Fix the Gateway config validator pre-check for OpenAI to perform instance type validation #10379
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… a provider Signed-off-by: Ben Wilson <benjamin.wilson@databricks.com>
Signed-off-by: Ben Wilson <benjamin.wilson@databricks.com>
Signed-off-by: Ben Wilson <benjamin.wilson@databricks.com>
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harupy
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mlflow/gateway/config.py
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| @@ -116,7 +116,10 @@ def validate_openai_api_key(cls, value): | |||
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| @classmethod | |||
| def _validate_field_compatibility(cls, info: Dict[str, Any]): | |||
| if not isinstance(info, Dict): | |||
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| if not isinstance(info, Dict): | |
| if not isinstance(info, dict): |
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oof. thanks for the catch.
harupy
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Nov 14, 2023
mlflow/gateway/config.py
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| api_type = (info.get("openai_api_type") or OpenAIAPIType.OPENAI).lower() | ||
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Signed-off-by: Ben Wilson <benjamin.wilson@databricks.com>
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Nov 14, 2023
Signed-off-by: Ben Wilson <39283302+BenWilson2@users.noreply.github.com>
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…ance type validation (#10379) Signed-off-by: Ben Wilson <benjamin.wilson@databricks.com> Signed-off-by: Ben Wilson <39283302+BenWilson2@users.noreply.github.com>
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Nov 29, 2023
…ance type validation (mlflow#10379) Signed-off-by: Ben Wilson <benjamin.wilson@databricks.com> Signed-off-by: Ben Wilson <39283302+BenWilson2@users.noreply.github.com> Signed-off-by: swathi <konakanchi.swathi@gmail.com>
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#xxxWhat changes are proposed in this pull request?
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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/gateway: AI Gateway service, Gateway client APIs, third-party Gateway integrationsarea/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