DTranspose(64, [5, 9], activation = \"relu\", strides = [5,9], padding = \"valid\", output_padding = [4, 8])(x)\n", + " x = layers.Conv2DTranspose(32, 11, activation = \"relu\", strides = [9,10], padding = \"valid\", output_padding = [8, 9])(x)\n", + " decoder_outputs = layers.Conv2DTranspose(1, 3, activation = \"sigmoid\", padding = \"same\")(x)\n", + " decoder = keras.Model(latent_inputs, decoder_outputs, name = \"decoder\")\n", + " \n", + " print(decoder.summary())\n", + " return decoder\n", + "\n", + "class VAE(keras.Model):\n", + " def __init__(self, encoder, decoder, **kwargs):\n", + " super(VAE, self).__init__(**kwargs)\n", + " self.encoder = encoder\n", + " self.decoder = decoder\n", + " self.total_loss_tracker = keras.metrics.Mean(name = \"total_loss\")\n", + " self.reconstruction_loss_tracker = keras.metrics.Mean(name = \"reconstruction_loss\")\n", + " self.kl_loss_tracker = keras.metrics.Mean(name = \"kl_loss\")\n", + "\n", + " @property\n", + " def metrics(self):\n", + " return [\n", + " self.total_loss_tracker,\n", + " self.reconstruction_loss_tracker,\n", + " self.kl_loss_tracker,\n", + " ]\n", + "\n", + " def train_step(self, data):\n", + " \n", + " with tf.GradientTape() as tape:\n", + " z_mean, z_log_var, z = self.encoder(data)\n", + " reconstruction = self.decoder(z)\n", + " # FIXME: Normalize loss with the number of features (28 * 28)\n", + " n_features = 28 * 28\n", + " reconstruction_loss = tf.reduce_mean(\n", + " tf.reduce_sum(\n", + " keras.losses.binary_crossentropy(data, reconstruction), axis = (1, 2)\n", + " )\n", + " ) / n_features\n", + " kl_loss = -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var))\n", + " kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis = 1)) / n_features\n", + " total_loss = (reconstruction_loss + kl_loss)\n", + " grads = tape.gradient(total_loss, self.trainable_weights)\n", + " self.optimizer.apply_gradients(zip(grads, self.trainable_weights))\n", + " self.total_loss_tracker.update_state(total_loss)\n", + " self.reconstruction_loss_tracker.update_state(reconstruction_loss)\n", + " self.kl_loss_tracker.update_state(kl_loss)\n", + " \n", + " return {\n", + " \"loss\": self.total_loss_tracker.result(),\n", + " \"reconstruction_loss\": self.reconstruction_loss_tracker.result(),\n", + " \"kl_loss\": self.kl_loss_tracker.result(),\n", + " }\n", + "\n", + " # Needed to validate (validation loss) and to evaluate\n", + " def test_step(self, data):\n", + " if type(data) == tuple:\n", + " data, _ = data\n", + " \n", + " z_mean, z_log_var, z = self.encoder(data)\n", + " reconstruction = self.decoder(z)\n", + " # FIXME: Normalize loss with the number of features (28 * 28)\n", + " n_features = 28 * 28\n", + " reconstruction_loss = tf.reduce_mean(\n", + " tf.reduce_sum(\n", + " keras.losses.binary_crossentropy(data, reconstruction), axis = (1, 2)\n", + " )\n", + " ) / n_features\n", + " kl_loss = -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var))\n", + " kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis = 1)) / n_features\n", + " total_loss = (reconstruction_loss + kl_loss)\n", + " # grads = tape.gradient(total_loss, self.trainable_weights)\n", + " # self.optimizer.apply_gradients(zip(grads, self.trainable_weights))\n", + " self.total_loss_tracker.update_state(total_loss)\n", + " self.reconstruction_loss_tracker.update_state(reconstruction_loss)\n", + " self.kl_loss_tracker.update_state(kl_loss)\n", + " \n", + " return {\n", + " \"loss\": self.total_loss_tracker.result(),\n", + " \"reconstruction_loss\": self.reconstruction_loss_tracker.result(),\n", + " \"kl_loss\": self.kl_loss_tracker.result(),\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a61f638f-42a9-4d44-8f31-c6c42017a6bf", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-25T14:33:26.197721Z", + "iopub.status.busy": "2024-07-25T14:33:26.197316Z", + "iopub.status.idle": "2024-07-25T14:33:26.219243Z", + "shell.execute_reply": "2024-07-25T14:33:26.218779Z" + }, + "papermill": { + "duration": 0.02803, + "end_time": "2024-07-25T14:33:26.220360", + "exception": false, + "start_time": "2024-07-25T14:33:26.192330", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# training definitions\n", + "\n", + "def train_model(X_train, X_test, X_valid, date, vae, model_dir):\n", + " early_stopping_cb = keras.callbacks.EarlyStopping(patience = 5, restore_best_weights = True) # stops training early if the validation loss does not improve\n", + " \n", + " if os.path.exists(os.path.join(model_dir, 'vae.weights.h5')): # if the model has already been trained at least once, load that model\n", + " vae.load_weights(os.path.join(model_dir, 'vae.weights.h5'))\n", + "\n", + " history = vae.fit(\n", + " X_train, epochs = 50, batch_size = 40,\n", + " callbacks = [early_stopping_cb],\n", + " validation_data = (X_valid,)\n", + " )\n", + "\n", + " vae.save_weights(os.path.join(model_dir, 'vae.weights.h5')) # save model weights after training\n", + " !cp model_dir/vae.weights.h5 model_dir/vae.weights_{date}.h5 # make a copy to dvc save\n", + " \n", + " hist_pd = pd.DataFrame(history.history)\n", + " hist_pd.to_csv(os.path.join(model_dir, f'history_{date}.csv'), index = False)\n", + "\n", + " test_loss = vae.evaluate(X_test)\n", + " test_loss = dict(zip([\"loss\", \"reconstruction_loss\", \"kl_loss\"], test_loss))\n", + "\n", + " print('Test loss:', test_loss)\n", + "\n", + " with open(os.path.join(model_dir, f'test_loss_{date}.json'), 'w') as json_file:\n", + " json.dump(test_loss, json_file, indent = 4)\n", + " \n", + " print(date)\n", + " !sh scripts/run_dvcgit.sh model_dir/history_{date}.csv f\"{date}\"\n", + " !sh scripts/run_dvcgit.sh model_dir/vae.weights_{date}.h5 f\"{date}\"\n", + " !rm model_dir/vae.weights_{date}.h5 # delete copy\n", + " \n", + "def run_train(num_files, date, vae, data_dir, model_dir):\n", + " slp = load_data(data_dir) # load data\n", + " print(\"shape:\", np.shape(slp)) # verify data shape\n", + " \n", + " # split the data - y values are throw away\n", + " X_train, X_test, y_train, y_test = train_test_split(slp[0:(num_files * 40 - 1), :, :, :], np.arange(0, num_files * 40 - 1), test_size = 0.2, random_state = 1)\n", + " X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size = 0.25, random_state = 1) # 0.25 x 0.8 = 0.2\n", + "\n", + " train_model(X_train, X_test, X_valid, date, vae, model_dir)\n", + " remove_data(data_pdir)" + ] + }, + { + "cell_type": "markdown", + "id": "3defdd05-1774-4e56-bd64-2f760c004842", + "metadata": { + "papermill": { + "duration": 0.005824, + "end_time": "2024-07-25T14:33:26.228836", + "exception": false, + "start_time": "2024-07-25T14:33:26.223012", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Train the VAE model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "3dd6a16c-2ff9-42fe-bdcd-b8a970f88b9f", + "metadata": { + "execution": { + "iopub.execute_input": "2024-07-25T14:33:26.239825Z", + "iopub.status.busy": "2024-07-25T14:33:26.239501Z", + "iopub.status.idle": "2024-07-25T14:33:26.810929Z", + "shell.execute_reply": "2024-07-25T14:33:26.810406Z" + }, + "papermill": { + "duration": 0.577884, + "end_time": "2024-07-25T14:33:26.812082", + "exception": false, + "start_time": "2024-07-25T14:33:26.234198", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:33:26.248557: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:33:26.250546: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2024-07-25 14:33:26.252578: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2024-07-25 14:33:26.384473: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2024-07-25 14:33:26.386279: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2024-07-25 14:33:26.387873: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2024-07-25 14:33:26.389371: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1928] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 20710 MB memory: -> device: 0, name: NVIDIA A10G, pci bus id: 0000:00:1e.0, compute capability: 8.6\n" + ] + }, + { + "data": { + "text/html": [ + "
Model: \"encoder\"\n",
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+       "┃ Layer (type)        ┃ Output Shape      ┃    Param # ┃ Connected to      ┃\n",
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+       "│ input_layer         │ (None, 721, 1440, │          0 │ -                 │\n",
+       "│ (InputLayer)        │ 1)                │            │                   │\n",
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+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
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+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ reshape (Reshape)               │ (None, 15, 15, 64)     │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_transpose                │ (None, 79, 143, 64)    │       184,384 │\n",
+       "│ (Conv2DTranspose)               │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_transpose_1              │ (None, 721, 1440, 32)  │       247,840 │\n",
+       "│ (Conv2DTranspose)               │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_transpose_2              │ (None, 721, 1440, 1)   │           289 │\n",
+       "│ (Conv2DTranspose)               │                        │               │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
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Devices:\n", + "I0000 00:00:1721918226.962736 82762 service.cc:153] StreamExecutor device (0): NVIDIA A10G, Compute Capability 8.6\n", + "2024-07-25 14:37:07.028323: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:37:07.216872: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:465] Loaded cuDNN version 8907\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:37:11.537023: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 5.07GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n", + "2024-07-25 14:37:11.537091: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 5.07GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n", + "2024-07-25 14:37:11.537113: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 5.07GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n", + "2024-07-25 14:37:11.537134: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 5.08GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n", + "2024-07-25 14:37:11.537154: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 5.07GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n", + "2024-07-25 14:37:11.537166: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 5.07GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n", + "2024-07-25 14:37:11.537185: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 5.07GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:37:14.091869: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng0{} for conv (f32[40,32,721,1440]{3,2,1,0}, u8[0]{0}) custom-call(f32[40,64,79,143]{3,2,1,0}, f32[64,32,11,11]{3,2,1,0}), window={size=11x11 stride=9x10}, dim_labels=bf01_oi01->bf01, custom_call_target=\"__cudnn$convBackwardInput\", backend_config={\"operation_queue_id\":\"0\",\"wait_on_operation_queues\":[],\"cudnn_conv_backend_config\":{\"activation_mode\":\"kNone\",\"conv_result_scale\":1,\"side_input_scale\":0,\"leakyrelu_alpha\":0}} is taking a while...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:37:58.156554: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 45.064752505s\n", + "Trying algorithm eng0{} for conv (f32[40,32,721,1440]{3,2,1,0}, u8[0]{0}) custom-call(f32[40,64,79,143]{3,2,1,0}, f32[64,32,11,11]{3,2,1,0}), window={size=11x11 stride=9x10}, dim_labels=bf01_oi01->bf01, custom_call_target=\"__cudnn$convBackwardInput\", backend_config={\"operation_queue_id\":\"0\",\"wait_on_operation_queues\":[],\"cudnn_conv_backend_config\":{\"activation_mode\":\"kNone\",\"conv_result_scale\":1,\"side_input_scale\":0,\"leakyrelu_alpha\":0}} is taking a while...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:38:20.227982: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng0{} for conv (f32[64,32,11,11]{3,2,1,0}, u8[0]{0}) custom-call(f32[40,32,721,1440]{3,2,1,0}, f32[40,64,79,143]{3,2,1,0}), window={size=11x11 stride=9x10}, dim_labels=bf01_oi01->bf01, custom_call_target=\"__cudnn$convBackwardFilter\", backend_config={\"operation_queue_id\":\"0\",\"wait_on_operation_queues\":[],\"cudnn_conv_backend_config\":{\"activation_mode\":\"kNone\",\"conv_result_scale\":1,\"side_input_scale\":0,\"leakyrelu_alpha\":0}} is taking a while...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:38:24.929055: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 5.701130817s\n", + "Trying algorithm eng0{} for conv (f32[64,32,11,11]{3,2,1,0}, u8[0]{0}) custom-call(f32[40,32,721,1440]{3,2,1,0}, f32[40,64,79,143]{3,2,1,0}), window={size=11x11 stride=9x10}, dim_labels=bf01_oi01->bf01, custom_call_target=\"__cudnn$convBackwardFilter\", backend_config={\"operation_queue_id\":\"0\",\"wait_on_operation_queues\":[],\"cudnn_conv_backend_config\":{\"activation_mode\":\"kNone\",\"conv_result_scale\":1,\"side_input_scale\":0,\"leakyrelu_alpha\":0}} is taking a while...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "I0000 00:00:1721918311.974033 82762 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "\u001b[1m1/8\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m10:05\u001b[0m 87s/step - kl_loss: 4.5086e-04 - loss: 860.0428 - reconstruction_loss: 860.0424" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m2/8\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 238ms/step - kl_loss: 4.5014e-04 - loss: 860.0762 - reconstruction_loss: 860.0758 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/8\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 237ms/step - 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reconstruction_loss: 860.0724" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m6/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 235ms/step - kl_loss: 4.4814e-04 - loss: 860.0573 - reconstruction_loss: 860.0568" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m7/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 235ms/step - kl_loss: 4.4794e-04 - loss: 860.0480 - reconstruction_loss: 860.0475" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:38:37.027997: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng0{} for conv (f32[7,32,721,1440]{3,2,1,0}, u8[0]{0}) custom-call(f32[7,64,79,143]{3,2,1,0}, f32[64,32,11,11]{3,2,1,0}), window={size=11x11 stride=9x10}, dim_labels=bf01_oi01->bf01, custom_call_target=\"__cudnn$convBackwardInput\", backend_config={\"operation_queue_id\":\"0\",\"wait_on_operation_queues\":[],\"cudnn_conv_backend_config\":{\"activation_mode\":\"kNone\",\"conv_result_scale\":1,\"side_input_scale\":0,\"leakyrelu_alpha\":0}} is taking a while...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:38:43.915898: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 7.887961793s\n", + "Trying algorithm eng0{} for conv (f32[7,32,721,1440]{3,2,1,0}, u8[0]{0}) custom-call(f32[7,64,79,143]{3,2,1,0}, f32[64,32,11,11]{3,2,1,0}), window={size=11x11 stride=9x10}, dim_labels=bf01_oi01->bf01, custom_call_target=\"__cudnn$convBackwardInput\", backend_config={\"operation_queue_id\":\"0\",\"wait_on_operation_queues\":[],\"cudnn_conv_backend_config\":{\"activation_mode\":\"kNone\",\"conv_result_scale\":1,\"side_input_scale\":0,\"leakyrelu_alpha\":0}} is taking a while...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3s/step - kl_loss: 4.4780e-04 - loss: 860.0314 - reconstruction_loss: 860.0309 " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "I0000 00:00:1721918331.535562 82761 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'input_reduce_select_fusion_2', 136 bytes spill stores, 136 bytes spill loads\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:38:57.834935: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng0{} for conv (f32[16,32,721,1440]{3,2,1,0}, u8[0]{0}) custom-call(f32[16,64,79,143]{3,2,1,0}, f32[64,32,11,11]{3,2,1,0}), window={size=11x11 stride=9x10}, dim_labels=bf01_oi01->bf01, custom_call_target=\"__cudnn$convBackwardInput\", backend_config={\"operation_queue_id\":\"0\",\"wait_on_operation_queues\":[],\"cudnn_conv_backend_config\":{\"activation_mode\":\"kNone\",\"conv_result_scale\":1,\"side_input_scale\":0,\"leakyrelu_alpha\":0}} is taking a while...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:39:14.861863: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 18.026990723s\n", + "Trying algorithm eng0{} for conv (f32[16,32,721,1440]{3,2,1,0}, u8[0]{0}) custom-call(f32[16,64,79,143]{3,2,1,0}, f32[64,32,11,11]{3,2,1,0}), window={size=11x11 stride=9x10}, dim_labels=bf01_oi01->bf01, custom_call_target=\"__cudnn$convBackwardInput\", backend_config={\"operation_queue_id\":\"0\",\"wait_on_operation_queues\":[],\"cudnn_conv_backend_config\":{\"activation_mode\":\"kNone\",\"conv_result_scale\":1,\"side_input_scale\":0,\"leakyrelu_alpha\":0}} is taking a while...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m133s\u001b[0m 7s/step - kl_loss: 4.4769e-04 - loss: 860.0184 - reconstruction_loss: 860.0179 - val_kl_loss: 4.4777e-04 - val_loss: 859.8553 - val_reconstruction_loss: 859.8549\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 2/50\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "\u001b[1m1/8\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 342ms/step - kl_loss: 4.4777e-04 - loss: 859.7046 - reconstruction_loss: 859.7042" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m2/8\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 236ms/step - kl_loss: 4.4817e-04 - loss: 859.6471 - reconstruction_loss: 859.6466" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/8\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 236ms/step - 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kl_loss: 5.0598e-04 - loss: 859.8282 - reconstruction_loss: 859.8278 - val_kl_loss: 5.0742e-04 - val_loss: 859.8666 - val_reconstruction_loss: 859.8661\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 19/50\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "\u001b[1m1/8\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 332ms/step - kl_loss: 5.0742e-04 - loss: 859.6811 - reconstruction_loss: 859.6806" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m2/8\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 236ms/step - kl_loss: 5.0731e-04 - loss: 859.7928 - reconstruction_loss: 859.7924" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/8\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 236ms/step - 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reconstruction_loss: 859.8207" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m6/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 235ms/step - kl_loss: 5.0728e-04 - loss: 859.8064 - reconstruction_loss: 859.8059" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m7/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 235ms/step - kl_loss: 5.0728e-04 - loss: 859.8033 - reconstruction_loss: 859.8029" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 258ms/step - kl_loss: 5.0728e-04 - loss: 859.8211 - reconstruction_loss: 859.8206 - val_kl_loss: 5.0995e-04 - val_loss: 859.8673 - val_reconstruction_loss: 859.8668\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:40:05.914039: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng0{} for conv (f32[32,32,721,1440]{3,2,1,0}, u8[0]{0}) custom-call(f32[32,64,79,143]{3,2,1,0}, f32[64,32,11,11]{3,2,1,0}), window={size=11x11 stride=9x10}, dim_labels=bf01_oi01->bf01, custom_call_target=\"__cudnn$convBackwardInput\", backend_config={\"operation_queue_id\":\"0\",\"wait_on_operation_queues\":[],\"cudnn_conv_backend_config\":{\"activation_mode\":\"kNone\",\"conv_result_scale\":1,\"side_input_scale\":0,\"leakyrelu_alpha\":0}} is taking a while...