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neptune-ai
📝 Examples of how to use Neptune for different use cases and with various MLOps tools
neptune.ai examples
What is neptune.ai?
Neptune is a lightweight experiment tracker for ML teams that struggle with debugging and reproducing experiments, sharing results, and messy model handover. It offers a single place to track, compare, store, and collaborate on experiments and models.
With Neptune, Data Scientists can develop production-ready models faster, and ML Engineers can access model artifacts instantly in order to deploy them to production.
📚Examples
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).
🎓How-to guides
👶 First steps
| Docs | Neptune | GitHub | Colab | |
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| Track and organize runs | |
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| Monitor runs live | |
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🧑 Deeper dive
| Docs | Neptune | GitHub | Colab | |
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| Use the model registry | |
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| Version datasets in runs | |
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| Programmatically manage projects | |
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| Compare datasets between runs | |
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| Resume run or other object | |
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| Use Neptune in HPO jobs | |
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| Use Neptune in pipelines | |
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| Reproduce Neptune runs | |
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| Restart runs from checkpoint | |
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| Use Neptune in distributed computing | |
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| Track models end-to-end | |
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👨 Advanced concepts
| Docs | Neptune | GitHub | Colab | |
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| Re-run failed training | |
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| Log from sequential pipelines | |
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| DDP training experiments | |
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| Use multiple integrations together | |
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👑 Use cases
| Neptune | GitHub | Colab | |
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| Text classification using fastText | |
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| Text classification using Keras | |
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| Text summarization | |
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| Time series forecasting | |
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🧩Integrations and supported tools
| Docs | Neptune | GitHub | Colab | |
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| Airflow | |
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| Altair | |
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| Amazon SageMaker (custom Docker containers) | |
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| Amazon SageMaker (PyTorch Estimator) | |
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| Azure ML | |
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| Bokeh | |
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| Catalyst | |
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| CatBoost | |
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| DALEX | |
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| Detectron2 | |
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| Docker | |
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| GitHub Actions | |
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| Evidently | |
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| fastai | |
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| Folium (Leaflet) | |
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| Google Colab | |
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| HTML | |
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| Kedro | |
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| Keras | |
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| lightGBM | |
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| Matplotlib | |
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| MLflow | |
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| MosaicML Composer | |
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| Optuna | |
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| pandas | |
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| Plotly | |
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| Prophet | |
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| Python | |
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| PyTorch | |
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| PyTorch Ignite | |
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| PyTorch Lightning | |
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| R | |
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| Sacred | |
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| scikit-learn | |
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| Seaborn | |
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| skorch | |
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| TensorBoard | |
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| TensorFlow | |
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| 🤗 Transformers | |
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| XGBoost | |
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| ZenML | |
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🛠️ Other utilities
🧳 Migration tools
| GitHub | |
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| Import runs from Weights & Biases | |
| Copy runs from one Neptune project to another | |
| Back up run metadata from Neptune | |
💼 Management utilities
| GitHub | Colab | |
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| Get Neptune storage per project and user | |
🔍 Cannot find what you are looking for?
Check out our docs → https://docs.neptune.ai/
Metadata
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neptune-ai
Metadata
📝 Examples of how to use Neptune for different use cases and with various MLOps tools