Proper Python worker reuse for test_model_cache.

[JoshuaAnickat]

Signed-off-by: Joshua Anickat joanicka@microsoft.com

What changes are proposed in this pull request?

This pull request allows the unit test test_model_cache in tests/pyfunc/test_spark.py to be run successfully in Python 3 by mitigating a PySpark bug.

In PySpark, if the user calls .map(...) with a range or xrange iterator and does not consume the iterator in the mapped function, then the Python worker will read the wrong signal and not be reused. See more details in SPARK-26573 and SPARK-26549. The mitigation is to make the iterator a list.

How is this patch tested?

The test is passing successfully without being gated on sys.version[0] == "3". Consequentially, there are also performance improvements from the Python worker being properly reused instead of repeatedly recreated:

Performance before proposed changes:

Performance after proposed changes:

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.

(Details in 1-2 sentences. You can just refer to another PR with a description if this PR is part of a larger change.)

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/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/projects: MLproject format, project running backends
• area/scoring: Local serving, model deployment tools, spark UDFs
• area/server-infra: MLflow server, JavaScript dev server
• area/tracking: Tracking Service, tracking client APIs, autologging

Interface

• area/uiux: Front-end, user experience, JavaScript, plotting
• 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/breaking-change - The PR will be mentioned in the "Breaking Changes" section
• 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/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

[harupy]

LGTM

[harupy]

@JoshuaAnickat Thanks for the contribution! I pushed a commit and confirmed the test works without list.