PyTorch Lightning

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

About PyTorch Lightning

PyTorch Lightning is an open-source framework for training PyTorch networks.

Usage

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

You just need to 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 metrics logs and summaries 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',
    summary=False
)
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