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Integrations#

Neptune integrates with various machine-learning libraries.

Most integrations are implemented as loggers or callbacks that you pass along in your model-training code.

Example

To log metadata when using Keras, add a Neptune callback to model.fit():

import neptune
from neptune.integrations.tensorflow_keras import NeptuneCallback

neptune_run = neptune.init_run()

model.fit(
    x_train,
    y_train,
    ...,
    callbacks=[NeptuneCallback(run=neptune_run)],
)

In the above example, Neptune will automatically log metadata that is typically generated while training models with Keras.

You can also use both the Neptune client library and Neptune integrations in combination. This way, you can benefit both from the integrated logging and customized metadata tracking.

Integrations by category#

Other supported tools#

While some libraries and tools may require more manual setup than others, Neptune generally supports anything you can do in Python.

We provide examples and guides for the following:

For integrations developed by third parties, see Community integrations.

Migration tools#

You can import your data from other experiment trackers with Neptune's migration tools.

My library is not here. What now?#

  1. Use the Neptune client library (neptune ). If you get stuck with setup or usage, we're here to help.
  2. Contact us via mail (contact@neptune.ai) to discuss what you need and how we can deliver it.

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