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neptune.ai examples

Neptune is an experiment tracker purpose-built for foundation model training.

With Neptune, you can monitor thousands of per-layer metrics—losses, gradients, and activations—at 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.

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
Quickstart
Track and organize runs
Monitor runs live

	Docs	Neptune	GitHub	Colab
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

	Docs	Neptune	GitHub	Colab
Re-run failed training
Log from sequential pipelines
DDP training experiments
Use multiple integrations together

	Neptune	GitHub	Colab
Text classification using fastText
Text classification using Keras
Text summarization
Time series forecasting

	Docs	Neptune	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
🤗 Transformers
XGBoost
ZenML

	GitHub
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
Get Neptune storage per project and user
Get runs with most fields logged

Check out ourdocs or ourblog.