Fix bug when registering model version to UC with non-Databricks tracking URI configured #8325
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Signed-off-by: Sid Murching <sid.murching@databricks.com>
smurching
commented
Apr 26, 2023
| @@ -308,7 +308,7 @@ def _get_temporary_model_version_write_credentials(self, name, version) -> Tempo | |||
| ).credentials | |||
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| def _get_workspace_id(self, run_id): | |||
| if run_id is None: | |||
| if run_id is None or not is_databricks_uri(self.tracking_uri): | |||
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This method makes an API request to a Databricks-hosted tracking server and reads the X-Databricks-Org-Id header to determine the tracking server's workspace ID. Previously, we didn't actually check whether the current tracking server is a Databricks-hosted one, leading to errors here when the tracking URI was set to something other than databricks[://profile]. Here we explicitly check
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lobrien
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May 10, 2023
Signed-off-by: Sid Murching <sid.murching@databricks.com> Signed-off-by: Larry O’Brien <larry.obrien@databricks.com>
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What changes are proposed in this pull request?
Fixes a bug when registering a model version to UC with a non-Databricks tracking URI configured.
How is this patch tested?
Does this PR change the documentation?
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/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