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.
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: