PyTorch Lightning

DVCLive allows you to add experiment tracking capabilities to your PyTorch Lightning projects.

Usage

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

Pass the DvcLiveLogger to your Trainer:

+from dvclive.lightning import DvcLiveLogger

...
 dvclive_logger = DvcLiveLogger()

 trainer = Trainer(
+       logger=dvclive_logger,
    )
 trainer.fit(model)

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

{Live_dir}/scalars/{split}/{iter_type}/{metric}.tsv

Where:

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

Parameters

• run_name - (None by default) - Name of the run, used in PyTorch Lightning to get version.

• prefix - (None by default) - string that adds to each metric name.

• experiment - (None by default) - Live object to be used instead of initializing a new one.

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

Examples

• Using **kwargs to customize Live.

from dvclive.lightning import DvcLiveLogger

dvclive_logger = DvcLiveLogger(
    path='my_logs_path'
)
trainer = Trainer(
    logger=dvclive_logger,
)
trainer.fit(model)