DVCLive allows you to easily add experiment tracking capabilities to your Catalyst projects.
Catalyst is a PyTorch framework for Deep Learning Research and Development. It focuses on reproducibility, rapid experimentation, and codebase reuse.
To start using DVCLive you just need to add a few lines to your training code in any Catalyst project.
You just need to add the
to the callbacks list passed to your
+from dvclive.catalyst import DvcLiveCallback . . . runner.train( model=model, criterion=criterion, optimizer=optimizer, loaders=loaders, - num_epochs=2) + num_epochs=2, + callbacks=[DvcLiveCallback()])
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.
model_file- The name of the file where the model will be saved at the end of each
from dvclive.catalyst import DvcLiveCallback runner.train( model=model, criterion=criterion, optimizer=optimizer, loaders=loaders, num_epochs=2, callbacks=[DvcLiveCallback("model.pth")])