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Neptune is a metadata store for MLOps, built for teams that run a lot of experiments.‌
It gives you a single place to log, store, display, organize, compare, and query all your model-building metadata.
‌Neptune is used for:‌
Experiment tracking: Log, display, organize, and compare ML experiments in a single place.
Model registry: Version, store, manage, and query trained models and model building metadata.
Monitoring ML runs live: Record and monitor model training, evaluation, or production runs live.
Explainer Video
Neptune in 30 seconds
Step 1: Install the Neptune client
Depending on your operating system, open a Terminal or Command Prompt window and enter the following command:
pip
conda
pip install neptune-client
conda install -c conda-forge neptune-client
For more help, see Installation and setup.
Step 2: Connect Neptune to your code
import neptune.new as neptune
​
run = neptune.init(project="corp_space/fraud_detection")
Step 3: Log metadata
run["parameters"] = {"lr": 0.001, "optim": "Adam"} # parameters
run["f1_score"] = 0.66 # metrics
run["roc_curve"].upload("roc_curve.png") # charts
run["model"].upload("model.h5") # models
Step 4: See it live in the app
Model building metadata in the Neptune app
Getting started
​Sign up and explore yourself​
​Run interactive tutorial (no signup needed)​
​Hello World​
​Core concepts​
If you're planning on using Neptune with R instead of Python, head to our dedicated section for R.
Integrations with the ML ecosystem
​PyTorch Lightning​
​PyTorch​
​Tensorflow / Keras​
​Optuna​
​Scikit Learn​
See also the full list of integrations.
Experiment tracking
​Logging metadata​
​Displaying metadata​
​What you can log and display​
​Comparing Runs​
​Organize ML experiments​
​Monitor model training runs live​
Model registry 
​Model registry overview​
Need help, have questions?
​Getting help​

Changelog

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Outline

Neptune in 30 seconds

Getting started

Integrations with the ML ecosystem

Experiment tracking

Model registry

Need help, have questions?