Proper Python worker reuse for test_model_cache. #3863
Merged
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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
intests/pyfunc/test_spark.py
to be run successfully in Python 3 by mitigating a PySpark bug.In PySpark, if the user calls
.map(...)
with arange
orxrange
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 alist
.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?
(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 loggingarea/build
: Build and test infrastructure for MLflowarea/docs
: MLflow documentation pagesarea/examples
: Example codearea/model-registry
: Model Registry service, APIs, and the fluent client calls for Model Registryarea/models
: MLmodel format, model serialization/deserialization, flavorsarea/projects
: MLproject format, project running backendsarea/scoring
: Local serving, model deployment tools, spark UDFsarea/server-infra
: MLflow server, JavaScript dev serverarea/tracking
: Tracking Service, tracking client APIs, autologgingInterface
area/uiux
: Front-end, user experience, JavaScript, plottingarea/docker
: Docker use across MLflow's components, such as MLflow Projects and MLflow Modelsarea/sqlalchemy
: Use of SQLAlchemy in the Tracking Service or Model Registryarea/windows
: Windows supportLanguage
language/r
: R APIs and clientslanguage/java
: Java APIs and clientslanguage/new
: Proposals for new client languagesIntegrations
integrations/azure
: Azure and Azure ML integrationsintegrations/sagemaker
: SageMaker integrationsintegrations/databricks
: Databricks integrationsHow should the PR be classified in the release notes? Choose one:
rn/breaking-change
- The PR will be mentioned in the "Breaking Changes" sectionrn/none
- No description will be included. The PR will be mentioned only by the PR number in the "Small Bugfixes and Documentation Updates" sectionrn/feature
- A new user-facing feature worth mentioning in the release notesrn/bug-fix
- A user-facing bug fix worth mentioning in the release notesrn/documentation
- A user-facing documentation change worth mentioning in the release notes