Neptune-MLflow Integration¶

What will you get with this integration?¶

Neptune-MLflow is an open source project curated by Neptune team that enables MLflow experiment runs to be hosted on Neptune.

The integration lets you enjoy the best of both worlds: the tracking and reproducibility of MLflow with the organization and collaboration of Neptune. With Neptune, you can push the following MLrun objects to a Neptune experiment:

experiment id
experiment name
run id
run name
metrics
parameters
artifacts
tags

Note

This integration is tested with neptune-client==0.4.132, neptune-mlflow==0.2.5 and mlflow==1.12.1

Quickstart¶

This quickstart will show you how to:

Install the necessary Neptune packages
Convert MLruns directory of experiment runs into Neptune experiments.

Run in Google Colab

View source on GitHub

See example in Neptune

Before you start¶

Ensure that you have Python 3.x and following libraries installed:
- neptune-mlflow
- mlflow==1.12.1. See the MLflow Installation Guide.
```
pip install --quiet mlflow neptune-mlflow
```
You also need minimal familiarity with Mlflow. Have a look at the MLflow Quickstart guide to get started.

Step 1: Set your `NEPTUNE_API_TOKEN`¶

Linux/iOS:

export NEPTUNE_API_TOKEN='YOUR_API_TOKEN'

Windows:

set NEPTUNE_API_TOKEN="YOUR_API_TOKEN"

Tip

Read more about how to securely set the Neptune API token.

Step 2: Convert your MLruns to Neptune experiments¶

Navigate to the MLflow project in your directory and run:

neptune mlflow --project USER_NAME/PROJECT_NAME

Alternatively, you can point to the MLflow project directory:

neptune mlflow /PATH/TO/MLflow_PROJECT --project USER_NAME/PROJECT_NAME

Tip

You can also set the project qualified name in the NEPTUNE_PROJECT environment variable and skip the --project argument

Linux/macOS:

export NEPTUNE_PROJECT='USER_NAME/PROJECT_NAME'

Windows:

set NEPTUNE_PROJECT="USER_NAME/PROJECT_NAME"

That’s it! You can now browse and collaborate on your MLflow runs in Neptune.

Remember that you can try it out with zero setup: