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.
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).
- All charts visible for team members.
- Compare across multiple experiments and gain insight.
- Download charts from UI.
Register and try it out!