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Version: 0.12.1

MLflow

Testing

Important Capabilities

CapabilityStatusNotes
DescriptionsExtract descriptions for MLflow Registered Models and Model Versions
Extract TagsExtract tags for MLflow Registered Model Stages

Concept Mapping

This ingestion source maps the following MLflow Concepts to DataHub Concepts:

Source ConceptDataHub ConceptNotes
Registered ModelMlModelGroupThe name of a Model Group is the same as a Registered Model's name (e.g. my_mlflow_model)
Model VersionMlModelThe name of a Model is {registered_model_name}{model_name_separator}{model_version} (e.g. my_mlflow_model_1 for Registered Model named my_mlflow_model and Version 1, my_mlflow_model_2, etc.)
Model StageTagThe mapping between Model Stages and generated Tags is the following:
- Production: mlflow_production
- Staging: mlflow_staging
- Archived: mlflow_archived
- None: mlflow_none

CLI based Ingestion

Install the Plugin

pip install 'acryl-datahub[mlflow]'

Starter Recipe

Check out the following recipe to get started with ingestion! See below for full configuration options.

For general pointers on writing and running a recipe, see our main recipe guide.

source:
type: mlflow
config:
# Coordinates
tracking_uri: tracking_uri

sink:
# sink configs

Config Details

Note that a . is used to denote nested fields in the YAML recipe.

FieldDescription
model_name_separator
string
A string which separates model name from its version (e.g. model_1 or model-1)
Default: _
registry_uri
string
Registry server URI. If not set, an MLflow default registry_uri is used (value of tracking_uri or MLFLOW_REGISTRY_URI environment variable)
tracking_uri
string
Tracking server URI. If not set, an MLflow default tracking_uri is used (local mlruns/ directory or MLFLOW_TRACKING_URI environment variable)
env
string
The environment that all assets produced by this connector belong to
Default: PROD

Code Coordinates

  • Class Name: datahub.ingestion.source.mlflow.MLflowSource
  • Browse on GitHub

Questions

If you've got any questions on configuring ingestion for MLflow, feel free to ping us on our Slack.