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Neptune is an experiment tracker. It enables researchers to monitor their model training, visualize and compare model metadata, and collaborate on AI/ML projects within a team.

•  Get an overview

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  What is Neptune? What can you do with it? How does it work?

  Introduction

•  Try it out

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  See Neptune in action with our 5-minute "Hello Neptune" example.

  Quickstart

•  Set it up

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  Installing Neptune

  Interested in self-hosting/on-prem?

  Self-hosted Neptune

•  Get on board

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  Understand how to get your team started with Neptune and explore best practices.

  Onboarding guide for teams

• What's new in the docs

  Date	Changes
  Feb 10, 2025	Updated TensorFlow integration guide.
  Nov 4, 2024	Published self-hosted Neptune 2.5 upgrade and installation instructions.
  Nov 1, 2024	Included workaround for filtering out Error occurred during asynchronous operation processing errors. Added help page for NeptuneConnectionLostException.
  Oct 23, 2024	Improved Tracking models e2e tutorial.
  Oct 18, 2024	Added self-hosting FAQ answers.
  Sep 24, 2024	Added examples of logging Evidently LLM evaluation reports to Neptune.
  Sep 20, 2024	Clarified how SSO works in Neptune. Added self-hosting FAQ entries. Highlighted usage of a custom monitoring namespace.
  Sep 6, 2024	New how-tos: Set up a run deletion script to automatically free up storage, and log a custom hash when tracking artifact versions. Added examples for managing model stage and querying model metadata when tracking models using runs.
  Aug 23, 2024	New how-tos: Capture external logs from separate scripts, troubleshoot the timestamp must be non-decreasing error, and access production-ready models using runs.
  Jul 22, 2024	New how-to: Set custom run color

  Looking for product release notes? See the Changelog

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