Log TensorBoard-tracked metadata to neptune.ai.

• 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

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

You can also log:

• Existing TensorBoard logs
• Other metadata

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

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

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.