neptune.ai examples
Neptune is the most scalable experiment tracker for teams that train foundation models.
Log millions of runs, view and compare them all in seconds. Effortlessly monitor and visualize months-long model training with multiple steps and branches.
Deploy Neptune on your infra from day one, track 100% of your metadata and get to the next big AI breakthrough faster.
In this repo, you'll find examples of using Neptune to log and retrieve your ML metadata.
You can run every example with zero setup (no registration needed).
Docs | Neptune | GitHub | Colab | |
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Quickstart | ||||
Track and organize runs | ||||
Monitor runs live |
Docs | Neptune | GitHub | Colab | |
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Re-run failed training | ||||
Log from sequential pipelines | ||||
DDP training experiments | ||||
Use multiple integrations together |
Neptune | GitHub | Colab | |
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Text classification using fastText | |||
Text classification using Keras | |||
Text summarization | |||
Time series forecasting |
GitHub | |
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Import runs from Weights & Biases | |
Copy runs from one Neptune project to another | |
Copy models and model versions from model registry to runs | |
Back up run metadata from Neptune |
GitHub | Colab | |
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Get Neptune storage per project and user | ||
Get runs with most fields logged |