Important: This video was created in October 2021. For the most up-to-date code examples, please refer to the Neptune-Docker integration docs.
You can use Neptune in any Python environment to log and retrieve ML metadata, including containerized Python scripts or applications.
In this guide, you will learn how to use the Neptune-client library inside Docker to log experimentation metadata.
neptune.ai is an MLOps stack component for experiment tracking. So we’re constantly working on making it easy to integrate with other parts of the workflow.
It is already integrated with 25+ tools and libraries, and the list is growing. You can check our roadmap to see what’s currently under development.
Other useful resources
Check the docs to see how to use Neptune with Docker.
More about How to Log Your Experimentation Metadata: Docker + neptune.ai Integration
See also
Train, Track, and Deploy Your Models: Neptune + Modelbit Integration
See also
.upload(“product_updates_september_2023”)
See also
What is a Project in Neptune?
See also
Model Training: Detectron2 + neptune.ai Integration [Example]
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