Automatically log TensorFlow data to Neptune

  • Log TensorFlow runs to Neptune
  • Gain insights from comparing experiments interactively
  • Take the best of two the two worlds: Use TensorFlow for deep learning experiments and Neptune for experiment management and tracking.
Experiments tracking
Experiments sharing in the team
Experiments comparison
Collaboration
Notebooks tracking and sharing
Notebooks comparison
Team management
Open source integrations
Hardware monitoring for experiments
Interactive experiments dashboard

Manage and analyse TensorFlow runs in Neptune

Use TensorFlow to implement deep learning experiments and Neptune to keep track of them and share.

Neptune integration with TensorFlow is part of the neptune-tensorboard library. To start logging runs, simply use the integration directly in your Python code: neptune_tb.integrate_with_tensorflow(). Track: metrics, images, model weights, parameters and more. Organize your experiments with tags and share with team members.

Support for deep learning libraries

  • Start instantly with out-of-the-box integration.
  • Track rich data (metrics, text, images, files and more).
  • Save model weights.

Backed up experiments history

  • All visualizations are stored and secured.
  • Keep all history – review when needed.
  • Secured Intellectual Property (IP).

Shareable visualizations

  • All charts visible for team members.
  • Compare across multiple experiments and gain insight.
  • Download charts from UI.

Register and try it out!