Set globally_configured flag correctly for spark and pyspark.ml autologging integration #4929
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WeichenXu123
commented
Oct 22, 2021
| import pyspark as pyspark_module | ||
| import pyspark.ml as pyspark_ml_module | ||
| setup_autologging(pyspark_module) | ||
| setup_autologging(pyspark_ml_module) |
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Inside setup_autologging, it will set globally_configured flag, this flag was used in universe side autologging_integration_called_manually method.
We need set globally_configured flag correctly for spark/pyspark.ml
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harupy
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Oct 22, 2021
harupy
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Oct 22, 2021
WeichenXu123
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Oct 22, 2021
| def test_autolog_globally_configured_flag_set_correctly(): | ||
| from mlflow.utils.autologging_utils import AUTOLOGGING_INTEGRATIONS | ||
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| AUTOLOGGING_INTEGRATIONS.clear() |
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The AUTOLOGGING_INTEGRATIONS.clear() is required, because other tests may enable autologging. but if autolog prev_conf exists, the "globally_configured" flag won't be set, see
mlflow/mlflow/tracking/fluent.py
Line 1444 in 233ab7f
so here clear the AUTOLOGGING_INTEGRATIONS state to be empty
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Signed-off-by: Weichen Xu weichen.xu@databricks.com
What changes are proposed in this pull request?
Set globally_configured flag correctly for spark and pyspark.ml autologging integration
How is this patch tested?
Unit test.
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