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Catalyst

DVCLive allows you to add experiment tracking capabilities to your Catalyst projects.

Usage

To start using DVCLive, add a few lines to your training code in any Catalyst project.

Include the DvcLiveCallback int the callbacks list passed to your Runner:

+from dvclive.catalyst import DvcLiveCallback

...

runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    loaders=loaders,
-    num_epochs=2)
+    num_epochs=2,
+    callbacks=[DvcLiveCallback()])

The history of each {metric} will be stored in:

{Live.dir}/scalars/{split}/{metric}.tsv

Where:

  • {Live.dir} is the dir attribute of Live.
  • {split} can be either train or valid.
  • {metric} is the name provided by the framework.

Without requiring additional modifications to your training code, you can use DVCLive alongside DVC. See DVCLive with DVC for more info.

Parameters

  • model_file - (None by default) - The name of the file where the model will be saved at the end of each step.

  • **kwargs - Any additional arguments will be passed to Live.

Examples

  • Using model_file.
from dvclive.catalyst import DvcLiveCallback

runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    loaders=loaders,
    num_epochs=2,
    callbacks=[DvcLiveCallback(model_file="model.pth")])
  • Using **kwargs to customize Live.
from dvclive.catalyst import DvcLiveCallback

runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    loaders=loaders,
    num_epochs=2,
    callbacks=[
      DvcLiveCallback(model_file="model.pth", path="custom_path")])
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