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.
Docs
Neptune
GitHub
Colab
Hello World
Docs
Neptune
GitHub
Colab
Organize ML experiments
Docs
Neptune
GitHub
Colab
Log model building metadata
Docs
Neptune
GitHub
Colab
Monitor model training runs live
Docs
Neptune
GitHub
Colab
Version datasets in model training runs
Compare datasets between runs
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
Create and delete projects
Docs
Neptune
GitHub
Colab
Do Groupby on runs
Do sorting
Integrations and Supported Tools
Docs
Neptune
GitHub
Colab
Python
R
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
Docs
Neptune
GitHub
Colab
Kedro
Docs
Neptune
GitHub
Colab
MLflow
Sacred
TensorBoard
Docs
Neptune
GitHub
Colab
Any IDE
Amazon SageMaker notebooks
Deepnote
Google Colab
Jupyter Notebook and Jupyter Lab
Docs
Neptune
GitHub
Colab
AWS S3
Data Version Control (DVC)
Local filesystem
Continuous Integration and Delivery (CI/CD)
Docs
Neptune
GitHub
Colab
GitHub Actions