Home

Welcome to the neptune.ai docs!

Neptune is a metadata store that offers experiment tracking and model registry for machine learning researchers and engineers. With Neptune, you can log, query, manage, display, and compare all your model metadata in a single place.

•  Try it out

  ---

  See how it works with our 5-minute "Hello Neptune" example.

  No registration required.

  Quickstart

•  Overview

  ---

  What is Neptune? What can you do with it? The introduction should answer most of your questions.

  About Neptune

•  Set it up

  ---

  Head straight to installation and setup.

  Installing Neptune

  Interested in Neptune on-premises?

  Deploying Neptune on your server

•  Take the tutorial

  ---

  Start from the beginning, set up a sample project, and learn the central Neptune functions.

   Neptune tutorial

   Neptune R tutorial

• What's new in the docs

  Date	Changes
  Feb 3, 2023	Added two explainer pages: using handlers for logging and using the wait() and sync() operations to ensure synchronous logging. Also clarified how to log from different ranks in multi-node jobs.
  Jan 30, 2023	Released API references that were missing for integration libraries. Published guide on using the model registry with Amazon SageMaker. Added a new page for logging charts and plots and improved discoverability of related topics.
  Jan 23, 2023	Added introductory guide to the Neptune-AWS integration. Added examples for logging after fitting or testing to the Lightning and fastai guides.
  Jan 19, 2023	Added utils module to the API reference. Documented the new get_root_object() method, which enhances the support for namespace handlers. Changed instances of the name argument to project or workspace wherever names of existing projects or workspaces are passed (see neptune-client 0.16.16).
  Jan 17, 2023	Roughly one month ahead of the 1.0.0 release of the Neptune Python client library, we're publishing the 1.0 upgrade guide to help you understand the changes and what you'll need to update in your code.
  Jan 12, 2023	We're starting to roll out better Amazon SageMaker support: two new examples are now available.
  Jan 11, 2023	New tutorial: Working with TensorFlow. Clarified how to analyze the results in the Optuna guide. Added brief guide on how you can log datasets. Clarified how you can log 1D arrays.
  Jan 9, 2023	Replaced all examples of log() with append(). Added a new page that collects all your options for setting your Neptune credentials in your environment. Clarified the sys/state values. Adjusted the workaround for a Plotly/Matplotlib incompatibility issue.

  Looking for product release notes? See the Changelog

• Navigating the docs

  Tab	Contents
  Home	Introduction, changelogs, installation, and setup. You'll also find information about on-premises deployment and the legacy API here.
  Using Neptune	Once you're all set up, in this section you'll find explanations and tips for getting the most out of Neptune. Look here for what kinds of metadata you can log and display.
  Integrations	Detailed guides on using Neptune together with supported libraries, tools, and environments. Neptune integrates directly with several popular ML frameworks.
  Tutorials	Practical walkthroughs and MLOps use cases.
  API	API reference material and everything you need to know about our client library. Includes Neptune CLI usage instructions.
  Help	If you're facing problems or have questions, we're here to help.

---

Change cookie settings →