Important: This video was created in December 2021. For the most up-to-date code examples, please refer to the Neptune-TensorFlow / Keras integration docs.
What will you get with this integration?
TensorFlow is an open-source deep learning framework commonly used for building neural network models. Keras is an official higher-level API on top of TensorFlow. Neptune helps with keeping track of model training metadata.
With Neptune + TensorFlow / Keras integration you can:
- Log hyperparameters for every run
- See learning curves for losses and metrics during training
- See hardware consumption and stdout/stderr output during training
- Log TensorFlow tensors as images to see model predictions live
- Log training code and git commit information
- Log model weights
neptune.ai is an MLOps stack component for experiment tracking. So we’re constantly working on making it easy to integrate with other parts of the workflow.
It is already integrated with 25+ tools and libraries, and the list is growing. You can check our roadmap to see what’s currently under development.
Other useful resources
See also Keras integration guide.
More about How to Track ML Model Training: TensorFlow/Keras + neptune.ai Integration
See also
What is a Project in Neptune?
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Model Training: Detectron2 + neptune.ai Integration [Example]
See also
Model Training: Prophet + neptune.ai Integration [Example]
See also
Create AzureML Pipeline – Workshop with Aurimas Griciūnas
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