You should know
How-to guides
Integrations and Supported Tools
API reference
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!
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
Export as PDF
Copy link