Speed up test_pyfunc_model_with_type_hints.py#19296
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harupy merged 2 commits intomlflow:masterfrom Dec 9, 2025
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- Add autouse fixture to inject pip_requirements=[] to skip dependency inference - Replace subprocess-based pyfunc_serve_and_score_model with in-process FastAPI TestClient Signed-off-by: harupy <17039389+harupy@users.noreply.github.com>
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Pull request overview
This PR optimizes the execution speed of test_pyfunc_model_with_type_hints.py by eliminating two expensive operations: dependency inference during model logging and subprocess-based model serving.
Key Changes
- Adds an autouse fixture to automatically inject
pip_requirements=[]to alllog_modelcalls, skipping costly dependency inference - Replaces subprocess-based
pyfunc_serve_and_score_modelwith in-process FastAPITestClientfor faster model serving tests - Removes the
--env-manager localflag as it's no longer needed with in-process testing
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test_pyfunc_model_with_type_hints.py
Signed-off-by: harupy <17039389+harupy@users.noreply.github.com>
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Related Issues/PRs
#xxxWhat changes are proposed in this pull request?
Optimize
tests/pyfunc/test_pyfunc_model_with_type_hints.pyfor faster test execution:Skip dependency inference: Add an autouse fixture that injects
pip_requirements=[]into alllog_modelcalls, avoiding expensive dependency resolution.In-process model serving: Replace subprocess-based
pyfunc_serve_and_score_modelwithscore_model_in_processusing FastAPI'sTestClientfor direct in-process testing.Environment isolation: Snapshot and restore environment variables to prevent
_MLFLOW_IS_IN_SERVING_ENVIRONMENTfrom leaking between tests.Performance Comparison
Result: ~8.4x speedup (322.67s → 38.22s)
How is this PR tested?
Does this PR require documentation update?
Release Notes
Is this a user-facing change?
What component(s), interfaces, languages, and integrations does this PR affect?
Components
area/tracking: Tracking Service, tracking client APIs, autologgingarea/models: MLmodel format, model serialization/deserialization, flavorsarea/model-registry: Model Registry service, APIs, and the fluent client calls for Model Registryarea/scoring: MLflow Model server, model deployment tools, Spark UDFsarea/evaluation: MLflow model evaluation features, evaluation metrics, and evaluation workflowsarea/gateway: MLflow AI Gateway client APIs, server, and third-party integrationsarea/prompts: MLflow prompt engineering features, prompt templates, and prompt managementarea/tracing: MLflow Tracing features, tracing APIs, and LLM tracing functionalityarea/projects: MLproject format, project running backendsarea/uiux: Front-end, user experience, plotting, JavaScript, JavaScript dev serverarea/build: Build and test infrastructure for MLflowarea/docs: MLflow documentation pagesHow 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" sectionrn/breaking-change- The PR will be mentioned in the "Breaking Changes" 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 notesShould this PR be included in the next patch release?
Yesshould be selected for bug fixes, documentation updates, and other small changes.Noshould be selected for new features and larger changes. If you're unsure about the release classification of this PR, leave this unchecked to let the maintainers decide.What is a minor/patch release?
Bug fixes, doc updates and new features usually go into minor releases.
Bug fixes and doc updates usually go into patch releases.