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-25 14:40:40.965805: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 36.051839104s\n", + "Trying algorithm eng0{} for conv (f32[32,32,721,1440]{3,2,1,0}, u8[0]{0}) custom-call(f32[32,64,79,143]{3,2,1,0}, f32[64,32,11,11]{3,2,1,0}), window={size=11x11 stride=9x10}, dim_labels=bf01_oi01->bf01, custom_call_target=\"__cudnn$convBackwardInput\", backend_config={\"operation_queue_id\":\"0\",\"wait_on_operation_queues\":[],\"cudnn_conv_backend_config\":{\"activation_mode\":\"kNone\",\"conv_result_scale\":1,\"side_input_scale\":0,\"leakyrelu_alpha\":0}} is taking a while...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "\u001b[1m1/3\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:35\u001b[0m 48s/step - kl_loss: 5.0865e-04 - loss: 860.0677 - reconstruction_loss: 860.0673" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m2/3\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - kl_loss: 5.0865e-04 - loss: 860.0721 - reconstruction_loss: 860.0717 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - kl_loss: 5.0865e-04 - loss: 860.1227 - reconstruction_loss: 860.1223" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m48s\u001b[0m 90ms/step - kl_loss: 5.0865e-04 - loss: 860.1481 - reconstruction_loss: 860.1476\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test loss: {'loss': 860.2235107421875, 'reconstruction_loss': 0.0005086498567834496, 'kl_loss': 860.2239990234375}\n", + "200320\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "!\r", + "If DVC froze, see `hardlink_lock` in <\u001b[36mhttps://man.dvc.org/config#core\u001b[39m>\r", + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[?25l\u001b[32m⠋\u001b[0m Checking graph\r\n", + "\u001b[?25h\r", + "\u001b[1A\u001b[2K\r", + "!\r", + " 0% Adding...| |0/1 [00:00\r", + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "!\r", + "Collecting |0.00 [00:00, ?entry/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "Collecting |11.0 [00:00, 102entry/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "Collecting |22.0 [00:00, 95.3entry/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "Collecting |23.0 [00:00, 91.1entry/s]\r\n", + "\r", + "!\r", + "Pushing\r\n", + "\r", + "!\u001b[A\r\n", + "\r", + " 0% Checking cache in '/aws-dvc-bucket/files/md5'| |0/? [00:00\r\n", + "Your name and email address were configured automatically based\r\n", + "on your username and hostname. Please check that they are accurate.\r\n", + "You can suppress this message by setting them explicitly. Run the\r\n", + "following command and follow the instructions in your editor to edit\r\n", + "your configuration file:\r\n", + "\r\n", + " git config --global --edit\r\n", + "\r\n", + "After doing this, you may fix the identity used for this commit with:\r\n", + "\r\n", + " git commit --amend --reset-author\r\n", + "\r\n", + " 1 file changed, 5 insertions(+)\r\n", + " create mode 100644 run_on_cluster/cvae-weather-ensemble/model_dir/history_200320.csv.dvc\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: Permanently added 'github.com,140.82.114.3' (ECDSA) to the list of known hosts.\r", + "\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enumerating objects: 10, done.\r\n", + "Counting objects: 10% (1/10)\r", + "Counting objects: 20% (2/10)\r", + "Counting objects: 30% (3/10)\r", + "Counting objects: 40% (4/10)\r", + "Counting objects: 50% (5/10)\r", + "Counting objects: 60% (6/10)\r", + "Counting objects: 70% (7/10)\r", + "Counting objects: 80% (8/10)\r", + "Counting objects: 90% (9/10)\r", + "Counting objects: 100% (10/10)\r", + "Counting objects: 100% (10/10), done.\r\n", + "Delta compression using up to 4 threads\r\n", + "Compressing objects: 16% (1/6)\r", + "Compressing objects: 33% (2/6)\r", + "Compressing objects: 50% (3/6)\r", + "Compressing objects: 66% (4/6)\r", + "Compressing objects: 83% (5/6)\r", + "Compressing objects: 100% (6/6)\r", + "Compressing objects: 100% (6/6), done.\r\n", + "Writing objects: 16% (1/6)\r", + "Writing objects: 33% (2/6)\r", + "Writing objects: 50% (3/6)\r", + "Writing objects: 66% (4/6)\r", + "Writing objects: 83% (5/6)\r", + "Writing objects: 100% (6/6)\r", + "Writing objects: 100% (6/6), 605 bytes | 605.00 KiB/s, done.\r\n", + "Total 6 (delta 4), reused 0 (delta 0), pack-reused 0\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "remote: Resolving deltas: 0% (0/4)\u001b[K\r", + "remote: Resolving deltas: 25% (1/4)\u001b[K\r", + "remote: Resolving deltas: 50% (2/4)\u001b[K\r", + "remote: Resolving deltas: 75% (3/4)\u001b[K\r", + "remote: Resolving deltas: 100% (4/4)\u001b[K\r", + "remote: Resolving deltas: 100% (4/4), completed with 4 local objects.\u001b[K\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "To github.com:oobielodan/parsl_mpi.git\r\n", + " 18e8707..82f1e98 main -> main\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "!\r", + "If DVC froze, see `hardlink_lock` in <\u001b[36mhttps://man.dvc.org/config#core\u001b[39m>\r", + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[?25l\u001b[32m⠋\u001b[0m Checking graph\r\n", + "\u001b[?25h\r", + "\u001b[1A\u001b[2K\r", + "!\r", + " 0% Adding...| |0/1 [00:00\r", + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "!\r", + "Collecting |0.00 [00:00, ?entry/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "Collecting |9.00 [00:00, 88.1entry/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "Collecting |18.0 [00:00, 85.5entry/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "Collecting |24.0 [00:00, 82.1entry/s]\r\n", + "\r", + "!\r", + "Pushing" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r\n", + "\r", + "!\u001b[A\r\n", + "\r", + " 0% Checking cache in '/aws-dvc-bucket/files/md5'| |0/? [00:00\r\n", + "Your name and email address were configured automatically based\r\n", + "on your username and hostname. Please check that they are accurate.\r\n", + "You can suppress this message by setting them explicitly. Run the\r\n", + "following command and follow the instructions in your editor to edit\r\n", + "your configuration file:\r\n", + "\r\n", + " git config --global --edit\r\n", + "\r\n", + "After doing this, you may fix the identity used for this commit with:\r\n", + "\r\n", + " git commit --amend --reset-author\r\n", + "\r\n", + " 1 file changed, 5 insertions(+)\r\n", + " create mode 100644 run_on_cluster/cvae-weather-ensemble/model_dir/vae.weights_200320.h5.dvc\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: Permanently added 'github.com,140.82.114.3' (ECDSA) to the list of known hosts.\r", + "\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enumerating objects: 10, done.\r\n", + "Counting objects: 10% (1/10)\r", + "Counting objects: 20% (2/10)\r", + "Counting objects: 30% (3/10)\r", + "Counting objects: 40% (4/10)\r", + "Counting objects: 50% (5/10)\r", + "Counting objects: 60% (6/10)\r", + "Counting objects: 70% (7/10)\r", + "Counting objects: 80% (8/10)\r", + "Counting objects: 90% (9/10)\r", + "Counting objects: 100% (10/10)\r", + "Counting objects: 100% (10/10), done.\r\n", + "Delta compression using up to 4 threads\r\n", + "Compressing objects: 16% (1/6)\r", + "Compressing objects: 33% (2/6)\r", + "Compressing objects: 50% (3/6)\r", + "Compressing objects: 66% (4/6)\r", + "Compressing objects: 83% (5/6)\r", + "Compressing objects: 100% (6/6)\r", + "Compressing objects: 100% (6/6), done.\r\n", + "Writing objects: 16% (1/6)\r", + "Writing objects: 33% (2/6)\r", + "Writing objects: 50% (3/6)\r", + "Writing objects: 66% (4/6)\r", + "Writing objects: 83% (5/6)\r", + "Writing objects: 100% (6/6)\r", + "Writing objects: 100% (6/6), 597 bytes | 597.00 KiB/s, done.\r\n", + "Total 6 (delta 4), reused 0 (delta 0), pack-reused 0\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "remote: Resolving deltas: 0% (0/4)\u001b[K\r", + "remote: Resolving deltas: 25% (1/4)\u001b[K\r", + "remote: Resolving deltas: 50% (2/4)\u001b[K\r", + "remote: Resolving deltas: 75% (3/4)\u001b[K\r", + "remote: Resolving deltas: 100% (4/4)\u001b[K\r", + "remote: Resolving deltas: 100% (4/4), completed with 4 local objects.\u001b[K\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "To github.com:oobielodan/parsl_mpi.git\r\n", + " 82f1e98..b816144 main -> main\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Memory usage after training: {'current': 6979584, 'peak': 18227546112}\n" + ] + } + ], + "source": [ + "# training\n", + "\n", + "num_files = 0\n", + "\n", + "# get wanted data -------------------------------------------------------------------------\n", + "for month in months:\n", + " for ensemble in ensembles:\n", + " download_file(year, month, day, ensemble, data_pdir)\n", + " convert_file(year, month, day, ensemble, data_dir)\n", + " subset_file(f'pres_msl_{year}{month}{day}00_{ensemble}.nc', data_dir)\n", + "\n", + " if f'pres_msl_{year}{month}{day}00_{ensemble}.nc' in os.listdir(data_dir):\n", + " num_files += 1\n", + "# ------------------------------------------------------------------------------------------ \n", + " \n", + "run_train(num_files, year + day, vae, data_dir, model_dir) # run training \n", + " \n", + "print(\"Memory usage after training:\", tf.config.experimental.get_memory_info('GPU:0'))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:cvae_env]", + "language": "python", + "name": "conda-env-cvae_env-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + }, + "papermill": { + "default_parameters": {}, + "duration": 457.414216, + "end_time": "2024-07-25T14:40:59.958587", + "environment_variables": {}, + "exception": null, + "input_path": "cvae_training.ipynb", + "output_path": "cvae_log.ipynb", + "parameters": { + "day": "20", + "year": "2003" + }, + "start_time": "2024-07-25T14:33:22.544371", + "version": "2.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/run_on_cluster/cvae-weather-ensemble/CVAE_example.ipynb b/run_on_cluster/cvae-weather-ensemble/cvae_runner_and_example.ipynb similarity index 78% rename from run_on_cluster/cvae-weather-ensemble/CVAE_example.ipynb rename to run_on_cluster/cvae-weather-ensemble/cvae_runner_and_example.ipynb index d1c1b21..36bf500 100644 --- a/run_on_cluster/cvae-weather-ensemble/CVAE_example.ipynb +++ b/run_on_cluster/cvae-weather-ensemble/cvae_runner_and_example.ipynb @@ -6,41 +6,9 @@ "tags": [] }, "source": [ - "# **Demo using CVAE on CORE2 data**\n", - "This is based on an example from Keras.io by fchollet. Please see last cell for original code and links.\n", + "# **CVAE Weather Ensemble Model**\n", "\n", - "# Conda env Setup\n", - "The following is the list steps needed to be taken in order to set up a correct conda environment for running this notebook.\n", - "\n", - "**Create Environment:**\n", - "```bash\n", - "source ~/pw/.miniconda3c/etc/profile.d/conda.sh\n", - "conda create --name NAME python=3.9\n", - "conda activate NAME\n", - "```\n", - "\n", - "**Install Pacakages:** \n", - "\n", - "The following packages need to be installed on top of a typical base Conda env. Install the packages in the following order so the environment solves correctly:\n", - "```bash\n", - "conda install -y -c conda-forge tensorflow\n", - "conda install -y -c conda-forge netCDF4 # For reading nc files\n", - "conda install -y -c conda-forge cartopy # For making maps\n", - "conda install -y -c conda-forge matplotlib\n", - "conda install -y -c conda-forge pandas\n", - "conda install -y -c conda-forge scikit-learn\n", - "conda install -y -c conda-forge papermill\n", - "conda install -y -c conda-forge dvc\n", - "conda install -y -c conda-forge nco\n", - "conda install -y -c conda-forge cdo # For converting grib to nc\n", - "```\n", - "**Connect Notebook to Environment:**\n", - "```bash\n", - "conda install -y ipykernel\n", - "conda install -y requests\n", - "conda install -y -c anaconda jinja2\n", - "python -m ipykernel install --user --name=NAME --display-name \"Python (NAME)\"\n", - "```" + "This notebook provides examples for working with weather data along with a section to launch online learning." ] }, { @@ -99,6 +67,7 @@ }, "outputs": [], "source": [ + "# DVC initialization and storage set up\n", "!dvc init --subdir\n", "!dvc remote add -d dvcstorage /aws-dvc-bucket" ] @@ -106,10 +75,12 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "# make DVC initialization commit\n", + "# initial commit to git\n", "!git add .\n", "!git commit -m \"loaded dependencies, mkdir -p, DVC init\"" ] @@ -119,7 +90,7 @@ "metadata": {}, "source": [ "# Download and Convert Data\n", - "On my [first Google hit for GEFS](https://www.ncei.noaa.gov/products/weather-climate-models/global-ensemble-forecast), I clicked on [AWS Open Data Registry for GEFS](https://registry.opendata.aws/noaa-gefs-pds/) and selected [NOAA GEFS Re-forecast](https://registry.opendata.aws/noaa-gefs-reforecast/) which has no useage restrictions. The [GEFS Re-forecast data documentation](https://noaa-gefs-retrospective.s3.amazonaws.com/Description_of_reforecast_data.pdf) is very clear and we're going to download two files, 57 MB each. The date of the initialization of the re-forecast is in the file name in the format YYYYMMDDHH. The c00, p01, p02, p03, p04 are the control and perturbation ensemble members (5 total)." + "On my [first Google hit for GEFS](https://www.ncei.noaa.gov/products/weather-climate-models/global-ensemble-forecast), I clicked on [AWS Open Data Registry for GEFS](https://registry.opendata.aws/noaa-gefs-pds/) and selected [NOAA GEFS Re-forecast](https://registry.opendata.aws/noaa-gefs-reforecast/) which has no useage restrictions. The [GEFS Re-forecast data documentation](https://noaa-gefs-retrospective.s3.amazonaws.com/Description_of_reforecast_data.pdf) is very clear. The date of the initialization of the re-forecast is in the file name in the format YYYYMMDDHH. The c00, p01, p02, p03, p04 are the control and perturbation ensemble members (5 total)." ] }, { @@ -152,7 +123,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Examples:" + "## Examples" ] }, { @@ -163,6 +134,7 @@ }, "outputs": [], "source": [ + "# example parameters\n", "ex_year = \"2018\"\n", "ex_month = \"01\"\n", "ex_day = \"01\"\n", @@ -179,7 +151,7 @@ "source": [ "# example for getting and converting files \n", "download_file(ex_year, ex_month, ex_day, ex_ensemble, data_pdir)\n", - "convert_file(ex_year, ex_month, ex_day, ex_ensemble, data_pdir)" + "convert_file(ex_year, ex_month, ex_day, ex_ensemble, data_dir)" ] }, { @@ -244,7 +216,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Execute Online Training" + "# Execute Online Training\n", + "\n", + "*add a note about how datat is randomized, sorted, and stuff*" ] }, { @@ -268,11 +242,10 @@ "outputs": [], "source": [ "# parameters for pm -> change the lists to fit your needs\n", - "years = [\"2000\", \"2001\", \"2002\", \"2003\", \"2004\", \"2005\", \"2006\"] # , \"2007\", \"2008\", \"2009\", \n", - " # \"2010\", \"2011\", \"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\"]\n", - "# months = [\"01\", \"02\", \"03\", \"04\", \"05\", \"06\", \"07\", \"08\", \"09\", \"10\", \"11\", \"12\"] \n", - "days = [\"01\", \"02\", \"03\", \"04\", \"05\", \"06\", \"07\", \"08\", \"09\", \"10\", \"11\", \"12\", \"13\", \"14\", \"15\", \n", - " \"16\", \"17\", \"18\", \"19\", \"20\", \"21\", \"22\", \"23\", \"24\", \"25\", \"26\", \"27\", \"28\", \"29\", \"30\", \"31\"] # forecasts are 10 days long, so (01, 10, 20) provides converage of whole year." + "years = [\"2000\", \"2001\", \"2002\", \"2003\", \"2004\", \"2005\", \"2006\", \"2007\", \"2008\", \"2009\", \n", + " \"2010\", \"2011\", \"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\"]\n", + "days = [\"01\", \"10\", \"20\" \"02\", \"03\", \"04\", \"05\", \"06\", \"07\", \"08\", \"09\", \"10\", \"11\", \"12\", \"13\", \"14\", \"15\", \n", + " \"16\", \"17\", \"18\", \"19\", \"20\", \"21\", \"22\", \"23\", \"24\", \"25\", \"26\", \"27\", \"28\", \"29\", \"30\", \"31\"]" ] }, { @@ -286,10 +259,10 @@ "for y in years:\n", " for d in days:\n", " pm.execute_notebook(\n", - " 'CVAE_training.ipynb',\n", - " 'CVAE_log.ipynb',\n", + " 'cvae_training.ipynb',\n", + " 'cvae_log.ipynb',\n", " parameters = dict(year = y, day = d),\n", - " kernel_name = 'cvae_env' \n", + " kernel_name = 'cvae_env' # this should be changed to whatever you chose to be during environment set up\n", " )" ] }, diff --git a/run_on_cluster/cvae-weather-ensemble/cvae_training.ipynb b/run_on_cluster/cvae-weather-ensemble/cvae_training.ipynb new file mode 100644 index 0000000..414a099 --- /dev/null +++ b/run_on_cluster/cvae-weather-ensemble/cvae_training.ipynb @@ -0,0 +1,442 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f6226ca5-0869-4973-8369-3469cbdff43c", + "metadata": {}, + "source": [ + "# **CVAE Training**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "094dca89-dffa-4a56-8263-371fb372a2cb", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import os, json\n", + "\n", + "import papermill as pm\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import tensorflow as tf\n", + "import netCDF4\n", + "import cartopy\n", + "\n", + "from tensorflow import keras\n", + "from keras import layers\n", + "from sklearn.model_selection import train_test_split \n", + "\n", + "print(\"TF version:\", tf.__version__)\n", + "print(\"GPU is\", \"available\" if tf.config.list_physical_devices('GPU') else \"NOT AVAILABLE\")" + ] + }, + { + "cell_type": "markdown", + "id": "06a8b533-cdaa-4efd-938b-ef1d5a84338e", + "metadata": {}, + "source": [ + "# Download and Convert Data" + ] + }, + { + "cell_type": "markdown", + "id": "412796a9-48ba-4a38-b8ae-d1ef35f1fab5", + "metadata": {}, + "source": [ + "### How we cut down the data\n", + "\n", + "1. In the `subset_file` function from the `get_data` library, we used the `ncks -v -d ,,, infile.nc -O subset_infile.nc` command to grab every other time step from the original data file. \n", + "2. In the `load_data` function, we grab the subseted, converted files and concatenate them together to form one dataset before implementing min/max normalization scaling and casting the DataFrame to `float16`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4b08e8b0-f9fa-40b9-852b-8650e681cff2", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "data_pdir = \"./gefs_data\"\n", + "data_dir = \"./gefs_data/converted/\"\n", + "model_dir = './model_dir'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ff4403c-9c45-4129-902b-93ebfe9749e3", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# data definitions\n", + "\n", + "from scripts.get_data import download_file\n", + "from scripts.get_data import convert_file\n", + "from scripts.get_data import subset_file\n", + "from scripts.get_data import remove_data # removes all data\n", + "\n", + "# data loading\n", + "def load_data(data_dir): \n", + " files = [f for f in os.listdir(data_dir) if ('subset' in f and 'tmp' not in f)] # get th list of subseted and converted files from the data directory\n", + " \n", + " all_data = ((np.expand_dims(\n", + " np.concatenate(\n", + " [netCDF4.Dataset(data_dir + converted_file)['msl'][:] for converted_file in files] # grab the msl data from each of the files\n", + " ),\n", + " -1\n", + " ).astype(\"float32\") - 85000) / (110000 - 85000)).astype(\"float16\") # min/max normalization scaling and data casting\n", + " print(all_data)\n", + " return all_data" + ] + }, + { + "cell_type": "markdown", + "id": "d03ebb2a-bc7b-4f52-98d3-ec85c51c7a5f", + "metadata": { + "tags": [] + }, + "source": [ + "# Neural Network Design\n", + "\n", + "We need to get to a small latent space. Conv2D networks are good because they help reduce the number of connections in a network in a meaningful way. I'm using terms as defined in [this definition of conv2D](https://towardsdatascience.com/conv2d-to-finally-understand-what-happens-in-the-forward-pass-1bbaafb0b148).