Examples

Examples for using Neptune to log and retrieve your ML metadata.

You can run every example with zero setup as an "ANONYMOUS" Neptune user (no registration needed).

Note: This readme is best viewed in the GitHub Light theme.

Getting Started

	Docs	Neptune	GitHub	Colab
Hello World

How-to guides

Experiment Tracking

	Docs	Neptune	GitHub	Colab
Organize ML experiments
Log binary classification metrics
Log fairness metrics

Model Registry

	Docs	Neptune	GitHub	Colab
Log model building metadata

Monitoring ML Runs Live

	Docs	Neptune	GitHub	Colab
Monitor model training runs live

Data Versioning

	Docs	Neptune	GitHub	Colab
Do basic data versioning
Log image directory snapshot

Neptune API

	Docs	Neptune	GitHub	Colab
Resume run
Pass Run object between files
Use Neptune in distributed computing
Use Neptune in parallel computing
Use Neptune in Pipelines
Log to multiple runs in one script

Neptune UI

	Docs	Neptune	GitHub	Colab
Do Groupby on runs
Do sorting

Integrations and Supported Tools

Languages

	Docs	Neptune	GitHub	Colab
Python
R

Model Training

	Docs	Neptune	GitHub	Colab
Catalyst
fastai
lightGBM
PyTorch
PyTorch Ignite
PyTorch Lightning
Scikit Learn
skorch
TensorFlow / Keras
XGBoost

Hyperparameter Optimization

	Docs	Neptune	GitHub	Colab
Keras Tuner
Optuna
Scikit Optimize

Model Visualization and Debugging

	Docs	Neptune	GitHub	Colab
Altair
Bokeh
Dalex
HiPlot
HTML
Matplotlib
Pandas
Plotly

Experiment Tracking

	Docs	Neptune	GitHub	Colab
MLflow
Sacred
TensorBoard

IDEs and Notebooks

	Docs	Neptune	GitHub	Colab
Any IDE
Amazon SageMaker notebooks
Deepnote
Google Colab
Jupyter Notebook and Jupyter Lab

Data Versioning

	Docs	Neptune	GitHub	Colab
AWS S3
Data Version Control (DVC)
Local filesystem

Continuous Integration and Delivery (CI/CD)

	Docs	Neptune	GitHub	Colab
GitHub Actions