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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)

This will generate the outputs as described in the Get Started.

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

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)

By default, PyTorch Lightning creates a directory to store checkpoints using the logger's name (DvcLiveLogger). You can change the checkpoint path or disable checkpointing at all as described in the PyTorch Lightning documentation

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