\n", + "\n", + "**Definitions:**\n", + "K -> kernel size;\n", + "P -> padding;\n", + "S -> stride;\n", + "D -> Dilation;\n", + "G -> Groups\n", + "\n", + "**Filter options:**\n", + "Longitude is easy because it is large and even, so as long as you have an even stride, you get integer results when dividing.\n", + "e.g. lon 9: stride 4, lat 7: stride 5\n", + "\n", + "- Latitude - whole numbers occurr for P = 2 & K = 3 or K = 11.\n", + "- 11 grid points * 0.25 deg * 100 km/deg = 275 km filter window (a good scale for weather)\n", + "- 9 grid points * 0.25 deg * 100 km/deg = 225 km\n", + "- Longitude - whole numbers occur for P = 0 & K = 11 (nice match with Latitude), P = 1 & K = 3 or 13, P = 2 & K = 5.\n", + "\n", + "For a 5 x 7 filter with 3 stride (no overlap) and no padding:\n", + "- lat: (721 - 4) / 3 = 239 possible steps (good whole number!)\n", + "- lon: (1440 - 4) / 3 = 478.6666 possible steps\n", + "\n", + "## Load and Preprocess Training Data:\n", + "The standard way of manipulating arrays in Conv2D layers in TF is to use arrays in the shape:\n", + "`batch_size, height, width, channels = data.shape`\n", + "In our case, the the `batch_size` is the number of image frames (i.e. separate samples or rows in a `.csv` file), the `height` and `width` define the size of the image frame in number of pixels, and the `channels` are the number of layers in the frames. Typically, channels are color layers (e.g. RGB or CMYK) but in our case, we could use different metereological variables. However, for this first experiment, **we only need one channel** because we're only going to use mean sea level pressure (msl).\n", + "\n", + "## Build the Encoder:\n", + "GFS grids I have available here are at 0.25 degree resolution. I'm doing this as a \"worst case\" scenario since there are also 0.5 and 1.0 degree grids with lower resolution but I can't find that data quickly and don't know what's available.\n", + "\n", + "These 0.25 degree grids are 721 x 1440.\n", + "Each forecast file is 3 hourly for 10 days = 8 steps/forecast * 10 days = 80 \"frames\"\n", + "This demo is only using two forecasts from the control ensemble\n", + "(one launched Jan 01, 2019 and one launched Jan 02, 2019) -> this is only \n", + "a small subset of the variability possible in the model.\n", + "\n", + "This particular data set spans 2000-2019 and there are 5 ensemble members.\n", + "\n", + "## Build the Decoder:\n", + "With the 11 x 11 and 5 x 5 filters, non-overlapping stride, applied here, we have a final \"image\" size of 14 x 27 and 64 channels." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f0144303-f1c9-4220-8178-3988d707688c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# model defintions\n", + "\n", + "class Sampling(layers.Layer):\n", + " \"\"\"Uses (z_mean, z_log_var) to sample z, the vector encoding a digit.\"\"\"\n", + "\n", + " def call(self, inputs):\n", + " z_mean, z_log_var = inputs\n", + " batch = tf.shape(z_mean)[0]\n", + " dim = tf.shape(z_mean)[1]\n", + " epsilon = tf.keras.backend.random_normal(shape = (batch, dim))\n", + " return z_mean + tf.exp(0.5 * z_log_var) * epsilon\n", + "\n", + "def build_encoder(latent_dim):\n", + " encoder_inputs = keras.Input(shape=(721, 1440, 1))\n", + " \n", + " x = layers.Conv2D(32, 11, activation=\"relu\", strides=[9, 10], padding=\"valid\")(encoder_inputs)\n", + " x = layers.Conv2D(64, [5,9], activation=\"relu\", strides=[5, 9], padding=\"valid\")(x)\n", + " x = layers.Flatten()(x)\n", + " x = layers.Dense(16, activation=\"relu\")(x)\n", + " \n", + " z_mean = layers.Dense(latent_dim, name=\"z_mean\")(x)\n", + " z_log_var = layers.Dense(latent_dim, name=\"z_log_var\")(x)\n", + " z = Sampling()([z_mean, z_log_var])\n", + " \n", + " encoder = keras.Model(encoder_inputs, [z_mean, z_log_var, z], name=\"encoder\")\n", + " \n", + " print(encoder.summary())\n", + " return encoder\n", + "\n", + "def build_decoder(latent_dim):\n", + " latent_inputs = keras.Input(shape=(latent_dim,))\n", + " \n", + " x = layers.Dense(15 * 15 * 64, activation=\"relu\")(latent_inputs)\n", + " x = layers.Reshape((15, 15, 64))(x)\n", + " # FIXME - there is something wrong here, but at least there is a pattern.\n", + " # Using output_padding as a fudge factor -> it may be that there is exactly\n", + " # one \"missing\" filter stamp/convolution because for both Conv2DTranspose\n", + " # operations, output_padding is set to maximum it could be in both dims\n", + " # (i.e. exactly one less than the stride of each filter).\n", + " x = layers.Conv2DTranspose(64, [5, 9], activation=\"relu\", strides=[5,9], padding=\"valid\", output_padding=[4, 8])(x)\n", + " x = layers.Conv2DTranspose(32, 11, activation=\"relu\", strides=[9,10], padding=\"valid\", output_padding=[8, 9])(x)\n", + " \n", + " decoder_outputs = layers.Conv2DTranspose(1, 3, activation=\"sigmoid\", padding=\"same\")(x)\n", + " decoder = keras.Model(latent_inputs, decoder_outputs, name=\"decoder\")\n", + " \n", + " print(decoder.summary())\n", + " return decoder\n", + "\n", + "class VAE(keras.Model):\n", + " def __init__(self, encoder, decoder, **kwargs):\n", + " super(VAE, self).__init__(**kwargs)\n", + " self.encoder = encoder\n", + " self.decoder = decoder\n", + " self.total_loss_tracker = keras.metrics.Mean(name = \"total_loss\")\n", + " self.reconstruction_loss_tracker = keras.metrics.Mean(name = \"reconstruction_loss\")\n", + " self.kl_loss_tracker = keras.metrics.Mean(name = \"kl_loss\")\n", + "\n", + " @property\n", + " def metrics(self):\n", + " return [\n", + " self.total_loss_tracker,\n", + " self.reconstruction_loss_tracker,\n", + " self.kl_loss_tracker,\n", + " ]\n", + "\n", + " def train_step(self, data):\n", + " \n", + " with tf.GradientTape() as tape:\n", + " z_mean, z_log_var, z = self.encoder(data)\n", + " reconstruction = self.decoder(z)\n", + " # FIXME: Normalize loss with the number of features (28 * 28)\n", + " n_features = 28 * 28\n", + " reconstruction_loss = tf.reduce_mean(\n", + " tf.reduce_sum(\n", + " keras.losses.binary_crossentropy(data, reconstruction), axis = (1, 2)\n", + " )\n", + " ) / n_features\n", + " kl_loss = -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var))\n", + " kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis = 1)) / n_features\n", + " total_loss = (reconstruction_loss + kl_loss)\n", + " grads = tape.gradient(total_loss, self.trainable_weights)\n", + " self.optimizer.apply_gradients(zip(grads, self.trainable_weights))\n", + " self.total_loss_tracker.update_state(total_loss)\n", + " self.reconstruction_loss_tracker.update_state(reconstruction_loss)\n", + " self.kl_loss_tracker.update_state(kl_loss)\n", + " \n", + " return {\n", + " \"loss\": self.total_loss_tracker.result(),\n", + " \"reconstruction_loss\": self.reconstruction_loss_tracker.result(),\n", + " \"kl_loss\": self.kl_loss_tracker.result(),\n", + " }\n", + "\n", + " # Needed to validate (validation loss) and to evaluate\n", + " def test_step(self, data):\n", + " if type(data) == tuple:\n", + " data, _ = data\n", + " \n", + " z_mean, z_log_var, z = self.encoder(data)\n", + " reconstruction = self.decoder(z)\n", + " # FIXME: Normalize loss with the number of features (28 * 28)\n", + " n_features = 28 * 28\n", + " reconstruction_loss = tf.reduce_mean(\n", + " tf.reduce_sum(\n", + " keras.losses.binary_crossentropy(data, reconstruction), axis = (1, 2)\n", + " )\n", + " ) / n_features\n", + " kl_loss = -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var))\n", + " kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis = 1)) / n_features\n", + " total_loss = (reconstruction_loss + kl_loss)\n", + " # grads = tape.gradient(total_loss, self.trainable_weights)\n", + " # self.optimizer.apply_gradients(zip(grads, self.trainable_weights))\n", + " self.total_loss_tracker.update_state(total_loss)\n", + " self.reconstruction_loss_tracker.update_state(reconstruction_loss)\n", + " self.kl_loss_tracker.update_state(kl_loss)\n", + " \n", + " return {\n", + " \"loss\": self.total_loss_tracker.result(),\n", + " \"reconstruction_loss\": self.reconstruction_loss_tracker.result(),\n", + " \"kl_loss\": self.kl_loss_tracker.result(),\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a61f638f-42a9-4d44-8f31-c6c42017a6bf", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# training definitions\n", + "\n", + "def train_model(X_train, X_test, X_valid, date, vae, model_dir):\n", + " early_stopping_cb = keras.callbacks.EarlyStopping(patience = 5, restore_best_weights = True) # stops training early if the validation loss does not improve\n", + " \n", + " if os.path.exists(os.path.join(model_dir, 'vae.weights.h5')): # if the model has already been trained at least once, load that model\n", + " vae.load_weights(os.path.join(model_dir, 'vae.weights.h5'))\n", + "\n", + " history = vae.fit(\n", + " X_train, epochs = 50, batch_size = 40,\n", + " callbacks = [early_stopping_cb],\n", + " validation_data = (X_valid,)\n", + " )\n", + "\n", + " vae.save_weights(os.path.join(model_dir, 'vae.weights.h5')) # save model weights after training\n", + " !cp model_dir/vae.weights.h5 model_dir/vae.weights_{date}.h5 # make a copy to dvc save\n", + " \n", + " hist_pd = pd.DataFrame(history.history)\n", + " hist_pd.to_csv(os.path.join(model_dir, f'history_{date}.csv'), index = False)\n", + "\n", + " test_loss = vae.evaluate(X_test)\n", + " test_loss = dict(zip([\"loss\", \"reconstruction_loss\", \"kl_loss\"], test_loss))\n", + "\n", + " print('Test loss:', test_loss)\n", + "\n", + " with open(os.path.join(model_dir, f'test_loss_{date}.json'), 'w') as json_file:\n", + " json.dump(test_loss, json_file, indent = 4)\n", + " \n", + " print(date)\n", + " !sh scripts/run_dvcgit.sh model_dir/history_{date}.csv f\"{date}\"\n", + " !sh scripts/run_dvcgit.sh model_dir/vae.weights_{date}.h5 f\"{date}\"\n", + " !rm model_dir/vae.weights_{date}.h5 # delete copy\n", + " \n", + "def run_train(num_files, date, vae, data_dir, model_dir):\n", + " slp = load_data(data_dir) # load data\n", + " print(\"shape:\", np.shape(slp)) # verify data shape\n", + " \n", + " # split the data - y values are throw away\n", + " X_train, X_test, y_train, y_test = train_test_split(slp[0:(num_files * 40 - 1), :, :, :], np.arange(0, num_files * 40 - 1), test_size = 0.2, random_state = 1)\n", + " X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size = 0.25, random_state = 1) # 0.25 x 0.8 = 0.2\n", + "\n", + " train_model(X_train, X_test, X_valid, date, vae, model_dir)\n", + " remove_data(data_pdir)" + ] + }, + { + "cell_type": "markdown", + "id": "3defdd05-1774-4e56-bd64-2f760c004842", + "metadata": {}, + "source": [ + "# Train the VAE model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3dd6a16c-2ff9-42fe-bdcd-b8a970f88b9f", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# model build\n", + "\n", + "latent_dim = 2\n", + "\n", + "# build encoder\n", + "encoder = build_encoder(latent_dim)\n", + "print(\"Memory usage after building encoder:\", tf.config.experimental.get_memory_info('GPU:0'))\n", + "\n", + "# build decoder\n", + "decoder = build_decoder(latent_dim)\n", + "print(\"Memory usage after building decoder:\", tf.config.experimental.get_memory_info('GPU:0'))\n", + "\n", + "# build VAE (variational autoencoder)\n", + "vae = VAE(encoder, decoder)\n", + "vae.compile(optimizer = 'rmsprop') \n", + "print(\"Memory usage after building VAE:\", tf.config.experimental.get_memory_info('GPU:0'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2c79096d-dee9-4ffc-9279-442e9232f20f", + "metadata": { + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [ + "# parameter cell for pm \n", + "year = \"2018\"\n", + "day = \"01\"\n", + "months = [\"01\", \"02\", \"03\", \"04\", \"05\", \"06\", \"07\", \"08\", \"09\", \"10\", \"11\", \"12\"] \n", + "ensembles = [\"c00\"] #, \"p01\", \"p02\", \"p03\", \"p04\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b952473d-6ec6-4581-8b49-d6d472b4e0cb", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# training\n", + "\n", + "num_files = 0\n", + "\n", + "# get wanted data -------------------------------------------------------------------------\n", + "for month in months:\n", + " for ensemble in ensembles:\n", + " download_file(year, month, day, ensemble, data_pdir)\n", + " convert_file(year, month, day, ensemble, data_dir)\n", + " subset_file(f'pres_msl_{year}{month}{day}00_{ensemble}.nc', data_dir)\n", + "\n", + " if f'pres_msl_{year}{month}{day}00_{ensemble}.nc' in os.listdir(data_dir):\n", + " num_files += 1\n", + "# ------------------------------------------------------------------------------------------ \n", + " \n", + "run_train(num_files, year + day, vae, data_dir, model_dir) # run training \n", + " \n", + "print(\"Memory usage after training:\", tf.config.experimental.get_memory_info('GPU:0'))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:cvae_env]", + "language": "python", + "name": "conda-env-cvae_env-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/run_on_cluster/cvae-weather-ensemble/model_dir/.gitignore b/run_on_cluster/cvae-weather-ensemble/model_dir/.gitignore new file mode 100644 index 0000000..4edbf72 --- /dev/null +++ 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b/run_on_cluster/cvae-weather-ensemble/model_dir/history_200310.csv.dvc @@ -0,0 +1,5 @@ +outs: +- md5: ab264202ec5e33269bf7d12435e900c7 + size: 872 + hash: md5 + path: history_200310.csv diff --git a/run_on_cluster/cvae-weather-ensemble/model_dir/history_200320.csv.dvc b/run_on_cluster/cvae-weather-ensemble/model_dir/history_200320.csv.dvc new file mode 100644 index 0000000..54d5382 --- /dev/null +++ b/run_on_cluster/cvae-weather-ensemble/model_dir/history_200320.csv.dvc @@ -0,0 +1,5 @@ +outs: +- md5: 203efab769e207c58674b9d5fef6ea11 + size: 2240 + hash: md5 + path: history_200320.csv diff --git a/run_on_cluster/cvae-weather-ensemble/model_dir - 07_08_24/.ipynb_checkpoints/history_20180701-checkpoint.csv b/run_on_cluster/cvae-weather-ensemble/model_dir/models-one-day-all-ensembles/.ipynb_checkpoints/history_20180701-checkpoint.csv similarity index 100% rename from run_on_cluster/cvae-weather-ensemble/model_dir - 07_08_24/.ipynb_checkpoints/history_20180701-checkpoint.csv rename to 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07_08_24/vae.weights.h5.backup rename to run_on_cluster/cvae-weather-ensemble/model_dir/models-one-day-all-ensembles/vae.weights.h5.backup diff --git a/run_on_cluster/cvae-weather-ensemble/model_dir/test_loss_200001.json b/run_on_cluster/cvae-weather-ensemble/model_dir/test_loss_200001.json new file mode 100644 index 0000000..1c15e5b --- /dev/null +++ b/run_on_cluster/cvae-weather-ensemble/model_dir/test_loss_200001.json @@ -0,0 +1,5 @@ +{ + "loss": 860.30126953125, + "reconstruction_loss": 1.8108334188582376e-05, + "kl_loss": 860.30126953125 +} \ No newline at end of file diff --git a/run_on_cluster/cvae-weather-ensemble/model_dir/test_loss_200010.json b/run_on_cluster/cvae-weather-ensemble/model_dir/test_loss_200010.json new file mode 100644 index 0000000..0292304 --- /dev/null +++ b/run_on_cluster/cvae-weather-ensemble/model_dir/test_loss_200010.json @@ -0,0 +1,5 @@ +{ + "loss": 859.1036376953125, + "reconstruction_loss": 5.743626024923287e-05, + "kl_loss": 859.1036987304688 +} \ No newline at end of file diff --git a/run_on_cluster/cvae-weather-ensemble/model_dir/test_loss_200020.json b/run_on_cluster/cvae-weather-ensemble/model_dir/test_loss_200020.json new file mode 100644 index 0000000..8857a1e --- /dev/null +++ b/run_on_cluster/cvae-weather-ensemble/model_dir/test_loss_200020.json @@ -0,0 +1,5 @@ +{ + "loss": 860.1217041015625, + "reconstruction_loss": 8.654439443489537e-05, + "kl_loss": 860.1217651367188 +} \ No newline at end of file diff --git a/run_on_cluster/cvae-weather-ensemble/model_dir/test_loss_200101.json b/run_on_cluster/cvae-weather-ensemble/model_dir/test_loss_200101.json new file mode 100644 index 0000000..7edd4d6 --- /dev/null +++ b/run_on_cluster/cvae-weather-ensemble/model_dir/test_loss_200101.json @@ -0,0 +1,5 @@ +{ + "loss": 860.9672241210938, + "reconstruction_loss": 0.00011506733426358551, + "kl_loss": 860.96728515625 +} \ No newline at end of file diff --git a/run_on_cluster/cvae-weather-ensemble/model_dir/test_loss_200110.json 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--- /dev/null +++ b/run_on_cluster/cvae-weather-ensemble/model_dir/test_loss_200201.json @@ -0,0 +1,5 @@ +{ + "loss": 860.3161010742188, + "reconstruction_loss": 0.00025777178234420717, + "kl_loss": 860.3163452148438 +} \ No newline at end of file diff --git a/run_on_cluster/cvae-weather-ensemble/model_dir/test_loss_200210.json b/run_on_cluster/cvae-weather-ensemble/model_dir/test_loss_200210.json new file mode 100644 index 0000000..427566d --- /dev/null +++ b/run_on_cluster/cvae-weather-ensemble/model_dir/test_loss_200210.json @@ -0,0 +1,5 @@ +{ + "loss": 860.798828125, + "reconstruction_loss": 0.00029538158560171723, + "kl_loss": 860.7991943359375 +} \ No newline at end of file diff --git a/run_on_cluster/cvae-weather-ensemble/model_dir/test_loss_200220.json b/run_on_cluster/cvae-weather-ensemble/model_dir/test_loss_200220.json new file mode 100644 index 0000000..175eb8c --- /dev/null +++ b/run_on_cluster/cvae-weather-ensemble/model_dir/test_loss_200220.json @@ -0,0 +1,5 @@ +{ + "loss": 859.9513549804688, + "reconstruction_loss": 0.00035312824184075, + "kl_loss": 859.95166015625 +} \ No newline at end of file diff --git a/run_on_cluster/cvae-weather-ensemble/model_dir/test_loss_200301.json b/run_on_cluster/cvae-weather-ensemble/model_dir/test_loss_200301.json new file mode 100644 index 0000000..4fb503b --- /dev/null +++ b/run_on_cluster/cvae-weather-ensemble/model_dir/test_loss_200301.json @@ -0,0 +1,5 @@ +{ + "loss": 860.4375, + "reconstruction_loss": 0.00043432702659629285, + "kl_loss": 860.4379272460938 +} \ No newline at end of file diff --git a/run_on_cluster/cvae-weather-ensemble/model_dir/test_loss_200310.json b/run_on_cluster/cvae-weather-ensemble/model_dir/test_loss_200310.json new file mode 100644 index 0000000..8ec1fd6 --- /dev/null +++ b/run_on_cluster/cvae-weather-ensemble/model_dir/test_loss_200310.json @@ -0,0 +1,5 @@ +{ + "loss": 860.754638671875, + "reconstruction_loss": 0.00045085581950843334, + "kl_loss": 860.755126953125 +} \ No newline at end of file diff --git a/run_on_cluster/cvae-weather-ensemble/model_dir/test_loss_200320.json b/run_on_cluster/cvae-weather-ensemble/model_dir/test_loss_200320.json new file mode 100644 index 0000000..3de1c9b --- /dev/null +++ b/run_on_cluster/cvae-weather-ensemble/model_dir/test_loss_200320.json @@ -0,0 +1,5 @@ +{ + "loss": 860.2235107421875, + "reconstruction_loss": 0.0005086498567834496, + "kl_loss": 860.2239990234375 +} \ No newline at end of file diff --git a/run_on_cluster/cvae-weather-ensemble/model_dir/vae.weights.h5 b/run_on_cluster/cvae-weather-ensemble/model_dir/vae.weights.h5 new file mode 100644 index 0000000..f2dd21c Binary files /dev/null and b/run_on_cluster/cvae-weather-ensemble/model_dir/vae.weights.h5 differ diff --git a/run_on_cluster/cvae-weather-ensemble/model_dir/vae.weights_200001.h5.dvc b/run_on_cluster/cvae-weather-ensemble/model_dir/vae.weights_200001.h5.dvc new file mode 100644 index 0000000..4b566b4 --- /dev/null +++ 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b/run_on_cluster/cvae-weather-ensemble/old-code/.ipynb_checkpoints/cvae_assess-original-checkpoint.ipynb similarity index 99% rename from run_on_cluster/cvae-weather-ensemble/CVAE_output.ipynb rename to run_on_cluster/cvae-weather-ensemble/old-code/.ipynb_checkpoints/cvae_assess-original-checkpoint.ipynb index ae206f7..1a35324 100644 --- a/run_on_cluster/cvae-weather-ensemble/CVAE_output.ipynb +++ b/run_on_cluster/cvae-weather-ensemble/old-code/.ipynb_checkpoints/cvae_assess-original-checkpoint.ipynb @@ -14,44 +14,18 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "--2022-01-06 17:10:21-- https://noaa-gefs-retrospective.s3.amazonaws.com/GEFSv12/reforecast/2018/2018010100/c00/Days%3A1-10/pres_sfc_2018010100_c00.grib2\n", - "Resolving noaa-gefs-retrospective.s3.amazonaws.com (noaa-gefs-retrospective.s3.amazonaws.com)... 