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Introduction to

Neptune consists of:

  • A Python API (neptune + integrations) for logging and querying model-building metadata.
  • A web app ( ) for visualization, comparison, monitoring, and collaboration.

Neptune core concepts

You can have a workspace for each team or organization that you're working with. Within a workspace, you can create a project for each ML task you're solving.

Your project can contain metadata organized per run, model, or task.

Examples of ML metadata Neptune can track

Experiment and model training metadata:

  • Metrics, hyperparameters, learning curves
  • Training code and configuration files
  • Predictions (images, tables)
  • Diagnostic charts (Confusion matrices, ROC curves)
  • Console and hardware logs

Artifact metadata:

  • Paths to the dataset or model (Amazon S3 bucket, filesystem)
  • Dataset hash
  • Dataset or prediction preview (head of the table, snapshot of the image folder)
  • Feature column names (for tabular data)
  • When and by whom an artifact was created or modified
  • Size and description

Trained model metadata:

  • Model binaries or location of your model assets
  • Dataset versions
  • Links to recorded model-training runs and experiments
  • Who trained the model
  • Model descriptions and notes
  • Links to observability dashboards (like Grafana)

For a complete reference of what you can track, see What you can log and display.

How does it work?#

import neptune
from sklearn.datasets import load_wine

run = neptune.init_run()

data = load_wine()
X_train, X_test, y_train, y_test = train_test_split(...)

PARAMS = {"n_estimators": 10, "max_depth": 3, ...}
run["parameters"] = PARAMS


test_f1 = f1_score(y_test, y_test_pred.argmax(axis=1), average="macro")
run["test/f1"] = test_f1

All metadata preview

Metrics of several runs visualized as charts in Neptune

Custom dashboard with multiple metadata types

Explore an example in Neptune 

Integrations with the ML ecosystem

Skip the manual logging with our integrations.

You can usually create a Neptune logger or callback that you pass along in your code:

Example: Keras integration
import neptune
from neptune.integrations.tensorflow_keras import NeptuneCallback

neptune_run = neptune.init_run()

# Create a Neptune callback and pass it to

The above code will take care of logging metadata typically generated during Keras training runs.

For more, see Integrations.

What do I need in order to use Neptune?#

To use the SaaS (online) version of Neptune, you need an internet connection from your system.

To set up logging and perform queries through the client library (API):

  • You or your team should have a working knowledge of Python.
  • You do not need extensive command-line experience, but you should know:

Neptune self-hosted

You can also install Neptune on your own infrastructure. For details, see Self-hosted Neptune.

You can do the following without coding or technical expertise: