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Neptune is the MLOps stack component for experiment tracking. It offers a single place to log, compare, store, and collaborate on experiments and models.
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 Get an overview
What is Neptune? What can you do with it? How does it work?
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 Try it out
See Neptune in action with our 5-minute "Hello Neptune" example.
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 Set it up
Interested in self-hosting/on-prem?
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 Get on board
Understand how to get your team started with Neptune and explore best practices.
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What's new in the docs
Date Changes Jul 22, 2024 New how-to: Set custom run color Jul 7, 2024 New integration guide: Great Expectations OSS Jun 25, 2024 Revamped docs for the updated web app.
Documented reports and run groups.
Documented offline logging.
Added tip for improving batching when creating multiple series fields in a single statement.
Jun 18, 2024 Added Studio example to PyTorch Lightning integration guide. Clarified logging of system metrics and best practices. Added troubleshooting steps for long load times when using Google Load Balancer with self-hosted Neptune. Mar 25, 2024 Documented the Neptune Query Language, which can be used to filter runs or models queried from a project. New how-to: Set the logging level. Mar 11, 2024 Published two new tutorials: End-to-end model tracking and Using multiple integrations together. Documented InactiveRunException
and how to solve it. Fixed the sample code for logging 1D arrays withextend()
.Feb 27, 2024 New: MosaicML Composer integration guide and API reference Feb 22, 2024 Published full installation instructions for self-hosted Neptune 2.4
.Feb 14, 2024 Added docs for features introduced with neptune 1.9.0
: more powerful API querying options and support for logging Seaborn figures.Feb 13, 2024 New how-to: Define a custom init_run()
function. Added explanation of what data Neptune stores on its servers. Clarified which field types the experiments table supports. Clarified "last edited" timestamps and added sorting example.Looking for product release notes? See the Changelog