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

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.

Dependency	Optional
importlib-metadata	false
torch	false
neptune	true
pre-commit	true
pydot	true
pytest	true
pytest-cov	true
torchviz	true

Package profile

neptune-pytorch

Releases

Release table

PyPI Downloads

Dependencies

Details

Project URLs

Dependencies

Package description