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examples
๐ Examples of how to use Neptune for different use cases and with various MLOps tools
neptune-aiJupyter Notebookโ
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neptune.ai 2.x examples
Important
This repository contains examples for the Neptune app version 2.x, which uses the Python client version 1.x.
For the new Neptune client examples, go to scale-examples โ
What is neptune.ai?
Neptune is an experiment tracker purpose-built for foundation model training.
With Neptune, you can monitor thousands of per-layer metricsโlosses, gradients, and activationsโat any scale. Visualize them with no lag and no missed spikes. Drill down into logs and debug training issues fast. Keep your model training stable while reducing wasted GPU cycles.
Examples
| Docs | Neptune 2.x app |
GitHub | Colab | |
|---|---|---|---|---|
| Quickstart | ||||
| Track and organize runs | ||||
| Monitor runs live | ||||
| Version datasets in runs | ||||
| Programmatically manage projects | ||||
| Compare datasets between runs | ||||
| Resume run or other object | ||||
| Use Neptune in HPO jobs | ||||
| Use Neptune in pipelines | ||||
| Reproduce Neptune runs | ||||
| Restart runs from checkpoint | ||||
| Use Neptune in distributed computing | ||||
| Track models end-to-end | ||||
| Re-run failed training | ||||
| Log from sequential pipelines | ||||
| DDP training experiments | ||||
| Use multiple integrations together |
Use cases
Neptune 2.x app |
GitHub | Colab | |
|---|---|---|---|
| Text classification using fastText | |||
| Text classification using Keras | |||
| Text summarization | |||
| Time series forecasting |
Integrations
| Docs | Neptune 2.x app |
GitHub | Colab | |
|---|---|---|---|---|
| Airflow | ||||
| Altair | ||||
| Amazon SageMaker (custom Docker containers) | ||||
| Amazon SageMaker (PyTorch Estimator) | ||||
| Azure ML | ||||
| Bokeh | ||||
| Catalyst | ||||
| CatBoost | ||||
| DALEX | ||||
| Detectron2 | ||||
| Docker | ||||
| Evidently | ||||
| fastai | ||||
| Folium (Leaflet) | ||||
| GitHub Actions | ||||
| Google Colab | ||||
| Great Expectations | ||||
| HTML | ||||
| Kedro | ||||
| Keras | ||||
| lightGBM | ||||
| Matplotlib | ||||
| MLflow | ||||
| MosaicML Composer | ||||
| Optuna | ||||
| pandas | ||||
| Plotly | ||||
| Prophet | ||||
| Python | ||||
| PyTorch | ||||
| PyTorch Ignite | ||||
| PyTorch Lightning | ||||
| R | ||||
| Sacred | ||||
| scikit-learn | ||||
| Seaborn | ||||
| skorch | ||||
| TensorBoard | ||||
| TensorFlow | ||||
| ๐ค Transformers | ||||
| XGBoost | ||||
| ZenML |
Utilities
| GitHub | |
|---|---|
| Import runs from Weights & Biases to Neptune | |
| Copy runs from one Neptune project to another | |
| Copy models and model versions from model registry to runs | |
| Back up run metadata from Neptune | |
| Get Neptune storage per project and user | |
| Get runs with most fields logged |
Can't find what you're looking for?
- Visit the Neptune
2.xdocumentation โ - Check out the blog โ
- Reach out to us at support@neptune.ai
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