52.216.186.195, 52.217.109.84, 52.216.145.123\n", - "Connecting to noaa-gefs-retrospective.s3.amazonaws.com (noaa-gefs-retrospective.s3.amazonaws.com)|52.216.186.195|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 59318426 (57M) [binary/octet-stream]\n", - "Saving to: ‘pres_sfc_2018010100_c00.grib2’\n", - "\n", - "pres_sfc_2018010100 100%[===================>] 56.57M 7.14MB/s in 8.0s \n", - "\n", - "2022-01-06 17:10:30 (7.06 MB/s) - ‘pres_sfc_2018010100_c00.grib2’ saved [59318426/59318426]\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "!wget https://noaa-gefs-retrospective.s3.amazonaws.com/GEFSv12/reforecast/2018/2018010100/c00/Days%3A1-10/pres_sfc_2018010100_c00.grib2" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-rw-r--r-- 1 jovyan users 57M May 29 2020 pres_sfc_2018010100_c00.grib2\r\n" - ] - } - ], + "outputs": [], "source": [ "!ls -alh | grep pres" ] @@ -65,40 +39,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/pw/.miniconda3/envs/parsl-pw/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", - "/pw/.miniconda3/envs/parsl-pw/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", - "/pw/.miniconda3/envs/parsl-pw/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", - "/pw/.miniconda3/envs/parsl-pw/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", - "/pw/.miniconda3/envs/parsl-pw/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", - "/pw/.miniconda3/envs/parsl-pw/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n", - "/pw/.miniconda3/envs/parsl-pw/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", - "/pw/.miniconda3/envs/parsl-pw/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", - "/pw/.miniconda3/envs/parsl-pw/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", - "/pw/.miniconda3/envs/parsl-pw/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", - "/pw/.miniconda3/envs/parsl-pw/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", - "/pw/.miniconda3/envs/parsl-pw/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n" - ] - } - ], + "outputs": [], "source": [ "import tensorflow as tf\n", "tf.compat.v1.enable_eager_execution()\n", @@ -631,7 +574,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -645,7 +588,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/run_on_cluster/cvae-weather-ensemble/main_cvae.py b/run_on_cluster/cvae-weather-ensemble/old-code/.ipynb_checkpoints/main_cvae-checkpoint.py similarity index 100% rename from run_on_cluster/cvae-weather-ensemble/main_cvae.py rename to run_on_cluster/cvae-weather-ensemble/old-code/.ipynb_checkpoints/main_cvae-checkpoint.py diff --git a/run_on_cluster/cvae-weather-ensemble/old-code/.ipynb_checkpoints/old-README-checkpoint.md b/run_on_cluster/cvae-weather-ensemble/old-code/.ipynb_checkpoints/old-README-checkpoint.md new file mode 100644 index 0000000..b24348a --- /dev/null +++ b/run_on_cluster/cvae-weather-ensemble/old-code/.ipynb_checkpoints/old-README-checkpoint.md @@ -0,0 +1,98 @@ +# VM setup +1. Testing whether I can use a lower cost VM (n1-standard-4) to `conda install` and pre-download data and save data on image and then reboot image as a higher cost (a2-highgpu-1g) for the actual training. This seems to work BUT may require reinstalling Linux headers when going from low->high cost and then rebooting the VM, e.g.: +```bash +# Booted as a2-highgpu-1g after run as n1-standard-4 -> error message. +sudo apt-get install linux-headers-4.19.0-18-cloud-amd64 +reboot +``` +See `/var/log/apt/history.log` for history of install requests (includes the tcsh install in #4 below). Also, note that while it is possible to switch between n1-standards and a1-highgpu, you **cannot** switch between e1 class machines and a1 machines. + +2. Testing whether I can use pygrib as a direct load from grib -> YES, but need to manually add time axis and concatenate time steps within files in addition to concatenating time steps between files. See load_many_grib in main_cvae.py. + +3. Using the c1-deeplearning-tf-2-5-cu110-v20210714-debian-10 image as a starting point. It has conda and I will attempt to install default pygrib and parsl into the base conda environment. It appears that all the other key packages are already installed (numpy, pandas, matplotlib, json, os, scikit-learn, tensorflow, pickle, glob). + +4. Actual steps executed on an n1-standard-4: +```bash +conda install -c conda-forge pygrib # Could not solve env before package download but installed anyway. +conda install -c conda-forge parsl # Installed without issue (downgrading sqlalchemy) +sudo apt-get install tcsh # Used for some of the batch scripts in this dir, could be bypassed. +``` + +5. Testing data loading: +```python +import numpy as np +import pygrib +file_name = "./gefs_data/pres_msl_2019110100_c00.grib2" +file_data = pygrib.open(file_name) +msl = file_data.select(name='Mean sea level pressure') +data = msl[0].values +np.shape(data) +(721, 1440) +``` + +6. Testing the use of smaller data types. Please see [this blog](https://towardsdatascience.com/memory-efficient-data-science-types-53423d48ba1d) for a great intro to which data types are supported and how. In a nutshell, float16 is dangerous to use because it has inconsistent support. Float64 is the default numpy type, float32 should work easily with `data.astype(np.float32)`. The most commonly supported ints are int8 and int32, but there is no mention of the reliability of int16, which is what I want to use. + +# Data sizes: + +1. Each GEFS 2D field with 80 time steps (10 days at 3 hours) is 57MB in grib2 +2. Converted to netcdf, the field with 80 time steps is 317MB +3. A whole year in NetCDF is 12 months x 3 fields/month * 317MB = 11GB +4. Converted to pkl, each file is 634MB x 36 = 22GB +5. We need to figure out a robust and efficient way to go from grib2 to load to memory. Currently, this multistage approach is slow and takes up a lot of space. + +# Pipeline summary: + +1. wget to download from NOAA's S3 bucket +2. cdo to convert to nc (`apt-get install cdo`) +3. pyferret to load as numpy array (`conda install -y -c conda-forge pyferret`) +4. save as pkl for use outside of pyferret container b/c pyferret will not install in Conda (like pynio and cfgrib) + +This means ignore `batch_grib2pkl.csh` because pynio/cfgrib don't work reliably. + +# Tensorflow: + +1. docker pull tensorflow/tensorflow +2. Run this container, e.g. sudo docker run -it -v/home/sfgary/sbir-learnerworks-doc/NOAA_JTTI:/tmp/noaa tensorflow:tensorflow /bin/bash +3. pip install pandas +4. pip install scikit-learn +5. pip install matplotlib + +# Grids + +Since we have broken away from grib/netcdf, we need to explicitly +bring back the grid information. Here, I used ncdump -h on the lon +and lat variables in .nc files, manually removed the header, and +then used the following text pipeline to make into a column vector +in a text file: + +cat lon.txt | tr -d '}' | tr -d ';' | tr -d '\n' | tr -d ' ' | tr ',' '\n' > lon.x +cat lat.txt | tr -d '}' | tr -d ';' | tr -d '\n' | tr -d ' ' | tr ',' '\n' > lat.y + +# Training the ML model + +For two years of data (36 files each year, each file has 80 3h time steps = 10 days) +(Used 2018 and 2019) + +Input summary: +(5760, 721, 1440, 1) +Mean: 0.9185315259371455 +Std: 0.012157332330270077 +Testing shape +(1440, 721, 1440, 1) +Training shape +(3240, 721, 1440, 1) +Validaiton shape +(1080, 721, 1440, 1) + +Takes about 10 mins per epoch, uses ~128GB RAM, linear scaling wrt batch size seems +confirmed wrt previous runs. Running on 32CPU, all utilized but at around 50%. + +Training time: +Start: 2021-11-14 15:16:14 +End: + +# Using the ML model + +The model weights are in ./gefs/vae.data-00000-of-000001. The Python/Tensorflow +code that defines the model needs to be loaded first and then the weights are +loaded. \ No newline at end of file diff --git a/run_on_cluster/cvae-weather-ensemble/scripts/batch_download.csh b/run_on_cluster/cvae-weather-ensemble/old-code/batch_download.csh old mode 100755 new mode 100644 similarity index 100% rename from run_on_cluster/cvae-weather-ensemble/scripts/batch_download.csh rename to run_on_cluster/cvae-weather-ensemble/old-code/batch_download.csh diff --git a/run_on_cluster/cvae-weather-ensemble/old-code/cvae_assess-original.ipynb b/run_on_cluster/cvae-weather-ensemble/old-code/cvae_assess-original.ipynb new file mode 100644 index 0000000..1a35324 --- /dev/null +++ b/run_on_cluster/cvae-weather-ensemble/old-code/cvae_assess-original.ipynb @@ -0,0 +1,596 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Demo using a trained CVAE model\n", + "The goal here is to use a trained CVAE model with new data to create synthetic ensemble members.\n", + "\n", + "\n", + "# Download data\n", + "On my [first Google hit for GEFS](https://www.ncei.noaa.gov/products/weather-climate-models/global-ensemble-forecast), I clicked on [AWS Open Data Registry for GEFS](https://registry.opendata.aws/noaa-gefs-pds/) and selected [NOAA GEFS Re-forecast](https://registry.opendata.aws/noaa-gefs-reforecast/) which has no useage restrictions. The [GEFS Re-forecast data documentation](https://noaa-gefs-retrospective.s3.amazonaws.com/Description_of_reforecast_data.pdf) is very clear and we're going to download a file, 57 MB, that is identical in format to the data used in training the ML model but it was not used in the training. Pick Jan 01 2018 while the model was trained over all 2019 data. The date of the initialization of the re-forecast is in the file name in the format YYYYMMDDHH. The c00, p01, p02, p03, p04 are the control and perturbation ensemble members (5 total)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!wget https://noaa-gefs-retrospective.s3.amazonaws.com/GEFSv12/reforecast/2018/2018010100/c00/Days%3A1-10/pres_sfc_2018010100_c00.grib2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!ls -alh | grep pres" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "tf.compat.v1.enable_eager_execution()\n", + "\n", + "import os, json\n", + "import pickle5\n", + "import numpy as np\n", + "import pandas as pd\n", + "from scipy.ndimage.filters import gaussian_filter as gf\n", + "\n", + "from tensorflow import keras\n", + "from tensorflow.keras import layers\n", + "import cProfile # For eager execution, https://www.tensorflow.org/guide/eager\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import cartopy\n", + "\n", + "from main_cvae import Sampling, build_encoder, calculate_final_shape, calculate_output_paddings\n", + "from main_cvae import build_decoder, VAE, plot_latent_space, plot_images" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load and preprocess the input data\n", + "The standard way of manipulating arrays in Conv2D layers in TF is to use arrays in the shape:\n", + "`batch_size, height, width, channels = data.shape`\n", + "In our case, the the `batch_size` is the number of image frames (i.e. separate samples or rows in a `.csv` file), the `height` and `width` define the size of the image frame in number of pixels, and the `channels` are the number of layers in the frames. Typically, channels are color layers (e.g. RGB or CMYK) but in our case, we could use different metereological variables. However, for this first experiment, **we only need one channel** because we're only going to use sea level pressure (SLP).\n", + "\n", + "The code for loading GEFS `.grib` files and making an initial plot is from [Victor Gensini's example](https://github.com/vgensini/gefs_v12_example/blob/master/GEFS_v12_eample.ipynb) posted on the GEFS Open Data Registry landing page." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "f = open('pres_msl_2017010100_c00.nc.pkl','rb')\n", + "# Remove the scaling and convert Pa to hPa\n", + "#slp = pickle5.load(f)*110000/100\n", + "slp = pickle5.load(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(80, 721, 1440, 1)\n" + ] + } + ], + "source": [ + "# Look at data structure\n", + "print(np.shape(slp))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(721, 1440)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Grid point locations\n", + "lons = np.loadtxt('lon.x')\n", + "lats = np.loadtxt('lat.y')\n", + "\n", + "x, y = np.meshgrid(lons,lats)\n", + "\n", + "np.shape(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/pw/.miniconda3/envs/parsl-pw/lib/python3.7/site-packages/cartopy/mpl/geoaxes.py:388: MatplotlibDeprecationWarning: \n", + "The 'inframe' parameter of draw() was deprecated in Matplotlib 3.3 and will be removed two minor releases later. Use Axes.redraw_in_frame() instead. If any parameter follows 'inframe', they should be passed as keyword, not positionally.\n", + " inframe=inframe)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Example for plot\n", + "fig = plt.figure(figsize=(9,6))\n", + "ax = plt.axes(projection = cartopy.crs.LambertConformal())\n", + "ax.add_feature(cartopy.feature.LAND)\n", + "ax.add_feature(cartopy.feature.OCEAN)\n", + "ax.add_feature(cartopy.feature.LAKES, alpha = 0.5)\n", + "ax.add_feature(cartopy.feature.STATES, edgecolor='grey')\n", + "plt.contour(x,y,np.squeeze(slp[0,:,:,0])*120000/100,\n", + " transform = cartopy.crs.PlateCarree(),\n", + " levels=[970,975,980,985,990,995,1000,1005,1010,1015,1020,1025,1030,1035,1040,1045,1050,1055,1060],colors='k')\n", + "plt.title('GEFSv12 MSL 2017 01 01 0000 UTC')\n", + "#ax.set_extent([-120, -73, 23, 50])\n", + "ax.set_extent([-150, -60, 20, 65])\n", + "#plt.colorbar()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load ML model" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2021-11-15 01:59:07.854539: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA\n", + "2021-11-15 01:59:07.864083: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2300000000 Hz\n", + "2021-11-15 01:59:07.865766: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55e0f2829310 executing computations on platform Host. Devices:\n", + "2021-11-15 01:59:07.865821: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): , \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Entity > could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting >: AttributeError: module 'gast' has no attribute 'Index'\n", + "WARNING: Entity > could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting >: AttributeError: module 'gast' has no attribute 'Index'\n", + "Model: \"encoder\"\n", + "__________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + "input_1 (InputLayer) [(None, 721, 1440, 1 0 \n", + "__________________________________________________________________________________________________\n", + "conv2d (Conv2D) (None, 361, 720, 16) 160 input_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_1 (Conv2D) (None, 181, 360, 32) 4640 conv2d[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_2 (Conv2D) (None, 91, 180, 64) 18496 conv2d_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2d_3 (Conv2D) (None, 46, 90, 128) 73856 conv2d_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "flatten (Flatten) (None, 529920) 0 conv2d_3[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense (Dense) (None, 8) 4239368 flatten[0][0] \n", + "__________________________________________________________________________________________________\n", + "z_mean (Dense) (None, 3) 27 dense[0][0] \n", + "__________________________________________________________________________________________________\n", + "z_log_var (Dense) (None, 3) 27 dense[0][0] \n", + "__________________________________________________________________________________________________\n", + "sampling (Sampling) (None, 3) 0 z_mean[0][0] \n", + " z_log_var[0][0] \n", + "==================================================================================================\n", + "Total params: 4,336,574\n", + "Trainable params: 4,336,574\n", + "Non-trainable params: 0\n", + "__________________________________________________________________________________________________\n", + "None\n", + "Model: \"decoder\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "input_2 (InputLayer) [(None, 3)] 0 \n", + "_________________________________________________________________\n", + "dense_1 (Dense) (None, 529920) 2119680 \n", + "_________________________________________________________________\n", + "reshape (Reshape) (None, 46, 90, 128) 0 \n", + "_________________________________________________________________\n", + "conv2d_transpose (Conv2DTran (None, 91, 180, 128) 147584 \n", + "_________________________________________________________________\n", + "conv2d_transpose_1 (Conv2DTr (None, 181, 360, 64) 73792 \n", + "_________________________________________________________________\n", + "conv2d_transpose_2 (Conv2DTr (None, 361, 720, 32) 18464 \n", + "_________________________________________________________________\n", + "conv2d_transpose_3 (Conv2DTr (None, 721, 1440, 16) 4624 \n", + "_________________________________________________________________\n", + "conv2d_transpose_4 (Conv2DTr (None, 721, 1440, 1) 145 \n", + "=================================================================\n", + "Total params: 2,364,289\n", + "Trainable params: 2,364,289\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "None\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Key CVAE definition parameters\n", + "latent_dim = 3\n", + "n_conv_layers = 4\n", + "stride = 2\n", + "kernel_size = 3\n", + "batch_size, height, width, channels = slp.shape\n", + "\n", + "encoder = build_encoder(latent_dim, height, width, channels, n_conv_layers, kernel_size, stride, base_filters = 16)\n", + "decoder = build_decoder(latent_dim, height, width, channels, n_conv_layers, kernel_size, stride, base_filters = 16)\n", + "vae = VAE(encoder, decoder, height*width)\n", + "vae.compile(optimizer = 'rmsprop')\n", + "vae.load_weights(os.path.join('gefs', 'vae'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Encode, perturb, and decode" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2021-11-15 01:59:19.194158: W tensorflow/core/framework/allocator.cc:107] Allocation of 2661580800 exceeds 10% of system memory.