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:

Existing TensorBoard logs
Other metadata

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.