[Support inference params-2]Add params for PyFunc predict#8973
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Signed-off-by: Serena Ruan <[email protected]>
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Signed-off-by: Serena Ruan <[email protected]>
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Signed-off-by: Serena Ruan <[email protected]>
Signed-off-by: Serena Ruan <[email protected]>
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Seems like converting a draft PR to a normal PR doesn't trigger the workflows automatically. cc @harupy |
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Signed-off-by: Serena Ruan <[email protected]>
Signed-off-by: Serena Ruan <[email protected]>
Signed-off-by: Serena Ruan <[email protected]>
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LGTM! Thanks @serena-ruan :)
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…lflow#8973)" This reverts commit 9ebab71. Signed-off-by: Serena Ruan <[email protected]>
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* add predict params for pyfunc and python model Signed-off-by: Serena Ruan <[email protected]> * fix Signed-off-by: Serena Ruan <[email protected]> * add more tests and fix a small bug Signed-off-by: Serena Ruan <[email protected]> * address comments Signed-off-by: Serena Ruan <[email protected]> * add warnings for params missing case Signed-off-by: Serena Ruan <[email protected]> * reformat Signed-off-by: Serena Ruan <[email protected]> --------- Signed-off-by: Serena Ruan <[email protected]>
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Related Issues/PRs
Depends on: #8972
What changes are proposed in this pull request?
Support
paramsfield for PyFunc models and PythonModel.How is this patch tested?
Does this PR change the documentation?
Release Notes
Is this a user-facing change?
Support passing additional parameters as
paramsdictionary to modelpredictmethodWhat 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/recipes: Recipes, Recipe APIs, Recipe configs, Recipe Templatesarea/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