Home
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

Neptune in 30 seconds

Step 1: Install neptune-client
Depending on your operating system open a terminal or CMD and run this command:
pip
conda
1
pip install neptune-client
Copied!
1
conda install -c conda-forge neptune-client
Copied!
For more help see installing neptune-client.
Step 2: Connect Neptune to your code
1
import neptune.new as neptune
2
run = neptune.init(project="corp_space/fraud_detection")
Copied!
This is tested with neptune-client==0.9.16.
Step 3: Log metadata
1
run["parameters"] = {"lr": 0.001, "optim": "Adam"} #parameters
2
run["f1_score"] = 0.66 #metrics
3
run["roc_curve"].upload("roc_curve.png") #charts
4
run["model"].upload("model.h5") #models
Copied!
Step 4: See it live in the UI
Model building metadata logged to the Neptune UI

Getting started

Integrations with the ML ecosystem

Experiment Tracking

Model Registry

Need help, questions?

Last modified 5mo ago