\n", + "2021-11-15 01:59:22.238268: W tensorflow/core/framework/allocator.cc:107] Allocation of 2661580800 exceeds 10% of system memory.\n", + "2021-11-15 01:59:23.141114: W tensorflow/core/framework/allocator.cc:107] Allocation of 2661580800 exceeds 10% of system memory.\n", + "2021-11-15 01:59:23.861704: W tensorflow/core/framework/allocator.cc:107] Allocation of 5315788800 exceeds 10% of system memory.\n", + "2021-11-15 01:59:28.896137: W tensorflow/core/framework/allocator.cc:107] Allocation of 5315788800 exceeds 10% of system memory.\n" + ] + } + ], + "source": [ + "z_mean, z_log_var, z = vae.encoder(slp.astype('float32'))\n", + "sample_output_images = vae.decoder(z)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "Filtering...\n", + "1013.0244136676295\n", + "39.12082561606502\n", + "Contour plotting...\n", + "Filtering...\n", + "Contour plotting...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "print(tf.executing_eagerly())\n", + "#tf.compat.v1.enable_eager_execution()\n", + "\n", + "# Plot setup\n", + "#fig, ax = plt.subplots(figsize=(9,6))\n", + "fig = plt.figure(figsize=(9,6))\n", + "ax = plt.axes(projection = cartopy.crs.LambertConformal())\n", + "ax.add_feature(cartopy.feature.LAND)\n", + "ax.add_feature(cartopy.feature.OCEAN)\n", + "ax.add_feature(cartopy.feature.LAKES, alpha = 0.5)\n", + "ax.add_feature(cartopy.feature.STATES, edgecolor='grey')\n", + "\n", + "# Plot each one\n", + "for i, image in enumerate(sample_output_images):\n", + " if i == 0 :\n", + " print('Filtering...')\n", + " filtered = gf(np.squeeze(image)*120000/100, [3,3], mode='constant')\n", + " print(np.mean(filtered))\n", + " print(np.std(filtered))\n", + " #plt.pcolor(x,y,np.squeeze(image)*120000/100,shading='auto')\n", + " #transform = cartopy.crs.PlateCarree(),shading='auto')\n", + " print('Contour plotting...')\n", + " plt.contour(x,y,filtered,\n", + " transform = cartopy.crs.PlateCarree(),\n", + " levels=[980,990,1000,1010,1015,1020,1025,1030,1035,1040,1045,1050],colors='r',linewidths=1) \n", + "\n", + "# Plot original\n", + "print('Filtering...')\n", + "filtered = gf(np.squeeze(slp[0,:,:,0])*120000/100, [3,3], mode='constant')\n", + "print('Contour plotting...')\n", + "plt.contour(x,y,filtered,\n", + " transform = cartopy.crs.PlateCarree(),\n", + " levels=[980,990,1000,1010,1015,1020,1025,1030,1035,1040,1045,1050],colors='k',linewidths=2)\n", + "\n", + "#plt.title('GEFSv12 Re-forecast SLP 990hPa 2018 01 10 0000 UTC Cycle')\n", + "#ax.set_extent([-120, -73, 23, 50])\n", + "ax.set_extent([-150, -60, 20, 65])\n", + "#ax.set_xlim([-150,-60])\n", + "#ax.set_ylim([20,65])\n", + "#plt.colorbar()\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Perturb current state" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "Filtering...\n", + "Contour plotting...\n", + "Filtering...\n", + "Contour plotting...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "perturbed_images_high = vae.decoder(z_mean + z_log_var)\n", + "perturbed_images_low = vae.decoder(z_mean - z_log_var)\n", + "\n", + "print(tf.executing_eagerly())\n", + "#tf.compat.v1.enable_eager_execution()\n", + "\n", + "# Plot setup\n", + "fig = plt.figure(figsize=(9,6))\n", + "ax = plt.axes(projection = cartopy.crs.LambertConformal())\n", + "ax.add_feature(cartopy.feature.LAND)\n", + "ax.add_feature(cartopy.feature.OCEAN)\n", + "ax.add_feature(cartopy.feature.LAKES, alpha = 0.5)\n", + "ax.add_feature(cartopy.feature.STATES, edgecolor='grey')\n", + "\n", + "# Plot each one\n", + "for i, image in enumerate(perturbed_images_high):\n", + " if i == 0 :\n", + " print('Filtering...')\n", + " filtered = gf(np.squeeze(image)*120000/100, [3,3], mode='constant')\n", + " print('Contour plotting...')\n", + " plt.contour(x,y,filtered,\n", + " transform = cartopy.crs.PlateCarree(),\n", + " levels=[980,990,1000,1010,1015,1020,1025,1030,1035,1040,1045,1050],colors='r',linewidths=1) \n", + "\n", + "# Plot each one\n", + "for i, image in enumerate(perturbed_images_low):\n", + " if i == 0 :\n", + " print('Filtering...')\n", + " filtered = gf(np.squeeze(image)*120000/100, [3,3], mode='constant')\n", + " print('Contour plotting...')\n", + " plt.contour(x,y,filtered,\n", + " transform = cartopy.crs.PlateCarree(),\n", + " levels=[980,990,1000,1010,1015,1020,1025,1030,1035,1040,1045,1050],colors='b',linewidths=1) \n", + " \n", + "# Plot original\n", + "plt.contour(x,y,np.squeeze(slp[0,:,:,0])*120000/100,\n", + " transform = cartopy.crs.PlateCarree(),\n", + " levels=[980,990,1000,1010,1015,1020,1025,1030,1035,1040,1045,1050],colors='k',linewidths=2)\n", + "\n", + "plt.title('GEFSv12 Re-forecast SLP 990hPa 2018 01 10 0000 UTC Cycle')\n", + "#ax.set_extent([-120, -73, 23, 50])\n", + "ax.set_extent([-150, -60, 20, 65])\n", + "#plt.colorbar()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Generate totally random weather maps" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filtering...\n", + "Filtering...\n", + "Filtering...\n", + "Filtering...\n", + "Filtering...\n", + "Filtering...\n", + "Filtering...\n", + "Filtering...\n", + "Filtering...\n", + "Filtering...\n", + "Filtering...\n", + "Filtering...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "codings = tf.random.normal(shape = [12, latent_dim])\n", + "images = vae.decoder(codings).numpy()\n", + "\n", + "# Plot setup\n", + "fig = plt.figure(figsize=(9,6))\n", + "ax = plt.axes(projection = cartopy.crs.LambertConformal())\n", + "ax.add_feature(cartopy.feature.LAND)\n", + "ax.add_feature(cartopy.feature.OCEAN)\n", + "ax.add_feature(cartopy.feature.LAKES, alpha = 0.5)\n", + "ax.add_feature(cartopy.feature.STATES, edgecolor='grey')\n", + "\n", + "# Plot each one\n", + "for i, image in enumerate(images):\n", + " print('Filtering...')\n", + " filtered = gf(np.squeeze(image)*120000/100, [5,5], mode='constant')\n", + " plt.contour(x,y,filtered,\n", + " transform = cartopy.crs.PlateCarree(),\n", + " levels=[970,980,990,1000,1010,1015,1020,1025,1030,1040,1050,1060],colors='k',linewidths=1)\n", + " \n", + "#plt.title('Random MSL pressure maps from ML model trained with GEFSv12 Re-forecast 2018-2019')\n", + "#ax.set_extent([-120, -73, 23, 50])\n", + "ax.set_extent([-150, -60, 20, 65])\n", + "#plt.colorbar()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3\n" + ] + } + ], + "source": [ + "print(latent_dim)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git 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b/run_on_cluster/cvae-weather-ensemble/old-code/gefs-models-and-pictures/pres_sfc_2019/history.csv @@ -0,0 +1,6 @@ +loss,reconstruction_loss,kl_loss,val_loss,val_reconstruction_loss,val_kl_loss +0.36689645051956177,0.36689645051956177,6.231288263158774e-10,0.3483951985836029,0.3483951985836029,1.2442032959469884e-09 +0.3453749418258667,0.3453749418258667,4.2065453342843284e-09,0.34320762753486633,0.34320762753486633,8.16991541086054e-09 +0.34445956349372864,0.34445953369140625,1.3147645638866834e-08,0.34632036089897156,0.34632036089897156,1.91600868504338e-08 +0.3439769148826599,0.34397685527801514,2.8179519162563338e-08,0.34270548820495605,0.34270548820495605,3.669515891147057e-08 +0.3435705304145813,0.3435705304145813,4.69549128467861e-08,0.34341031312942505,0.34341028332710266,5.747823905721816e-08 diff --git a/run_on_cluster/cvae-weather-ensemble/old-code/gefs-models-and-pictures/pres_sfc_2019/interpolated.png 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a/run_on_cluster/cvae-weather-ensemble/gefs/pres_sfc_2019/vae.index b/run_on_cluster/cvae-weather-ensemble/old-code/gefs-models-and-pictures/pres_sfc_2019/vae.index similarity index 100% rename from run_on_cluster/cvae-weather-ensemble/gefs/pres_sfc_2019/vae.index rename to run_on_cluster/cvae-weather-ensemble/old-code/gefs-models-and-pictures/pres_sfc_2019/vae.index diff --git a/run_on_cluster/cvae-weather-ensemble/old-code/main_cvae.py b/run_on_cluster/cvae-weather-ensemble/old-code/main_cvae.py new file mode 100755 index 0000000..4c361d9 --- /dev/null +++ b/run_on_cluster/cvae-weather-ensemble/old-code/main_cvae.py @@ -0,0 +1,638 @@ +# https://keras.io/examples/generative/vae/ +import os, json +import pickle +import glob +import numpy as np +import pandas as pd + +import tensorflow as tf +from tensorflow import keras +from tensorflow.keras import layers + +from sklearn.model_selection import train_test_split + +import matplotlib.pyplot as plt + +# CNN require huge amounts of RAM, specially during training +# Computational requirements for a single CNN layer: +# - Number of parameters: (+1 is the bias term) +# (filter width * filter height * channels + 1 ) * feature maps +# - Number of float multiplications: +# feature maps * feature map width * feature map height * filter width * filfet height * channels +# ---> For the first layer feature map width and height are the inputs width and height divided by the strides!! +# - Output Memory: +# feature maps * feature map width * feature map height * 32 bits * training batch size +# ---> Reduce batch size to fir in memory! +# Memory for multiple layers: +# - During training: The sum of all the layers +# - During inference: Two consecutive layers + +# If you have memory issues: +# 1. Reuduce batch size +# 2. Increase stride +# 3. Remove layers +# 4. Use 16-bit floats +# 5. Distribute across multiple devices + +# Pooling layers: shrink the input image to reduce computational load, memory usage and number of parameters +# - Pooling neurons have no weights +# - keras.layers.MaxPool2D(pool_size=(2, 2), strides=None, padding='valid', data_format=None) +# - AvgPool2D +# - Only takes the maximum (or average, minimum, etc) value of the pooling_size window +# - If stride > 1 reduces the number of parameters. Stride defaults to pool_size +# - Introduces a level of invariance to small translations and some rotational and scale invariance +# - Invariance is desirable for classification but undersirable for semantic segmentation +# - Invariance is desirable if a small change in the inputs should lead to a small change on the outputs +# - MaxPool2D typically has better performance +# - Can also be applied to depth (channels) instead of spatial direction (see page 469) +# - GlobalAvgPool2D: Calculates the average of the entire feature map + +# Typical CNN architecture: +# - Convolutions(s) with same number of feature maps --> Pooling --> Convolution(s) --> Pooling --> repeat --> Flatten --> Fully connected layer +# - Image gets smaller (less width and height) and deeper (more feature maps) --> Dense --> Dropout --> Repeat +# - Avoid large kernels (window sizes)--> use more layers (except in first layer) +# - Number of repetitions is a hyperparameter to tune +# - Double number of filters after each pooling layer +# - Add keras.layers.Dropout(0.5) to reduce overfitting between dense layers + +class Sampling(layers.Layer): + """Uses (z_mean, z_log_var) to sample z, the vector encoding a digit.""" + + def call(self, inputs): + z_mean, z_log_var = inputs + batch = tf.shape(z_mean)[0] + dim = tf.shape(z_mean)[1] + epsilon = tf.keras.backend.random_normal(shape=(batch, dim)) + return z_mean + tf.exp(0.5 * z_log_var) * epsilon + + +def build_encoder(latent_dim, height, width, channels, n_conv_layers = 2, kernel_size = 3, strides = 2, base_filters = 32): + encoder_inputs = keras.Input(shape=(height, width, channels)) + # Number of filters: 32 + # kernel_size: 3 (integer or list of two integers) -> Width and height of the 2D convolution window or receptive field + # strides: Shift from one window to the next. The output size is the input size size divided by the stride (rounded up) + # pading: + # - same: Uses zero padding when stride and filter width don't match input width + # - valid: Ignores inputs to fit the input width to the stride and filter width + #x = layers.Conv2D(32, 20, activation="relu", strides=(7,10), padding="same")(encoder_inputs) + + for l in range(n_conv_layers): + n_filters = int(base_filters*np.power(2, l)) + if l == 0: + x = layers.Conv2D(n_filters, kernel_size, activation="relu", strides=strides, padding="same")(encoder_inputs) + else: + x = layers.Conv2D(n_filters, kernel_size, activation="relu", strides=strides, padding="same")(x) + # FIXME: Why are there no pooling layers? + + x = layers.Flatten()(x) + x = layers.Dense(8, activation="relu")(x) + z_mean = layers.Dense(latent_dim, name="z_mean")(x) + z_log_var = layers.Dense(latent_dim, name="z_log_var")(x) + z = Sampling()([z_mean, z_log_var]) + encoder = keras.Model(encoder_inputs, [z_mean, z_log_var, z], name="encoder") + print(encoder.summary()) + return encoder + +def calculate_final_shape(height, num_conv_2d_layers, stride): + for n in range(num_conv_2d_layers): + height = int(np.ceil(height / stride)) + return height + +# Only works for stride 2... +def calculate_output_paddings(height, num_conv_2d_layers, stride): + output_paddings = [] + for n in range(num_conv_2d_layers): + output_paddings.append(stride - (height % stride) - 1) + height = int(np.ceil(height / stride)) + + output_paddings.reverse() + return output_paddings + +# Works for stride 3 but may need extra work/testing +def _calculate_output_paddings(height, num_conv_layers, stride): + output_paddings = [] + encoder_heights = [] + decoder_heights = [] + output_paddings = [] + encoder_heights.append(height) + + for n in range(num_conv_layers): + height = int(np.ceil(height / stride)) + encoder_heights.append(height) + + encoder_heights.reverse() + + decoder_heights.append(height) + for n in range(num_conv_layers): + height = int(height * stride) + padding = height - encoder_heights[n+1] + print(padding) + #output_paddings.append(padding + stride - int(stride/2) - 2) + height -= padding + decoder_heights.append(height) + if padding == 0: + padding = 2 + elif padding == 2: + padding = 0 + output_paddings.append(padding) + + return output_paddings + +def build_decoder(latent_dim, height, width, channels, n_conv_layers = 2, kernel_size = 3, strides = 2, base_filters = 32): + latent_inputs = keras.Input(shape=(latent_dim,)) + + # Adjusting for the first and last layers! + #height = int(height/7) + #width = int(width/10) + + final_height = calculate_final_shape(height, n_conv_layers, strides) + final_width = calculate_final_shape(width, n_conv_layers, strides) + height_paddings = calculate_output_paddings(height, n_conv_layers, strides) + width_paddings = calculate_output_paddings(width, n_conv_layers, strides) + final_filters = base_filters*np.power(2, n_conv_layers-1) + + x = layers.Dense(final_height * final_width * final_filters, activation="relu")(latent_inputs) + x = layers.Reshape((final_height, final_width, final_filters))(x) + + for l in range(n_conv_layers): + filters = base_filters*np.power(2, n_conv_layers-l-1) + x = layers.Conv2DTranspose(filters, kernel_size, activation="relu", strides = strides, padding="same", output_padding = [height_paddings[l], width_paddings[l]])(x) + + #x = layers.Conv2DTranspose(32, 10, activation="relu", strides=(7,10), padding="same")(x) + decoder_outputs = layers.Conv2DTranspose(channels, 3, activation="sigmoid", padding="same")(x) + decoder = keras.Model(latent_inputs, decoder_outputs, name="decoder") + print(decoder.summary()) + return decoder + +class VAE(keras.Model): + def __init__(self, encoder, decoder, loss_scale, **kwargs): + super(VAE, self).