Add weekly cron job for running slow docker tests

[B-Step62]

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pip install git+https://github.com/mlflow/mlflow.git@refs/pull/11004/merge

Checkout with GitHub CLI

gh pr checkout 11004

Related Issues/PRs

Follow-up for #10954

What changes are proposed in this pull request?

Scheduling a weekly cron job for testing docker image building and querying with flavors.

Note Using environment variable MLFLOW_RUN_SLOW_TESTS to control whether or not to run slow tests, instead of pytest CLI option. The latter is a bit tricky when we want to use the flag outside the test functions scope. For example, in test_docker_flavors, we have many import statements for accessing fixtures defined in other files. We don't want to import them when the tests are skipped, but we can't lazily import it inside test functions, because pytest fixture visibility is determined before running tests. Hence we have to check whether or not to run slow tests in global scope, which is trickly to do with pytest CLI option.

How is this PR tested?

• [x] Existing unit/integration tests
• [ ] New unit/integration tests
• [ ] Manual tests

Question: I tested the functionality of skipping slow tests via env var, but don't know how to test the new workflow. Is there any good way to test Github action before merging the PR?

Does this PR require documentation update?

• [x] 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?

• [x] 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
• [x] 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:

• [x] 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

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