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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. Β
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 |