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* Delete old docs and rename the openmetadata-docs-v1 to openmetadata-docs * Delete old docs and rename the openmetadata-docs-v1 to openmetadata-docs * Delete old docs and rename the openmetadata-docs-v1 to openmetadata-docs
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| 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
{%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 %}
Workflow Configuration
{% codeInfo srNumber=5 %}
The main property here is the openMetadataServerConfig, where you can define the host and security provider of your OpenMetadata installation.
For a simple, local installation using our docker containers, this looks like:
{% /codeInfo %}
{% /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: {}
workflowConfig:
openMetadataServerConfig:
hostPort: "http://localhost:8585/api"
authProvider: openmetadata
securityConfig:
jwtToken: "{bot_jwt_token}"
{% /codeBlock %}
{% /codePreview %}
Workflow Configs for Security Provider
We support different security providers. You can find their definitions here.
Openmetadata JWT Auth
- JWT tokens will allow your clients to authenticate against the OpenMetadata server. To enable JWT Tokens, you will get more details here.
workflowConfig:
openMetadataServerConfig:
hostPort: "http://localhost:8585/api"
authProvider: openmetadata
securityConfig:
jwtToken: "{bot_jwt_token}"
- You can refer to the JWT Troubleshooting section link for any issues in your JWT configuration. If you need information on configuring the ingestion with other security providers in your bots, you can follow this doc link.
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.