Fix JSON schema validation issue for run_uuid
#5778
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Signed-off-by: harupy <hkawamura0130@gmail.com>
harupy
changed the title
run_uuidrun_uuid
Signed-off-by: harupy <hkawamura0130@gmail.com>
harupy
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Apr 26, 2022
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| value = request_json.get(schema_key) | ||
| if schema_key == "run_id" and value is None and "run_uuid" in request_json: | ||
| value = request_json.get("run_uuid") |
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I think we should replace run_uuid in the MLflow UI code with run_id.
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Signed-off-by: harupy hkawamura0130@gmail.com
What changes are proposed in this pull request?
Fix a bug added in #5458.
The current schema validation implementation fails when a request uses
run_uuidinstead ofrun_id. In the attached video, I tried to rename a run on MLflow UI but it failed because the request sent usesrun_uuid.schema-validation-issue.mov
How is this patch tested?
The added test
Does this PR change the documentation?
ci/circleci: build_doccheck. If it's successful, proceed to thenext step, otherwise fix it.
Detailson the right to open the job page of CircleCI.Artifactstab.docs/build/html/index.html.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