__init__(**kwargs) + self.encoder = encoder + self.decoder = decoder + self.total_loss_tracker = keras.metrics.Mean(name="total_loss") + self.reconstruction_loss_tracker = keras.metrics.Mean( + name="reconstruction_loss" + ) + self.kl_loss_tracker = keras.metrics.Mean(name="kl_loss") + self.loss_scale = loss_scale + + @property + def metrics(self): + return [ + self.total_loss_tracker, + self.reconstruction_loss_tracker, + self.kl_loss_tracker, + ] + + def train_step(self, data): + + with tf.GradientTape() as tape: + z_mean, z_log_var, z = self.encoder(data) + reconstruction = self.decoder(z) + reconstruction_loss = tf.reduce_mean( + tf.reduce_sum( + keras.losses.binary_crossentropy(data, reconstruction), axis=(1, 2) + ) + )/self.loss_scale + kl_loss = -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var)) + kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis=1))/self.loss_scale + total_loss = (reconstruction_loss + kl_loss) + grads = tape.gradient(total_loss, self.trainable_weights) + self.optimizer.apply_gradients(zip(grads, self.trainable_weights)) + self.total_loss_tracker.update_state(total_loss) + self.reconstruction_loss_tracker.update_state(reconstruction_loss) + self.kl_loss_tracker.update_state(kl_loss) + return { + "loss": self.total_loss_tracker.result(), + "reconstruction_loss": self.reconstruction_loss_tracker.result(), + "kl_loss": self.kl_loss_tracker.result(), + } + + # Needed to validate (validation loss) and to evaluate + def test_step(self, data): + if type(data) == tuple: + data, _ = data + + z_mean, z_log_var, z = self.encoder(data) + reconstruction = self.decoder(z) + # FIXME: Normalize loss with the number of features (28*28) + reconstruction_loss = tf.reduce_mean( + tf.reduce_sum( + keras.losses.binary_crossentropy(data, reconstruction), axis=(1, 2) + ) + )/self.loss_scale + kl_loss = -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var)) + kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis=1))/self.loss_scale + total_loss = (reconstruction_loss + kl_loss) + #grads = tape.gradient(total_loss, self.trainable_weights) + #self.optimizer.apply_gradients(zip(grads, self.trainable_weights)) + self.total_loss_tracker.update_state(total_loss) + self.reconstruction_loss_tracker.update_state(reconstruction_loss) + self.kl_loss_tracker.update_state(kl_loss) + return { + "loss": self.total_loss_tracker.result(), + "reconstruction_loss": self.reconstruction_loss_tracker.result(), + "kl_loss": self.kl_loss_tracker.result(), + } + +# Only works if latent_dim = 2 +def plot_latent_space(vae, digit_size, channels, n=30, figsize=15, show = False, path = ''): + # display a n*n 2D manifold of digits + #digit_size = 28 + scale = 1.0 + figure = np.zeros((digit_size * n, digit_size * n)) + # linearly spaced coordinates corresponding to the 2D plot + # of digit classes in the latent space + grid_x = np.linspace(-scale, scale, n) + grid_y = np.linspace(-scale, scale, n)[::-1] + + for i, yi in enumerate(grid_y): + for j, xi in enumerate(grid_x): + z_sample = np.array([[xi, yi]]) + x_decoded = vae.decoder.predict(z_sample) + digit = x_decoded[0].reshape(digit_size, digit_size, channels) + figure[ + i * digit_size : (i + 1) * digit_size, + j * digit_size : (j + 1) * digit_size, + ] = digit[:,:,0] + + plt.figure(figsize=(figsize, figsize)) + start_range = digit_size // 2 + end_range = n * digit_size + start_range + pixel_range = np.arange(start_range, end_range, digit_size) + sample_range_x = np.round(grid_x, 1) + sample_range_y = np.round(grid_y, 1) + plt.xticks(pixel_range, sample_range_x) + plt.yticks(pixel_range, sample_range_y) + plt.xlabel("z[0]") + plt.ylabel("z[1]") + plt.imshow(figure, cmap="Greys_r") + if show: + plt.show() + if path: + plt.savefig(path) + + +def plot_images(images, rows, columns, path = '', show = False): + fig = plt.figure(figsize = (10, 7)) + + for i, image in enumerate(images): + fig.add_subplot(rows, columns, i+1) + try: + plt.imshow(image, cmap = 'binary') + except: + plt.imshow(image.squeeze(), cmap = 'binary') + plt.axis('off') + + if show: + plt.show() + if path: + plt.savefig(path) + +def load_fashion_mnist(): + fashion_mnist = keras.datasets.fashion_mnist + (X_train, y_train), (X_test, y_test) = fashion_mnist.load_data() + fashion_mnist = np.concatenate([X_train, X_test], axis=0) + fashion_mnist = np.expand_dims(fashion_mnist, -1).astype("float16") / 255 + return fashion_mnist + +def load_digits_mnist(): + (x_train, _), (x_test, _) = keras.datasets.mnist.load_data() + mnist_digits = np.concatenate([x_train, x_test], axis=0) + mnist_digits = np.expand_dims(mnist_digits, -1).astype("float16") / 255 + return mnist_digits + + +def load_cifar10(): + # https://pgaleone.eu/neural-networks/deep-learning/2016/12/13/convolutional-autoencoders-in-tensorflow/ + (x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data() + cifar10 = np.concatenate([x_train, x_test], axis=0) + #cifar10 = np.expand_dims(cifar10, -1).astype("float32") / 255 + cifar10 = cifar10.astype("float16") / 255 + return cifar10 + +def load_pkl(pkl_path): + with open(pkl_path, "rb") as input_file: + # For use with batch produced pkl files + #slp = np.expand_dims(pickle.load(input_file),-1).astype("float16")/110000 + # For use with the single manual pkl file + # Assume data is already normalized. + # Assume data is already type selected (float16 or float32). + slp = pickle.load(input_file) + return slp + +def load_many_pkl(pkl_pattern): + # Similar to load_pkl but loads several files + # that pattern match the submitted string + filename_list = glob.glob(pkl_pattern) + + list_arrays = [] + for filename in filename_list: + print('Loading '+filename) + with open(filename, "rb") as input_file: + # For use with batch produced pkl files + # Assume data is already normalized. + # Assume data is already type selected (float16 or float32). + list_arrays.append(pickle.load(input_file)) + + slp = np.concatenate(list_arrays,axis=0) + return slp + +def load_many_grib( + grib_pattern, + data_type=np.float16, + slow_summary=False, + min_max_norm=True): + + import pygrib + + # Similar to load_many_pkl but loads several files + # that pattern match the submitted string. Include + # asterix, question mark, etc. in the _pattern. + filename_list = glob.glob(grib_pattern) + + # For data access, using examples at https://jswhit.github.io/pygrib/api.html + # as a template. Set flag and counter. + tt = 0 + ff = 1 + first_time_step_flag = True + for filename in filename_list: + print('Loading '+filename+' file '+str(ff)+' of '+str(len(filename_list))) + data = pygrib.open(filename).select(name='Mean sea level pressure') + + # Advantage to directly grabbing from grib is that we can + # check for units with timestep['units'] and dimensions + # to preallocate memory. To keep things fast, + # assume same units throughout data set and MSL is in Pa. + # Convert to hPa by divide by 100 -> used in weather maps and + # fits in float16/int16 range of +- 32767. + # We also know in advance the + # size of the data we are about to load so we can preallocate + # a block of data. See https://stackoverflow.com/questions/13215525/how-to-extend-an-array-in-place-in-numpy + # for why we cannot in general extend array size on the fly. + # This is where FORTRAN and C would excel. Get data size for + # first file only. + if first_time_step_flag: + ntime = 0 + for timestep in data: + ntime = ntime + 1 + nfile = len(filename_list) + print("Found "+str(nfile)+" files and "+str(ntime)+" time steps.") + + batch = nfile*ntime + height = data[1]['Nj'] + width = data[1]['Ni'] + channel = 1 + + print("Allocating for "+str(batch)+" x "+str(height)+" x "+str(width)+" x "+str(channel)+" shape") + output = np.zeros([batch,height,width,1],dtype=data_type) + + print("Allocating mean, min, and max storage") + avgs = np.zeros([batch,1],dtype=data_type) + maxs = np.zeros([batch,1],dtype=data_type) + mins = np.zeros([batch,1],dtype=data_type) + stds = np.zeros([batch,1],dtype=data_type) + + # Set flag to avoid repeating. + first_time_step_flag = False + + for timestep in data: + # First time step creates the array + # Add batch and channel dimensions to first and last axes. + # WORKING HERE: Automatic type conversion to ints is truncation!!! + #print('Loading timestep '+str(tt)+' of '+str(batch)) + output[tt,:,:,0] = timestep.values/100 + avgs[tt,0] = timestep['average']/100 + stds[tt,0] = timestep['standardDeviation']/100 + mins[tt,0] = timestep['minimum']/100 + maxs[tt,0] = timestep['maximum']/100 + tt = tt + 1 + + # Move to the next file + ff = ff + 1 + + # Normalize and spit out sanity check statistics at the very end. + # Crosscheck with ['average'] and ['standardDeviation'] + # data attributes. + min_out = np.min(mins) + max_out = np.max(maxs) + del_out = max_out - min_out + + print('Summary: ------------------') + # Taking mean, min, max over all data is time consuming. + # Only included here for cross checking. + if slow_summary: + print('np.mean(output) = '+str(np.mean(output))) + + print('np.mean(avgs) = '+str(np.mean(avgs))) + + if slow_summary: + print('np.min(output) = '+str(np.min(output))) + + print('np.min(mins) = '+str(min_out)) + + if slow_summary: + print('np.max(output) = '+str(np.max(output))) + + print('np.max(maxs) = '+str(max_out)) + # Computing np.std on output takes up huge amount of RAM. + # Coalescing different files' standard deviations is complex, + # these numbers are just for reference and are not to be + # used in production. Since +/- 3std should encompass most + # of the data, max-min ~ 6*mean(stds) + # Actually, max-min > 6*mean(std) because a true combined std + # has an additional term that takes into account the differences + # in the means of the two samples that are being combined. + #print('np.std(output) = '+str(np.std(output))) + print('np.mean(stds) = '+str(np.mean(stds))) + + # Use min-max scaler approach to ensure that all values are + # always between 0 and 1. If we use std as a normalizer, there + # could be values outside +/- 1 (and even outside +/- 3). + # Direct approach makes copy of data -> large RAM usage + #output = (output - np.min(mins))/(np.max(maxs) - np.min(mins)) + # Replace with inplace operations. See https://stackoverflow.com/questions/10149416/numpy-modify-array-in-place for even more general code. + # Allow for NOT normalizing in case data is loaded piece-wise in __main__ + # so normalization can be done over multiple loads. + if min_max_norm: + output -= min_out + output /= del_out + + if slow_summary: + print('After normalization:') + print('np.mean(output) = '+str(np.mean(output))) + print('np.min(output) = '+str(np.min(output))) + print('np.max(output) = '+str(np.max(output))) + + return output, min_out, max_out + +def load_gefs_open_data_registry(dataset): + import xarray as xr + slp_01_ds = xr.open_mfdataset(dataset, engine='cfgrib') + print(slp_01_ds) + # Use MNIST data preproc as template + many_time_steps = False + if many_time_steps: + slp = np.expand_dims( + np.concatenate( + [ + slp_01_ds.PRES_P1_L1_GLL0.values, + slp_02_ds.PRES_P1_L1_GLL0.values + ], + axis=0 + ), + -1 + ).astype("float16") / 110000 + else: + slp = np.expand_dims( + slp_01_ds.PRES_P1_L1_GLL0.values, + -1).astype("float16") / 110000 + return slp + + + + +# Maybe reconstruction loss is big because it is not normalized? +if __name__ == '__main__': + latent_dim = 3 + train = True + model_dir = './digits' + model_dir = './cifar' + model_dir = './gefs' + n_conv_layers = 4 + stride = 2 + kernel_size = 3 + os.makedirs(model_dir, exist_ok = True) + + #data = load_fashion_mnist() + #data = load_digits_mnist() + #data = load_cifar10() + + # Batch size is too small! + #data = load_gefs_open_data_registry("/home/jovyan/work/pres_sfc_2019010100*") + #data = load_pkl('pres_sfc_2019100100_c00.nc.pkl') + #data = load_many_pkl('pres_msl_201*00_c00.nc.pkl') + + # WORKS FINE, but use selective loading, below, to avoid + # making data copies. + #data, data_min, data_max = load_many_grib('./gefs_data/pres_msl_*.grib2') + #data = load_pkl('gefs.pkl') + + # FIXME: Width and height need to be divisible by number of layers! + # + #print('Input summary:') + #print(data.shape) + + # When using a large data set, + # - mean takes a long time, + # - std takes more than 2x RAM + # - train_test_split makes RAM intensive data copies. + # Instead, selectively load different data sets. + #print('Mean: '+str(np.mean(data))) + # More than doubles RAM usage to find std. + #print('Std: '+str(np.std(data))) + #X_train, X_test = train_test_split(data) + #X_train, X_valid = train_test_split(X_train) + + # Load and then normalize all data based on + # same absolute min/max values. It looks like + # if data is not in np.float32, it will be internally + # converted to np.float32 in vae.fit, so there may be no + # way to cut memory useage there. + X_train, train_min, train_max = load_many_grib( + './gefs_data/pres_msl_201[78]*.grib2', + data_type=np.float32, + min_max_norm=False) + X_test, test_min, test_max = load_many_grib( + './gefs_data/pres_msl_20190[13579]*.grib2', + data_type=np.float32, + min_max_norm=False) + X_valid, valid_min, valid_max = load_many_grib( + './gefs_data/pres_msl_20190[2468]*.grib2', + data_type=np.float32, + min_max_norm=False) + + all_min = np.min([train_min,test_min,valid_min]) + all_max = np.max([train_max,test_max,valid_max]) + all_del = all_max - all_min + + print('Normalize X_train...') + X_train -= all_min + X_train /= all_del + + print('Normalize X_test...') + X_test -= all_min + X_test /= all_del + + print('Normalize X_valid...') + X_valid -= all_min + X_valid /= all_del + + print('Testing shape') + print(np.shape(X_test)) + + print('Training shape') + print(np.shape(X_train)) + + print('Validaiton shape') + print(np.shape(X_valid)) + + batch_size, height, width, channels = X_train.shape + # Plot some inputs + rows = 3 + columns = 4 + sample_input_images = X_train[0:rows*columns] + #plot_images(sample_input_images, rows, columns, path = os.path.join(model_dir, 'sample_input.png')) + + # More conv layers reduces the total parameters at first but then it does not! + encoder = build_encoder(latent_dim, height, width, channels, n_conv_layers, kernel_size, stride, base_filters = 16) + decoder = build_decoder(latent_dim, height, width, channels, n_conv_layers, kernel_size, stride, base_filters = 16) + vae = VAE(encoder, decoder, height*width) + #vae.compile(optimizer=keras.optimizers.Adam()) + vae.compile(optimizer = 'rmsprop') + if train: + early_stopping_cb = keras.callbacks.EarlyStopping(patience = 7, restore_best_weights = True) + history = vae.fit( + X_train, epochs = 5, batch_size = 8, + callbacks = [early_stopping_cb], + validation_data = (X_valid,) + ) + vae.save_weights(os.path.join(model_dir, 'vae')) + hist_pd = pd.DataFrame(history.history) + hist_pd.to_csv(os.path.join(model_dir, 'history.csv'), index = False) + test_loss = vae.evaluate(X_test) + test_loss = dict(zip(["loss", "reconstruction_loss", "kl_loss"], test_loss)) + print('Test loss:') + print(test_loss) + with open(os.path.join(model_dir, 'test_loss.json'), 'w') as json_file: + json.dump(test_loss, json_file, indent = 4) + + else: + vae.load_weights(os.path.join(model_dir, 'vae')) + + if latent_dim == 2: + # Only works if: + if height == width: + #plot_latent_space(vae, height, channels, path = os.path.join(model_dir, 'latent_space.png')) + print('Not plotting anything.') + + # Generating new images + codings = tf.random.normal(shape = [12, latent_dim]) + images = vae.decoder(codings).numpy() + #plot_images(images, 3, 4, path = os.path.join(model_dir, 'generated.png')) + + # Semantic interpolation + codings_grid = tf.reshape(codings, [1, 3, 4, latent_dim]) + larger_grid = tf.image.resize(codings_grid, size = [5, 7]) + interpolated_codings = tf.reshape(larger_grid, [-1, latent_dim]) + images = vae.decoder(interpolated_codings).numpy() + #plot_images(images, 5, 7, path = os.path.join(model_dir, 'interpolated.png')) + + # Encode / decode images + # FIXME: Maybe add a predict method? + # sample_output_images = vae.predict(sample_input_images) + z_mean, z_log_var, z = vae.encoder(sample_input_images) + sample_output_images = vae.decoder(z) + #plot_images(sample_output_images, rows, columns, path = os.path.join(model_dir, 'sample_output.png')) + + diff --git a/run_on_cluster/cvae-weather-ensemble/old-code/old-README.md b/run_on_cluster/cvae-weather-ensemble/old-code/old-README.md new file mode 100644 index 0000000..b24348a --- /dev/null +++ b/run_on_cluster/cvae-weather-ensemble/old-code/old-README.md @@ -0,0 +1,98 @@ +# VM setup +1. Testing whether I can use a lower cost VM (n1-standard-4) to `conda install` and pre-download data and save data on image and then reboot image as a higher cost (a2-highgpu-1g) for the actual training. This seems to work BUT may require reinstalling Linux headers when going from low->high cost and then rebooting the VM, e.g.: +```bash +# Booted as a2-highgpu-1g after run as n1-standard-4 -> error message. +sudo apt-get install linux-headers-4.19.0-18-cloud-amd64 +reboot +``` +See `/var/log/apt/history.log` for history of install requests (includes the tcsh install in #4 below). Also, note that while it is possible to switch between n1-standards and a1-highgpu, you **cannot** switch between e1 class machines and a1 machines. + +2. Testing whether I can use pygrib as a direct load from grib -> YES, but need to manually add time axis and concatenate time steps within files in addition to concatenating time steps between files. See load_many_grib in main_cvae.py. + +3. Using the c1-deeplearning-tf-2-5-cu110-v20210714-debian-10 image as a starting point. It has conda and I will attempt to install default pygrib and parsl into the base conda environment. It appears that all the other key packages are already installed (numpy, pandas, matplotlib, json, os, scikit-learn, tensorflow, pickle, glob). + +4. Actual steps executed on an n1-standard-4: +```bash +conda install -c conda-forge pygrib # Could not solve env before package download but installed anyway. +conda install -c conda-forge parsl # Installed without issue (downgrading sqlalchemy) +sudo apt-get install tcsh # Used for some of the batch scripts in this dir, could be bypassed. +``` + +5. Testing data loading: +```python +import numpy as np +import pygrib +file_name = "./gefs_data/pres_msl_2019110100_c00.grib2" +file_data = pygrib.open(file_name) +msl = file_data.select(name='Mean sea level pressure') +data = msl[0].values +np.shape(data) +(721, 1440) +``` + +6. Testing the use of smaller data types. Please see [this blog](https://towardsdatascience.com/memory-efficient-data-science-types-53423d48ba1d) for a great intro to which data types are supported and how. In a nutshell, float16 is dangerous to use because it has inconsistent support. Float64 is the default numpy type, float32 should work easily with `data.astype(np.float32)`. The most commonly supported ints are int8 and int32, but there is no mention of the reliability of int16, which is what I want to use. + +# Data sizes: + +1. Each GEFS 2D field with 80 time steps (10 days at 3 hours) is 57MB in grib2 +2. Converted to netcdf, the field with 80 time steps is 317MB +3. A whole year in NetCDF is 12 months x 3 fields/month * 317MB = 11GB +4. Converted to pkl, each file is 634MB x 36 = 22GB +5. We need to figure out a robust and efficient way to go from grib2 to load to memory. Currently, this multistage approach is slow and takes up a lot of space. + +# Pipeline summary: + +1. wget to download from NOAA's S3 bucket +2. cdo to convert to nc (`apt-get install cdo`) +3. pyferret to load as numpy array (`conda install -y -c conda-forge pyferret`) +4. save as pkl for use outside of pyferret container b/c pyferret will not install in Conda (like pynio and cfgrib) + +This means ignore `batch_grib2pkl.csh` because pynio/cfgrib don't work reliably. + +# Tensorflow: + +1. docker pull tensorflow/tensorflow +2. Run this container, e.g. sudo docker run -it -v/home/sfgary/sbir-learnerworks-doc/NOAA_JTTI:/tmp/noaa tensorflow:tensorflow /bin/bash +3. pip install pandas +4. pip install scikit-learn +5. pip install matplotlib + +# Grids + +Since we have broken away from grib/netcdf, we need to explicitly +bring back the grid information. Here, I used ncdump -h on the lon +and lat variables in .nc files, manually removed the header, and +then used the following text pipeline to make into a column vector +in a text file: + +cat lon.txt | tr -d '}' | tr -d ';' | tr -d '\n' | tr -d ' ' | tr ',' '\n' > lon.x +cat lat.txt | tr -d '}' | tr -d ';' | tr -d '\n' | tr -d ' ' | tr ',' '\n' > lat.y + +# Training the ML model + +For two years of data (36 files each year, each file has 80 3h time steps = 10 days) +(Used 2018 and 2019) + +Input summary: +(5760, 721, 1440, 1) +Mean: 0.9185315259371455 +Std: 0.012157332330270077 +Testing shape +(1440, 721, 1440, 1) +Training shape +(3240, 721, 1440, 1) +Validaiton shape +(1080, 721, 1440, 1) + +Takes about 10 mins per epoch, uses ~128GB RAM, linear scaling wrt batch size seems +confirmed wrt previous runs. Running on 32CPU, all utilized but at around 50%. + +Training time: +Start: 2021-11-14 15:16:14 +End: + +# Using the ML model + +The model weights are in ./gefs/vae.data-00000-of-000001. The Python/Tensorflow +code that defines the model needs to be loaded first and then the weights are +loaded. \ No newline at end of file diff --git a/run_on_cluster/cvae-weather-ensemble/scripts/.ipynb_checkpoints/batch_download-checkpoint.csh b/run_on_cluster/cvae-weather-ensemble/scripts/.ipynb_checkpoints/batch_download-checkpoint.csh deleted file mode 100755 index 44789e3..0000000 --- a/run_on_cluster/cvae-weather-ensemble/scripts/.ipynb_checkpoints/batch_download-checkpoint.csh +++ /dev/null @@ -1,24 +0,0 @@ -#!/bin/tcsh -f -#======================== -# Download a year of GEFS -# output. -#======================= - -# Choose years -#set year_list = ( 2018 2019 ) -set year_list = ( 2012 2013 2014 2015 2016 2017 ) - -# Choose all months. -set month_list = ( 01 02 03 04 05 06 07 08 09 10 11 12 ) - -# Forecasts are 10 days long, so this provides converage of whole year. -set day_list = ( 01 10 20 ) - -foreach year ( $year_list ) - foreach month ( $month_list ) - foreach day ( $day_list ) - wget https://noaa-gefs-retrospective.s3.amazonaws.com/GEFSv12/reforecast/${year}/${year}${month}${day}00/c00/Days%3A1-10/pres_msl_${year}${month}${day}00_c00.grib2 - mv pres_msl_${year}${month}${day}00_c00.grib2 ./gefs_data - end - end -end diff --git a/run_on_cluster/cvae-weather-ensemble/scripts/.ipynb_checkpoints/cvae-checkpoint.py b/run_on_cluster/cvae-weather-ensemble/scripts/.ipynb_checkpoints/cvae-checkpoint.py new file mode 100644 index 0000000..ef1b8fe --- /dev/null +++ b/run_on_cluster/cvae-weather-ensemble/scripts/.ipynb_checkpoints/cvae-checkpoint.py @@ -0,0 +1,428 @@ +# https://keras.io/examples/generative/vae/ +import os, json +import netCDF4 +import glob + +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt + +import tensorflow as tf +from tensorflow import keras +from keras import layers + +from sklearn.model_selection import train_test_split + + +# model defintions ---------------------------------------------------------------------------------------------- + +class Sampling(layers.Layer): + """Uses (z_mean, z_log_var) to sample z, the vector encoding a digit.""" + + def call(self, inputs): + z_mean, z_log_var = inputs + batch = tf.shape(z_mean)[0] + dim = tf.shape(z_mean)[1] + epsilon = tf.keras.backend.random_normal(shape = (batch, dim)) + return z_mean + tf.exp(0.5 * z_log_var) * epsilon + +def build_encoder(latent_dim): + encoder_inputs = keras.Input(shape=(721, 1440, 1)) + + x = layers.Conv2D(32, 11, activation="relu", strides=[9, 10], padding="valid")(encoder_inputs) + x = layers.Conv2D(64, [5,9], activation="relu", strides=[5, 9], padding="valid")(x) + x = layers.Flatten()(x) + x = layers.Dense(16, activation="relu")(x) + + z_mean = layers.Dense(latent_dim, name="z_mean")(x) + z_log_var = layers.Dense(latent_dim, name="z_log_var")(x) + z = Sampling()([z_mean, z_log_var]) + + encoder = keras.Model(encoder_inputs, [z_mean, z_log_var, z], name="encoder") + + print(encoder.summary()) + return encoder + +def build_decoder(latent_dim): + latent_inputs = keras.Input(shape=(latent_dim,)) + + x = layers.Dense(15 * 15 * 64, activation="relu")(latent_inputs) + x = layers.Reshape((15, 15, 64))(x) + # FIXME - there is something wrong here, but at least there is a pattern. + # Using output_padding as a fudge factor -> it may be that there is exactly + # one "missing" filter stamp/convolution because for both Conv2DTranspose + # operations, output_padding is set to maximum it could be in both dims + # (i.e. exactly one less than the stride of each filter). + x = layers.Conv2DTranspose(64, [5, 9], activation="relu", strides=[5,9], padding="valid", output_padding=[4, 8])(x) + x = layers.Conv2DTranspose(32, 11, activation="relu", strides=[9,10], padding="valid", output_padding=[8, 9])(x) + + decoder_outputs = layers.Conv2DTranspose(1, 3, activation="sigmoid", padding="same")(x) + decoder = keras.Model(latent_inputs, decoder_outputs, name="decoder") + + print(decoder.summary()) + return decoder + +class VAE(keras.Model): + def __init__(self, encoder, decoder, **kwargs): + super(VAE, self).__init__(**kwargs) + self.encoder = encoder + self.decoder = decoder + self.total_loss_tracker = keras.metrics.Mean(name = "total_loss") + self.reconstruction_loss_tracker = keras.metrics.Mean(name = "reconstruction_loss") + self.kl_loss_tracker = keras.metrics.Mean(name = "kl_loss") + + @property + def metrics(self): + return [ + self.total_loss_tracker, + self.reconstruction_loss_tracker, + self.kl_loss_tracker, + ] + + def train_step(self, data): + + with tf.GradientTape() as tape: + z_mean, z_log_var, z = self.encoder(data) + reconstruction = self.decoder(z) + # FIXME: Normalize loss with the number of features (28 * 28) + n_features = 28 * 28 + reconstruction_loss = tf.reduce_mean( + tf.reduce_sum( + keras.losses.binary_crossentropy(data, reconstruction), axis = (1, 2) + ) + ) / n_features + kl_loss = -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var)) + kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis = 1)) / n_features + total_loss = (reconstruction_loss + kl_loss) + grads = tape.gradient(total_loss, self.trainable_weights) + self.optimizer.apply_gradients(zip(grads, self.trainable_weights)) + self.total_loss_tracker.update_state(total_loss) + self.reconstruction_loss_tracker.update_state(reconstruction_loss) + self.kl_loss_tracker.update_state(kl_loss) + + return { + "loss": self.total_loss_tracker.result(), + "reconstruction_loss": self.reconstruction_loss_tracker.result(), + "kl_loss": self.kl_loss_tracker.result(), + } + + # Needed to validate (validation loss) and to evaluate + def test_step(self, data): + if type(data) == tuple: + data, _ = data + + z_mean, z_log_var, z = self.encoder(data) + reconstruction = self.decoder(z) + # FIXME: Normalize loss with the number of features (28 * 28) + n_features = 28 * 28 + reconstruction_loss = tf.reduce_mean( + tf.reduce_sum( + keras.losses.binary_crossentropy(data, reconstruction), axis = (1, 2) + ) + ) / n_features + kl_loss = -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var)) + kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis = 1)) / n_features + total_loss = (reconstruction_loss + kl_loss) + # grads = tape.gradient(total_loss, self.trainable_weights) + # self.optimizer.apply_gradients(zip(grads, self.trainable_weights)) + self.total_loss_tracker.update_state(total_loss) + self.reconstruction_loss_tracker.update_state(reconstruction_loss) + self.kl_loss_tracker.update_state(kl_loss) + + return { + "loss": self.total_loss_tracker.result(), + "reconstruction_loss": self.reconstruction_loss_tracker.result(), + "kl_loss": self.kl_loss_tracker.result(), + } + +# --------------------------------------------------------------------------------------------------------------- + +# CNN require huge amounts of RAM, specially during training +# Computational requirements for a single CNN layer: +# - Number of parameters: (+1 is the bias term) +# (filter width * filter height * channels + 1 ) * feature maps +# - Number of float multiplications: +# feature maps * feature map width * feature map height * filter width * filfet height * channels +# ---> For the first layer feature map width and height are the inputs width and height divided by the strides!! +# - Output Memory: +# feature maps * feature map width * feature map height * 32 bits * training batch size +# ---> Reduce batch size to fir in memory! +# Memory for multiple layers: +# - During training: The sum of all the layers +# - During inference: Two consecutive layers + +# If you have memory issues: +# 1. Reuduce batch size +# 2. Increase stride +# 3. Remove layers +# 4. Use 16-bit floats +# 5. Distribute across multiple devices + +# Pooling layers: shrink the input image to reduce computational load, memory usage and number of parameters +# - Pooling neurons have no weights +# - keras.layers.MaxPool2D(pool_size=(2, 2), strides=None, padding='valid', data_format=None) +# - AvgPool2D +# - Only takes the maximum (or average, minimum, etc) value of the pooling_size window +# - If stride > 1 reduces the number of parameters. Stride defaults to pool_size +# - Introduces a level of invariance to small translations and some rotational and scale invariance +# - Invariance is desirable for classification but undersirable for semantic segmentation +# - Invariance is desirable if a small change in the inputs should lead to a small change on the outputs +# - MaxPool2D typically has better performance +# - Can also be applied to depth (channels) instead of spatial direction (see page 469) +# - GlobalAvgPool2D: Calculates the average of the entire feature map + +# Typical CNN architecture: +# - Convolutions(s) with same number of feature maps --> Pooling --> Convolution(s) --> Pooling --> repeat --> Flatten --> Fully connected layer +# - Image gets smaller (less width and height) and deeper (more feature maps) --> Dense --> Dropout --> Repeat +# - Avoid large kernels (window sizes)--> use more layers (except in first layer) +# - Number of repetitions is a hyperparameter to tune +# - Double number of filters after each pooling layer +# - Add keras.layers.Dropout(0.5) to reduce overfitting between dense layers + + +def calculate_final_shape(height, num_conv_2d_layers, stride): + for n in range(num_conv_2d_layers): + height = int(np.ceil(height / stride)) + return height + +# Only works for stride 2... +def calculate_output_paddings(height, num_conv_2d_layers, stride): + output_paddings = [] + for n in range(num_conv_2d_layers): + output_paddings.append(stride - (height % stride) - 1) + height = int(np.ceil(height / stride)) + + output_paddings.reverse() + return output_paddings + +# Works for stride 3 but may need extra work/testing +def _calculate_output_paddings(height, num_conv_layers, stride): + output_paddings = [] + encoder_heights = [] + decoder_heights = [] + output_paddings = [] + encoder_heights.append(height) + + for n in range(num_conv_layers): + height = int(np.ceil(height / stride)) + encoder_heights.append(height) + + encoder_heights.reverse() + + decoder_heights.append(height) + for n in range(num_conv_layers): + height = int(height * stride) + padding = height - encoder_heights[n+1] + print(padding) + #output_paddings.append(padding + stride - int(stride/2) - 2) + height -= padding + decoder_heights.append(height) + if padding == 0: + padding = 2 + elif padding == 2: + padding = 0 + output_paddings.append(padding) + + return output_paddings + +# Only works if latent_dim = 2 +def plot_latent_space(vae, digit_size, channels, n=30, figsize=15, show = False, path = ''): + # display a n*n 2D manifold of digits + #digit_size = 28 + scale = 1.0 + figure = np.zeros((digit_size * n, digit_size * n)) + # linearly spaced coordinates corresponding to the 2D plot + # of digit classes in the latent space + grid_x = np.linspace(-scale, scale, n) + grid_y = np.linspace(-scale, scale, n)[::-1] + + for i, yi in enumerate(grid_y): + for j, xi in enumerate(grid_x): + z_sample = np.array([[xi, yi]]) + x_decoded = vae.decoder.predict(z_sample) + digit = x_decoded[0].reshape(digit_size, digit_size, channels) + figure[ + i * digit_size : (i + 1) * digit_size, + j * digit_size : (j + 1) * digit_size, + ] = digit[:,:,0] + + plt.figure(figsize=(figsize, figsize)) + start_range = digit_size // 2 + end_range = n * digit_size + start_range + pixel_range = np.arange(start_range, end_range, digit_size) + sample_range_x = np.round(grid_x, 1) + sample_range_y = np.round(grid_y, 1) + plt.xticks(pixel_range, sample_range_x) + plt.yticks(pixel_range, sample_range_y) + plt.xlabel("z[0]") + plt.ylabel("z[1]") + plt.imshow(figure, cmap="Greys_r") + if show: + plt.show() + if path: + plt.savefig(path) + + +def plot_images(images, rows, columns, path = '', show = False): + fig = plt.figure(figsize = (10, 7)) + + for i, image in enumerate(images): + fig.add_subplot(rows, columns, i+1) + try: + plt.imshow(image, cmap = 'binary') + except: + plt.imshow(image.squeeze(), cmap = 'binary') + plt.axis('off') + + if show: + plt.show() + if path: + plt.savefig(path) + + + + +# Maybe reconstruction loss is big because it is not normalized? +if __name__ == '__main__': + latent_dim = 3 + train = True + model_dir = './digits' + model_dir = './cifar' + model_dir = './gefs' + n_conv_layers = 4 + stride = 2 + kernel_size = 3 + os.makedirs(model_dir, exist_ok = True) + + #data = load_fashion_mnist() + #data = load_digits_mnist() + #data = load_cifar10() + + # Batch size is too small! + #data = load_gefs_open_data_registry("/home/jovyan/work/pres_sfc_2019010100*") + #data = load_pkl('pres_sfc_2019100100_c00.nc.pkl') + #data = load_many_pkl('pres_msl_201*00_c00.nc.pkl') + + # WORKS FINE, but use selective loading, below, to avoid + # making data copies. + #data, data_min, data_max = load_many_grib('./gefs_data/pres_msl_*.grib2') + #data = load_pkl('gefs.pkl') + + # FIXME: Width and height need to be divisible by number of layers! + # + #print('Input summary:') + #print(data.shape) + + # When using a large data set, + # - mean takes a long time, + # - std takes more than 2x RAM + # - train_test_split makes RAM intensive data copies. + # Instead, selectively load different data sets. + #print('Mean: '+str(np.mean(data))) + # More than doubles RAM usage to find std. + #print('Std: '+str(np.std(data))) + #X_train, X_test = train_test_split(data) + #X_train, X_valid = train_test_split(X_train) + + # Load and then normalize all data based on + # same absolute min/max values. It looks like + # if data is not in np.float32, it will be internally + # converted to np.float32 in vae.fit, so there may be no + # way to cut memory useage there. + X_train, train_min, train_max = load_many_grib( + './gefs_data/pres_msl_201[78]*.grib2', + data_type=np.float32, + min_max_norm=False) + X_test, test_min, test_max = load_many_grib( + './gefs_data/pres_msl_20190[13579]*.grib2', + data_type=np.float32, + min_max_norm=False) + X_valid, valid_min, valid_max = load_many_grib( + './gefs_data/pres_msl_20190[2468]*.grib2', + data_type=np.float32, + min_max_norm=False) + + all_min = np.min([train_min,test_min,valid_min]) + all_max = np.max([train_max,test_max,valid_max]) + all_del = all_max - all_min + + print('Normalize X_train...') + X_train -= all_min + X_train /= all_del + + print('Normalize X_test...') + X_test -= all_min + X_test /= all_del + + print('Normalize X_valid...') + X_valid -= all_min + X_valid /= all_del + + print('Testing shape') + print(np.shape(X_test)) + + print('Training shape') + print(np.shape(X_train)) + + print('Validaiton shape') + print(np.shape(X_valid)) + + batch_size, height, width, channels = X_train.shape + # Plot some inputs + rows = 3 + columns = 4 + sample_input_images = X_train[0:rows*columns] + #plot_images(sample_input_images, rows, columns, path = os.path.join(model_dir, 'sample_input.png')) + + # More conv layers reduces the total parameters at first but then it does not! + encoder = build_encoder(latent_dim, height, width, channels, n_conv_layers, kernel_size, stride, base_filters = 16) + decoder = build_decoder(latent_dim, height, width, channels, n_conv_layers, kernel_size, stride, base_filters = 16) + vae = VAE(encoder, decoder, height*width) + #vae.compile(optimizer=keras.optimizers.Adam()) + vae.compile(optimizer = 'rmsprop') + if train: + early_stopping_cb = keras.callbacks.EarlyStopping(patience = 7, restore_best_weights = True) + history = vae.fit( + X_train, epochs = 5, batch_size = 8, + callbacks = [early_stopping_cb], + validation_data = (X_valid,) + ) + vae.save_weights(os.path.join(model_dir, 'vae')) + hist_pd = pd.DataFrame(history.history) + hist_pd.to_csv(os.path.join(model_dir, 'history.csv'), index = False) + test_loss = vae.evaluate(X_test) + test_loss = dict(zip(["loss", "reconstruction_loss", "kl_loss"], test_loss)) + print('Test loss:') + print(test_loss) + with open(os.path.join(model_dir, 'test_loss.json'), 'w') as json_file: + json.dump(test_loss, json_file, indent = 4) + + else: + vae.load_weights(os.path.join(model_dir, 'vae')) + + if latent_dim == 2: + # Only works if: + if height == width: + #plot_latent_space(vae, height, channels, path = os.path.join(model_dir, 'latent_space.png')) + print('Not plotting anything.') + + # Generating new images + codings = tf.random.normal(shape = [12, latent_dim]) + images = vae.decoder(codings).numpy() + #plot_images(images, 3, 4, path = os.path.join(model_dir, 'generated.png')) + + # Semantic interpolation + codings_grid = tf.reshape(codings, [1, 3, 4, latent_dim]) + larger_grid = tf.image.resize(codings_grid, size = [5, 7]) + interpolated_codings = tf.reshape(larger_grid, [-1, latent_dim]) + images = vae.decoder(interpolated_codings).numpy() + #plot_images(images, 5, 7, path = os.path.join(model_dir, 'interpolated.png')) + + # Encode / decode images + # FIXME: Maybe add a predict method? + # sample_output_images = vae.predict(sample_input_images) + z_mean, z_log_var, z = vae.encoder(sample_input_images) + sample_output_images = vae.decoder(z) + #plot_images(sample_output_images, rows, columns, path = os.path.join(model_dir, 'sample_output.png')) + + diff --git a/run_on_cluster/cvae-weather-ensemble/scripts/.ipynb_checkpoints/get_data-checkpoint.py b/run_on_cluster/cvae-weather-ensemble/scripts/.ipynb_checkpoints/get_data-checkpoint.py index d6c4d18..0414508 100644 --- a/run_on_cluster/cvae-weather-ensemble/scripts/.ipynb_checkpoints/get_data-checkpoint.py +++ b/run_on_cluster/cvae-weather-ensemble/scripts/.ipynb_checkpoints/get_data-checkpoint.py @@ -6,14 +6,14 @@ def download_file(year, month, day, ensemble, data_predir): os.system(f'wget -q -P {data_predir} https://noaa-gefs-retrospective.s3.amazonaws.com/GEFSv12/reforecast/{year}/{year}{month}{day}00/{ensemble}/Days%3A1-10/pres_msl_{year}{month}{day}00_{ensemble}.grib2') # convert -def convert_file(year, month, day, ensemble, data_predir): - if f'pres_msl_{year}{month}{day}00_{ensemble}.grib2' in os.listdir(data_predir): +def convert_file(year, month, day, ensemble, data_dir): + if f'pres_msl_{year}{month}{day}00_{ensemble}.nc' not in os.listdir(data_dir): os.system(f'cdo -f nc copy ./gefs_data/pres_msl_{year}{month}{day}00_{ensemble}.grib2 ./gefs_data/converted/pres_msl_{year}{month}{day}00_{ensemble}.nc') # subset def subset_file(data_file, data_dir): - if '.nc' in data_file and f'{data_dir}subset_{f}' not in os.listdir(data_dir): - os.system(f'ncks -v msl -d time,1,79,2 {data_dir}{f} -O {data_dir}subset_{f}') + if '.nc' in data_file and f'{data_dir}subset_{data_file}' not in os.listdir(data_dir): + os.system(f'ncks -v msl -d time,1,79,2 {data_dir}{data_file} -O {data_dir}subset_{data_file}') # delete (all) def remove_data(data_predir): diff --git a/run_on_cluster/cvae-weather-ensemble/scripts/.ipynb_checkpoints/run_dvcgit-checkpoint.sh b/run_on_cluster/cvae-weather-ensemble/scripts/.ipynb_checkpoints/run_dvcgit-checkpoint.sh index c420aad..9169085 100644 --- a/run_on_cluster/cvae-weather-ensemble/scripts/.ipynb_checkpoints/run_dvcgit-checkpoint.sh +++ b/run_on_cluster/cvae-weather-ensemble/scripts/.ipynb_checkpoints/run_dvcgit-checkpoint.sh @@ -1,4 +1,4 @@ -#!/bin/tcsh -f +#!/bin/bash # Start Conda and select env (you will need to cross-check the paths and env name) @@ -9,17 +9,13 @@ conda activate cvae_env # $1 stores the first entry on the command line that launches # the shell script and so on. Add as many as you need. -history_file_name = $1 -model_file_name = $2 -branch_name = $3 -commit_message = $4 +file_name=$1 +commit_message="$2" -dvc add $history_file_name -git add .gitignore ${history_file_name}.dvc - -dvc add $model_file_name -git add .gitignore ${model_file_name}.dvc +dvc add "$file_name" +git add .gitignore +git add "${file_name}.dvc" dvc push -git commit -m $commit_message -git push origin $branch_name \ No newline at end of file +git commit -m "$commit_message" +git push \ No newline at end of file diff --git a/run_on_cluster/cvae-weather-ensemble/scripts/__pycache__/cvae.cpython-39.pyc b/run_on_cluster/cvae-weather-ensemble/scripts/__pycache__/cvae.cpython-39.pyc new file mode 100644 index 0000000..e0a0faa Binary files /dev/null and b/run_on_cluster/cvae-weather-ensemble/scripts/__pycache__/cvae.cpython-39.pyc differ diff --git a/run_on_cluster/cvae-weather-ensemble/scripts/__pycache__/get_data.cpython-39.pyc b/run_on_cluster/cvae-weather-ensemble/scripts/__pycache__/get_data.cpython-39.pyc index 069eaa9..17b6f92 100644 Binary files a/run_on_cluster/cvae-weather-ensemble/scripts/__pycache__/get_data.cpython-39.pyc and b/run_on_cluster/cvae-weather-ensemble/scripts/__pycache__/get_data.cpython-39.pyc differ diff --git a/run_on_cluster/cvae-weather-ensemble/scripts/cvae.py b/run_on_cluster/cvae-weather-ensemble/scripts/cvae.py new file mode 100644 index 0000000..ef1b8fe --- /dev/null +++ b/run_on_cluster/cvae-weather-ensemble/scripts/cvae.py @@ -0,0 +1,428 @@ +# https://keras.io/examples/generative/vae/ +import os, json +import netCDF4 +import glob + +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt + +import tensorflow as tf +from tensorflow import keras +from keras import layers + +from sklearn.model_selection import train_test_split + + +# model defintions ---------------------------------------------------------------------------------------------- + +class Sampling(layers.Layer): + """Uses (z_mean, z_log_var) to sample z, the vector encoding a digit.""" + + def call(self, inputs): + z_mean, z_log_var = inputs + batch = tf.shape(z_mean)[0] + dim = tf.shape(z_mean)[1] + epsilon = tf.keras.backend.random_normal(shape = (batch, dim)) + return z_mean + tf.exp(0.5 * z_log_var) * epsilon + +def build_encoder(latent_dim): + encoder_inputs = keras.Input(shape=(721, 1440, 1)) + + x = layers.Conv2D(32, 11, activation="relu", strides=[9, 10], padding="valid")(encoder_inputs) + x = layers.Conv2D(64, [5,9], activation="relu", strides=[5, 9], padding="valid")(x) + x = layers.Flatten()(x) + x = layers.Dense(16, activation="relu")(x) + + z_mean = layers.Dense(latent_dim, name="z_mean")(x) + z_log_var = layers.Dense(latent_dim, name="z_log_var")(x) + z = Sampling()([z_mean, z_log_var]) + + encoder = keras.Model(encoder_inputs, [z_mean, z_log_var, z], name="encoder") + + print(encoder.summary()) + return encoder + +def build_decoder(latent_dim): + latent_inputs = keras.Input(shape=(latent_dim,)) + + x = layers.Dense(15 * 15 * 64, activation="relu")(latent_inputs) + x = layers.Reshape((15, 15, 64))(x) + # FIXME - there is something wrong here, but at least there is a pattern. + # Using output_padding as a fudge factor -> it may be that there is exactly + # one "missing" filter stamp/convolution because for both Conv2DTranspose + # operations, output_padding is set to maximum it could be in both dims + # (i.e. exactly one less than the stride of each filter). + x = layers.Conv2DTranspose(64, [5, 9], activation="relu", strides=[5,9], padding="valid", output_padding=[4, 8])(x) + x = layers.Conv2DTranspose(32, 11, activation="relu", strides=[9,10], padding="valid", output_padding=[8, 9])(x) + + decoder_outputs = layers.Conv2DTranspose(1, 3, activation="sigmoid", padding="same")(x) + decoder = keras.Model(latent_inputs, decoder_outputs, name="decoder") + + print(decoder.summary()) + return decoder + +class VAE(keras.Model): + def __init__(self, encoder, decoder, **kwargs): + super(VAE, self).__init__(**kwargs) + self.encoder = encoder + self.decoder = decoder + self.total_loss_tracker = keras.metrics.Mean(name = "total_loss") + self.reconstruction_loss_tracker = keras.metrics.Mean(name = "reconstruction_loss") + self.kl_loss_tracker = keras.metrics.Mean(name = "kl_loss") + + @property + def metrics(self): + return [ + self.total_loss_tracker, + self.reconstruction_loss_tracker, + self.kl_loss_tracker, + ] + + def train_step(self, data): + + with tf.GradientTape() as tape: + z_mean, z_log_var, z = self.encoder(data) + reconstruction = self.decoder(z) + # FIXME: Normalize loss with the number of features (28 * 28) + n_features = 28 * 28 + reconstruction_loss = tf.reduce_mean( + tf.reduce_sum( + keras.losses.binary_crossentropy(data, reconstruction), axis = (1, 2) + ) + ) / n_features + kl_loss = -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var)) + kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis = 1)) / n_features + total_loss = (reconstruction_loss + kl_loss) + grads = tape.gradient(total_loss, self.trainable_weights) + self.optimizer.apply_gradients(zip(grads, self.trainable_weights)) + self.total_loss_tracker.update_state(total_loss) + self.reconstruction_loss_tracker.update_state(reconstruction_loss) + self.kl_loss_tracker.update_state(kl_loss) + + return { + "loss": self.total_loss_tracker.result(), + "reconstruction_loss": self.reconstruction_loss_tracker.result(), + "kl_loss": self.kl_loss_tracker.result(), + } + + # Needed to validate (validation loss) and to evaluate + def test_step(self, data): + if type(data) == tuple: + data, _ = data + + z_mean, z_log_var, z = self.encoder(data) + reconstruction = self.decoder(z) + # FIXME: Normalize loss with the number of features (28 * 28) + n_features = 28 * 28 + reconstruction_loss = tf.reduce_mean( + tf.reduce_sum( + keras.losses.binary_crossentropy(data, reconstruction), axis = (1, 2) + ) + ) / n_features + kl_loss = -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var)) + kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis = 1)) / n_features + total_loss = (reconstruction_loss + kl_loss) + # grads = tape.gradient(total_loss, self.trainable_weights) + # self.optimizer.apply_gradients(zip(grads, self.trainable_weights)) + self.total_loss_tracker.update_state(total_loss) + self.reconstruction_loss_tracker.update_state(reconstruction_loss) + self.kl_loss_tracker.update_state(kl_loss) + + return { + "loss": self.total_loss_tracker.result(), + "reconstruction_loss": self.reconstruction_loss_tracker.result(), + "kl_loss": self.kl_loss_tracker.result(), + } + +# --------------------------------------------------------------------------------------------------------------- + +# CNN require huge amounts of RAM, specially during training +# Computational requirements for a single CNN layer: +# - Number of parameters: (+1 is the bias term) +# (filter width * filter height * channels + 1 ) * feature maps +# - Number of float multiplications: +# feature maps * feature map width * feature map height * filter width * filfet height * channels +# ---> For the first layer feature map width and height are the inputs width and height divided by the strides!! +# - Output Memory: +# feature maps * feature map width * feature map height * 32 bits * training batch size +# ---> Reduce batch size to fir in memory! +# Memory for multiple layers: +# - During training: The sum of all the layers +# - During inference: Two consecutive layers + +# If you have memory issues: +# 1. Reuduce batch size +# 2. Increase stride +# 3. Remove layers +# 4. Use 16-bit floats +# 5. Distribute across multiple devices + +# Pooling layers: shrink the input image to reduce computational load, memory usage and number of parameters +# - Pooling neurons have no weights +# - keras.layers.MaxPool2D(pool_size=(2, 2), strides=None, padding='valid', data_format=None) +# - AvgPool2D +# - Only takes the maximum (or average, minimum, etc) value of the pooling_size window +# - If stride > 1 reduces the number of parameters. Stride defaults to pool_size +# - Introduces a level of invariance to small translations and some rotational and scale invariance +# - Invariance is desirable for classification but undersirable for semantic segmentation +# - Invariance is desirable if a small change in the inputs should lead to a small change on the outputs +# - MaxPool2D typically has better performance +# - Can also be applied to depth (channels) instead of spatial direction (see page 469) +# - GlobalAvgPool2D: Calculates the average of the entire feature map + +# Typical CNN architecture: +# - Convolutions(s) with same number of feature maps --> Pooling --> Convolution(s) --> Pooling --> repeat --> Flatten --> Fully connected layer +# - Image gets smaller (less width and height) and deeper (more feature maps) --> Dense --> Dropout --> Repeat +# - Avoid large kernels (window sizes)--> use more layers (except in first layer) +# - Number of repetitions is a hyperparameter to tune +# - Double number of filters after each pooling layer +# - Add keras.layers.Dropout(0.5) to reduce overfitting between dense layers + + +def calculate_final_shape(height, num_conv_2d_layers, stride): + for n in range(num_conv_2d_layers): + height = int(np.ceil(height / stride)) + return height + +# Only works for stride 2... +def calculate_output_paddings(height, num_conv_2d_layers, stride): + output_paddings = [] + for n in range(num_conv_2d_layers): + output_paddings.append(stride - (height % stride) - 1) + height = int(np.ceil(height / stride)) + + output_paddings.reverse() + return output_paddings + +# Works for stride 3 but may need extra work/testing +def _calculate_output_paddings(height, num_conv_layers, stride): + output_paddings = [] + encoder_heights = [] + decoder_heights = [] + output_paddings = [] + encoder_heights.append(height) + + for n in range(num_conv_layers): + height = int(np.ceil(height / stride)) + encoder_heights.append(height) + + encoder_heights.reverse() + + decoder_heights.append(height) + for n in range(num_conv_layers): + height = int(height * stride) + padding = height - encoder_heights[n+1] + print(padding) + #output_paddings.append(padding + stride - int(stride/2) - 2) + height -= padding + decoder_heights.append(height) + if padding == 0: + padding = 2 + elif padding == 2: + padding = 0 + output_paddings.append(padding) + + return output_paddings + +# Only works if latent_dim = 2 +def plot_latent_space(vae, digit_size, channels, n=30, figsize=15, show = False, path = ''): + # display a n*n 2D manifold of digits + #digit_size = 28 + scale = 1.0 + figure = np.zeros((digit_size * n, digit_size * n)) + # linearly spaced coordinates corresponding to the 2D plot + # of digit classes in the latent space + grid_x = np.linspace(-scale, scale, n) + grid_y = np.linspace(-scale, scale, n)[::-1] + + for i, yi in enumerate(grid_y): + for j, xi in enumerate(grid_x): + z_sample = np.array([[xi, yi]]) + x_decoded = vae.decoder.predict(z_sample) + digit = x_decoded[0].reshape(digit_size, digit_size, channels) + figure[ + i * digit_size : (i + 1) * digit_size, + j * digit_size : (j + 1) * digit_size, + ] = digit[:,:,0] + + plt.figure(figsize=(figsize, figsize)) + start_range = digit_size // 2 + end_range = n * digit_size + start_range + pixel_range = np.arange(start_range, end_range, digit_size) + sample_range_x = np.round(grid_x, 1) + sample_range_y = np.round(grid_y, 1) + plt.xticks(pixel_range, sample_range_x) + plt.yticks(pixel_range, sample_range_y) + plt.xlabel("z[0]") + plt.ylabel("z[1]") + plt.imshow(figure, cmap="Greys_r") + if show: + plt.show() + if path: + plt.savefig(path) + + +def plot_images(images, rows, columns, path = '', show = False): + fig = plt.figure(figsize = (10, 7)) + + for i, image in enumerate(images): + fig.add_subplot(rows, columns, i+1) + try: + plt.imshow(image, cmap = 'binary') + except: + plt.imshow(image.squeeze(), cmap = 'binary') + plt.axis('off') + + if show: + plt.show() + if path: + plt.savefig(path) + + + + +# Maybe reconstruction loss is big because it is not normalized? +if __name__ == '__main__': + latent_dim = 3 + train = True + model_dir = './digits' + model_dir = './cifar' + model_dir = './gefs' + n_conv_layers = 4 + stride = 2 + kernel_size = 3 + os.makedirs(model_dir, exist_ok = True) + + #data = load_fashion_mnist() + #data = load_digits_mnist() + #data = load_cifar10() + + # Batch size is too small! + #data = load_gefs_open_data_registry("/home/jovyan/work/pres_sfc_2019010100*") + #data = load_pkl('pres_sfc_2019100100_c00.nc.pkl') + #data = load_many_pkl('pres_msl_201*00_c00.nc.pkl') + + # WORKS FINE, but use selective loading, below, to avoid + # making data copies. + #data, data_min, data_max = load_many_grib('./gefs_data/pres_msl_*.grib2') + #data = load_pkl('gefs.pkl') + + # FIXME: Width and height need to be divisible by number of layers! + # + #print('Input summary:') + #print(data.shape) + + # When using a large data set, + # - mean takes a long time, + # - std takes more than 2x RAM + # - train_test_split makes RAM intensive data copies. + # Instead, selectively load different data sets. + #print('Mean: '+str(np.mean(data))) + # More than doubles RAM usage to find std. + #print('Std: '+str(np.std(data))) + #X_train, X_test = train_test_split(data) + #X_train, X_valid = train_test_split(X_train) + + # Load and then normalize all data based on + # same absolute min/max values. It looks like + # if data is not in np.float32, it will be internally + # converted to np.float32 in vae.fit, so there may be no + # way to cut memory useage there. + X_train, train_min, train_max = load_many_grib( + './gefs_data/pres_msl_201[78]*.grib2', + data_type=np.float32, + min_max_norm=False) + X_test, test_min, test_max = load_many_grib( + './gefs_data/pres_msl_20190[13579]*.grib2', + data_type=np.float32, + min_max_norm=False) + X_valid, valid_min, valid_max = load_many_grib( + './gefs_data/pres_msl_20190[2468]*.grib2', + data_type=np.float32, + min_max_norm=False) + + all_min = np.min([train_min,test_min,valid_min]) + all_max = np.max([train_max,test_max,valid_max]) + all_del = all_max - all_min + + print('Normalize X_train...') + X_train -= all_min + X_train /= all_del + + print('Normalize X_test...') + X_test -= all_min + X_test /= all_del + + print('Normalize X_valid...') + X_valid -= all_min + X_valid /= all_del + + print('Testing shape') + print(np.shape(X_test)) + + print('Training shape') + print(np.shape(X_train)) + + print('Validaiton shape') + print(np.shape(X_valid)) + + batch_size, height, width, channels = X_train.shape + # Plot some inputs + rows = 3 + columns = 4 + sample_input_images = X_train[0:rows*columns] + #plot_images(sample_input_images, rows, columns, path = os.path.join(model_dir, 'sample_input.png')) + + # More conv layers reduces the total parameters at first but then it does not! + encoder = build_encoder(latent_dim, height, width, channels, n_conv_layers, kernel_size, stride, base_filters = 16) + decoder = build_decoder(latent_dim, height, width, channels, n_conv_layers, kernel_size, stride, base_filters = 16) + vae = VAE(encoder, decoder, height*width) + #vae.compile(optimizer=keras.optimizers.Adam()) + vae.compile(optimizer = 'rmsprop') + if train: + early_stopping_cb = keras.callbacks.EarlyStopping(patience = 7, restore_best_weights = True) + history = vae.fit( + X_train, epochs = 5, batch_size = 8, + callbacks = [early_stopping_cb], + validation_data = (X_valid,) + ) + vae.save_weights(os.path.join(model_dir, 'vae')) + hist_pd = pd.DataFrame(history.history) + hist_pd.to_csv(os.path.join(model_dir, 'history.csv'), index = False) + test_loss = vae.evaluate(X_test) + test_loss = dict(zip(["loss", "reconstruction_loss", "kl_loss"], test_loss)) + print('Test loss:') + print(test_loss) + with open(os.path.join(model_dir, 'test_loss.json'), 'w') as json_file: + json.dump(test_loss, json_file, indent = 4) + + else: + vae.load_weights(os.path.join(model_dir, 'vae')) + + if latent_dim == 2: + # Only works if: + if height == width: + #plot_latent_space(vae, height, channels, path = os.path.join(model_dir, 'latent_space.png')) + print('Not plotting anything.') + + # Generating new images + codings = tf.random.normal(shape = [12, latent_dim]) + images = vae.decoder(codings).numpy() + #plot_images(images, 3, 4, path = os.path.join(model_dir, 'generated.png')) + + # Semantic interpolation + codings_grid = tf.reshape(codings, [1, 3, 4, latent_dim]) + larger_grid = tf.image.resize(codings_grid, size = [5, 7]) + interpolated_codings = tf.reshape(larger_grid, [-1, latent_dim]) + images = vae.decoder(interpolated_codings).numpy() + #plot_images(images, 5, 7, path = os.path.join(model_dir, 'interpolated.png')) + + # Encode / decode images + # FIXME: Maybe add a predict method? + # sample_output_images = vae.predict(sample_input_images) + z_mean, z_log_var, z = vae.encoder(sample_input_images) + sample_output_images = vae.decoder(z) + #plot_images(sample_output_images, rows, columns, path = os.path.join(model_dir, 'sample_output.png')) + + diff --git a/run_on_cluster/cvae-weather-ensemble/scripts/get_data.py b/run_on_cluster/cvae-weather-ensemble/scripts/get_data.py index d6c4d18..57d3543 100644 --- a/run_on_cluster/cvae-weather-ensemble/scripts/get_data.py +++ b/run_on_cluster/cvae-weather-ensemble/scripts/get_data.py @@ -6,14 +6,14 @@ def download_file(year, month, day, ensemble, data_predir): os.system(f'wget -q -P {data_predir} https://noaa-gefs-retrospective.s3.amazonaws.com/GEFSv12/reforecast/{year}/{year}{month}{day}00/{ensemble}/Days%3A1-10/pres_msl_{year}{month}{day}00_{ensemble}.grib2') # convert -def convert_file(year, month, day, ensemble, data_predir): - if f'pres_msl_{year}{month}{day}00_{ensemble}.grib2' in os.listdir(data_predir): - os.system(f'cdo -f nc copy ./gefs_data/pres_msl_{year}{month}{day}00_{ensemble}.grib2 ./gefs_data/converted/pres_msl_{year}{month}{day}00_{ensemble}.nc') +def convert_file(year, month, day, ensemble, data_dir): + if f'pres_msl_{year}{month}{day}00_{ensemble}.grib2' in os.listdir('./gefs_data') and f'pres_msl_{year}{month}{day}00_{ensemble}.nc' not in os.listdir(data_dir): + os.system(f'cdo -f nc copy ./gefs_data/pres_msl_{year}{month}{day}00_{ensemble}.grib2 {data_dir}pres_msl_{year}{month}{day}00_{ensemble}.nc') # subset def subset_file(data_file, data_dir): - if '.nc' in data_file and f'{data_dir}subset_{f}' not in os.listdir(data_dir): - os.system(f'ncks -v msl -d time,1,79,2 {data_dir}{f} -O {data_dir}subset_{f}') + if '.nc' in data_file and f'{data_dir}subset_{data_file}' not in os.listdir(data_dir): + os.system(f'ncks -v msl -d time,1,79,2 {data_dir}{data_file} -O {data_dir}subset_{data_file}') # delete (all) def remove_data(data_predir): diff --git a/run_on_cluster/cvae-weather-ensemble/scripts/run_dvcgit.sh b/run_on_cluster/cvae-weather-ensemble/scripts/run_dvcgit.sh index 8485277..9169085 100644 --- a/run_on_cluster/cvae-weather-ensemble/scripts/run_dvcgit.sh +++ b/run_on_cluster/cvae-weather-ensemble/scripts/run_dvcgit.sh @@ -9,17 +9,13 @@ conda activate cvae_env # $1 stores the first entry on the command line that launches # the shell script and so on. Add as many as you need. -history_file_name = $1 -model_file_name = $2 -branch_name = $3 -commit_message = $4 +file_name=$1 +commit_message="$2" -dvc add $history_file_name -git add .gitignore ${history_file_name}.dvc - -dvc add $model_file_name -git add .gitignore ${model_file_name}.dvc +dvc add "$file_name" +git add .gitignore +git add "${file_name}.dvc" dvc push -git commit -m $commit_message -git push origin $branch_name \ No newline at end of file +git commit -m "$commit_message" +git push \ No newline at end of file