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Neptune.ai Tensorboard integration library

Project description

Neptune and TensorFlow logos

Neptune-TensorBoard integration

Log TensorBoard-tracked metadata to neptune.ai.

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?

  • Model summary and predictions
  • Training code and Git information
  • System metrics and hardware consumption

You can also log:

Dashboard with TensorBoard metadata

Resources

Example

Install Neptune and the integration:

pip install -U "neptune[tensorboard]"

Enable Neptune logging:

import neptune
from neptune_tensorboard import enable_tensorboard_logging

neptune_run = neptune.init_run(
    project="workspace-name/project-name",  # replace with your own
    tags = ["tensorboard", "test"],  # optional
    dependencies="infer",  # optional
)

enable_tensorboard_logging(neptune_run)

Export existing TensorBoard logs:

neptune tensorboard --api_token YourNeptuneApiToken --project YourNeptuneProjectName logs

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.

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