Neptune comes with 25+ integrations with Python libraries popular in machine learning, deep learning and reinforcement learning.

How do integrations work?

Integrations are written using neptune-client and provide a convenient way to jump-start working with Neptune and a library that you are using. There is no need to integrate your code manually using neptune-client (it’s easy though).

For each integration, the installation, scope, and usage examples are explained in detail in the documentation (i.e.: TensorFlow-Keras integration).

List of all integrations

• ​Altair​

• ​Bokeh​

• ​Catalyst​

• ​Dalex​

• ​Fastai​

• ​HTML​

• ​Kedro​

• ​Keras Tuner​

• ​Lightgbm​

• ​Matplotlib​

• ​MLflow​

• ​Optuna​

• ​Pandas​

• ​Plotly​

• ​Pytorch​

• ​Pytorch Ignite​

• ​Pytorch Lightning​

• ​Sacred​

• ​Scikit-learn​

• ​Scikit Optimize​

• ​Skorch​

• ​Tensorboard​

• ​TensorFlow / Keras​

• ​XGBoost​

My library is not here. What now?

There are two common paths:

1. You can always use neptune-client, our open-source Python library for logging all kinds of data and metadata to runs.

2. Contact us directly via mail or chat (that little thing in the lower right corner) to discuss what you need and how we can deliver it.