OpenMetadata/openmetadata-docs/content/v1.1.0/connectors/ml-model/mlflow/cli.md

title	slug
Run Mlflow Connector using the CLI	/connectors/ml-model/mlflow/cli

Run Mlflow using the metadata CLI

In this section, we provide guides and references to use the Mlflow connector.

Configure and schedule Mlflow metadata and profiler workflows from the OpenMetadata UI:

• Requirements
• Metadata Ingestion

Requirements

{%inlineCallout icon="description" bold="OpenMetadata 0.12 or later" href="/deployment"%} To deploy OpenMetadata, check the Deployment guides. {%/inlineCallout%}

To run the Ingestion via the UI you'll need to use the OpenMetadata Ingestion Container, which comes shipped with custom Airflow plugins to handle the workflow deployment.

Python Requirements

To run the Mlflow ingestion, you will need to install:

pip3 install "openmetadata-ingestion[mlflow]"

Metadata Ingestion

All connectors are defined as JSON Schemas. Here you can find the structure to create a connection to Mlflow.

In order to create and run a Metadata Ingestion workflow, we will follow the steps to create a YAML configuration able to connect to the source, process the Entities if needed, and reach the OpenMetadata server.

The workflow is modeled around the following JSON Schema

1. Define the YAML Config

This is a sample config for Mlflow:

{% codePreview %}

{% codeInfoContainer %}

Source Configuration - Service Connection

{% codeInfo srNumber=1 %}

trackingUri: Mlflow Experiment tracking URI. E.g., http://localhost:5000

{% /codeInfo %}

{% codeInfo srNumber=2 %}

registryUri: Mlflow Model registry backend. E.g., mysql+pymysql://mlflow:password@localhost:3307/experiments

{% /codeInfo %}

Source Configuration - Source Config

{% codeInfo srNumber=3 %}

The sourceConfig is defined here:

markDeletedMlModels: Set the Mark Deleted Ml Models toggle to flag ml models as soft-deleted if they are not present anymore in the source system.

{% /codeInfo %}

Sink Configuration

{% codeInfo srNumber=4 %}

To send the metadata to OpenMetadata, it needs to be specified as type: metadata-rest.

{% /codeInfo %}

{% partial file="workflow-config.md" /%}

{% /codeInfoContainer %}

{% codeBlock fileName="filename.yaml" %}

source:
  type: mlflow
  serviceName: local_mlflow
  serviceConnection:
    config:
      type: Mlflow

      trackingUri: http://localhost:5000

      registryUri: mysql+pymysql://mlflow:password@localhost:3307/experiments

  sourceConfig:
    config:
      type: MlModelMetadata
      # markDeletedMlModels: true

sink:
  type: metadata-rest
  config: {}

{% partial file="workflow-config-yaml.md" /%}

{% /codeBlock %}

{% /codePreview %}

2. Run with the CLI

First, we will need to save the YAML file. Afterward, and with all requirements installed, we can run:

metadata ingest -c <path-to-yaml>

Note that from connector to connector, this recipe will always be the same. By updating the YAML configuration, you will be able to extract metadata from different sources.