All global project configurations are stored in config/project.yaml.
Example:
sub_project: "v01"
cv_library: "opencv"
device: "auto"
device_ids: "auto"
logs:
history_train_batches: True
history_train_epochs: True
history_validation: True
notification: True
on_wake: NullSpecify the name of your current session. Results from the current session, such as weights, logs and history will be saved into a sub-directory with this name.
- Data type: str
- Default value: "version01"
Example:
name: "version01"Specify the computer vision library with which to read and manipulate image or video files.
- Data type: str
- Default value: "pil"
- Valid entries:
- "pil"
- "opencv"
Use "pil" to specify the Python Imagin Library (a.k.a. PIL/Pillow) or "opencv" to specify the Open Source Computer Vision Library (OpenCV). (Note that OpenCV is typically ~ 50% faster.)
Example:
cv_library: "opencv"Specify the device to use.
- Data type: str
- Default value: "auto"
- Valid entries:
- "auto"
- "cuda:0"
- "cpu"
Example:
device: "cuda:0"The CPU or a CUDA device can be specified explicitly with "cpu" or "cuda:0" respectively. Or, if "auto" is given, the device will be set to "cuda:0" if available, otherwise "cpu".
If using multiple GPUs, you may specify the specific devices to use. Alternatively, use "auto" to automatically find and use all available CUDA-enabled GPUs.
- Data type: list of ints
- Default value: "auto"
- Valid entries:
Example:
device_ids: [0, 1, 2]Specify whether or not to write each type of log file.
Example:
logs:
history_train_batches: True
history_train_epochs: True
history_validation: True
notification: Truehistory_train_batches- training history for each batch.history_train_epochs- training history for each epoch.history_validation- validation history.notification- log all inputs and outputs of the deeplodocus interface.
Specify any start-up commands.
Example:
on_wake:
- config.summary()
- load()
- train()
- sleep()