Fix & simplify pytorch / statsmodels logging tests #4724
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dbczumar
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Aug 23, 2021
| @@ -215,29 +215,18 @@ def test_signature_and_examples_are_saved_correctly(sequential_model, data): | |||
| @pytest.mark.large | |||
| @pytest.mark.parametrize("scripted_model", [True, False]) | |||
| def test_log_model(sequential_model, data, sequential_predicted): | |||
| old_uri = tracking.get_tracking_uri() | |||
| # should_start_run tests whether or not calling log_model() automatically starts a run. | |||
| for should_start_run in [False, True]: | |||
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The behavior tested by this case can be captured by simplifying to should_start_run=False.
dbczumar
commented
Aug 23, 2021
| model = statsmodels_model.model | ||
| with TempDir(chdr=True, remove_on_exit=True) as tmp: | ||
| for should_start_run in [False, True]: |
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The behavior tested by this case can be captured by simplifying to should_start_run=False.
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Signed-off-by: dbczumar <corey.zumar@databricks.com>
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What changes are proposed in this pull request?
When #4719 was merged, it broke a couple of test cases that needlessly modify the tracking URI used by MLflow and tear down a temporary tracking directory mid-way through the test. This PR refactors the test cases to reduce complexity while still testing the necessary functionality.
How is this patch tested?
Integration tests
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: MLflow Model server, model deployment tools, Spark UDFsarea/server-infra: MLflow Tracking server backendarea/tracking: Tracking Service, tracking client APIs, autologgingInterface
area/uiux: Front-end, user experience, plotting, JavaScript, JavaScript dev serverarea/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