DVCLive allows you to easily add experiment tracking capabilities to your Fastai projects.
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.
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
to the callbacks list passed to your
+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.
model_file- The name of the file where the model will be saved at the end of each
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')])