Automatically track Catalyst model training progress to Neptune
- Log parameters, metrics, losses, hardware utilization and monitor it live
- Save training checkpoints automatically to the app where they are backed-up and easily accessible
- Analyze and compare the results across multiple experiments and share them with others
Catalyst metrics, losses, checkpoints, and hardware utilization logged to Neptune automatically.
Experiment tracking tool with open source integration with Catalyst, to automatically log metrics model checkpoints and more.
NeptuneLogger and pass it to your
Trainer callbacks to log metrics, model checkpoints, and more.
Metrics will be automatically plotted and model checkpoints will be downloadable from the Neptune app.
neptune_logger = NeptuneLogger( api_token="ANONYMOUS", project_name='shared/pytorch-ignite-integration',...) runner = SupervisedRunner() runner.train( ... callbacks=[neptune_logger] )
Support for deep learning libraries
- Start instantly with out-of-the-box integration.
- Track rich data (metrics, text, images, files and more).
- Save model weights.
- All charts are visible to all team members.
- Compare across multiple experiments and gain insight.
- Download charts from UI.
Backed up experiments history
- All visualizations are stored and secured.
- Keep all history – review when needed.
- Secured Intellectual Property (IP).
Register and try it out!