Stabilize dataset logging APIs #11229
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Signed-off-by: dbczumar <corey.zumar@databricks.com>
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Didn't realize that protobuf looked at comments for version validation. |
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Signed-off-by: dbczumar <corey.zumar@databricks.com> Signed-off-by: mlflow-automation <mlflow-automation@users.noreply.github.com> Co-authored-by: mlflow-automation <mlflow-automation@users.noreply.github.com>
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Signed-off-by: dbczumar <corey.zumar@databricks.com> Signed-off-by: mlflow-automation <mlflow-automation@users.noreply.github.com> Co-authored-by: mlflow-automation <mlflow-automation@users.noreply.github.com> Signed-off-by: Arthur Jenoudet <arthur.jenoudet@databricks.com>
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What changes are proposed in this pull request?
Stabilize dataset logging APIs
How is this PR tested?
Does this PR require documentation update?
Release Notes
Remove experimental designation from MLflow Datasets API
Is this a user-facing 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/deployments: MLflow Deployments client APIs, server, and third-party Deployments integrationsarea/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/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 notes