pip install git+https://github.com/mlflow/mlflow.git@refs/pull/10927/merge

gh pr checkout 10927

Related Issues/PRs

What changes are proposed in this pull request?

Fixes this error https://github.com/mlflow/mlflow/actions/runs/7691119289/job/20955938381/

It looks like _tracking_store_registry is somehow copied (by pytest?), the original _tracking_store_registry doesn't know about pickle and complains "I don't know pickle":

Code:

        _tracking_store_registry.register("pickle", lambda *args, **kwargs: mock_tracking_store)
        _get_model_registry_store_registry().register(
            "pickle", lambda *args, **kwargs: mock_model_registry_store
        )
    
        # Create an MlflowClient with the store that cannot be pickled, perform
        # tracking & model registry operations, and verify that the client can still be pickled
>       client = MlflowClient("pickle://foo")

Error:

E           mlflow.tracking.registry.UnsupportedModelRegistryStoreURIException:  Model registry functionality is unavailable; got unsupported URI 'pickle://foo' for model registry data storage. Supported URI schemes are: ['', 'file', 'databricks', 'databricks-uc', 'http', 'https', 'postgresql', 'mysql', 'sqlite', 'mssql', 'file-plugin']. See https://www.mlflow.org/docs/latest/tracking.html#storage for how to run an MLflow server against one of the supported backend storage locations.

How is this PR tested?

• Existing unit/integration tests
• New unit/integration tests
• Manual tests

Does this PR require documentation update?

• No. You can skip the rest of this section.
• Yes. I've updated:
  • Examples
  • API references
  • Instructions

Release Notes

Is this a user-facing change?

• No. You can skip the rest of this section.
• Yes. Give a description of this change to be included in the release notes for MLflow users.

What component(s), interfaces, languages, and integrations does this PR affect?

Components

• area/artifacts: Artifact stores and artifact logging
• area/build: Build and test infrastructure for MLflow
• area/deployments: MLflow Deployments client APIs, server, and third-party Deployments integrations
• area/docs: MLflow documentation pages
• area/examples: Example code
• area/model-registry: Model Registry service, APIs, and the fluent client calls for Model Registry
• area/models: MLmodel format, model serialization/deserialization, flavors
• area/recipes: Recipes, Recipe APIs, Recipe configs, Recipe Templates
• area/projects: MLproject format, project running backends
• area/scoring: MLflow Model server, model deployment tools, Spark UDFs
• area/server-infra: MLflow Tracking server backend
• area/tracking: Tracking Service, tracking client APIs, autologging

Interface

• area/uiux: Front-end, user experience, plotting, JavaScript, JavaScript dev server
• area/docker: Docker use across MLflow's components, such as MLflow Projects and MLflow Models
• area/sqlalchemy: Use of SQLAlchemy in the Tracking Service or Model Registry
• area/windows: Windows support

Language

• language/r: R APIs and clients
• language/java: Java APIs and clients
• language/new: Proposals for new client languages

Integrations

• integrations/azure: Azure and Azure ML integrations
• integrations/sagemaker: SageMaker integrations
• integrations/databricks: Databricks integrations

How should the PR be classified in the release notes? Choose one:

• rn/none - No description will be included. The PR will be mentioned only by the PR number in the "Small Bugfixes and Documentation Updates" section
• rn/breaking-change - The PR will be mentioned in the "Breaking Changes" section
• rn/feature - A new user-facing feature worth mentioning in the release notes
• rn/bug-fix - A user-facing bug fix worth mentioning in the release notes
• rn/documentation - A user-facing documentation change worth mentioning in the release notes