Fast.ai

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

About Fast.ai

Fast.ai is a deep learning library which provides practitioners with high-level components that can quickly and easily provide state-of-the-art results in standard deep learning domains, and provides researchers with low-level components that can be mixed and matched to build new approaches.

Usage

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

You just need to add the DvcLiveCallback to the callbacks list passed to your Learner:

+from dvclive.fastai import DvcLiveCallback

. . .

learn = tabular_learner(data_loader, metrics=accuracy)
learn.fit_one_cycle(
-  n_epoch=2)
+  n_epoch=2,
+  cbs=[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.

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.fastai import DvcLiveCallback

learn = tabular_learner(data_loader, metrics=accuracy)
learn.fit_one_cycle(
  n_epoch=2,
  cbs=[DvcLiveCallback(model_file='model.pth')])

• Using **kwargs to customize Live.

from dvclive.fastai import DvcLiveCallback

learn = tabular_learner(data_loader, metrics=accuracy)
learn.fit_one_cycle(
  n_epoch=2,
  cbs=[DvcLiveCallback(path='custom_path', summary=False)])