Neptune - PyTorch integration

Experiment tracking for PyTorch-trained models.

What will you get with this integration?

• Log, organize, visualize, and compare ML experiments in a single place
• Monitor model training live
• Version and query production-ready models and associated metadata (e.g., datasets)
• Collaborate with the team and across the organization

What will be logged to Neptune?

• Training metrics
• Model checkpoints
• Model predictions
• Other metadata

Resources

• Documentation
• Code example on GitHub
• Example project in the Neptune app

Example

from neptune_pytorch import NeptuneLogger

run = neptune.init_run()
neptune_logger = NeptuneLogger(
    run,
    model=model,  # your torch Model()
    log_model_diagram=True,
    log_gradients=True,
    log_parameters=True,
    log_freq=30,
)

Support

If you got stuck or simply want to talk to us, here are your options:

• Check our FAQ page.
• You can submit bug reports, feature requests, or contributions directly to the repository.
• Chat! In the Neptune app, click the blue message icon in the bottom-right corner and send a message. A real person will talk to you ASAP (typically very ASAP).
• You can just shoot us an email at support@neptune.ai.