Fix the transformers whisper example and add uri validation #8537
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Signed-off-by: Ben Wilson <benjamin.wilson@databricks.com>
harupy
reviewed
May 26, 2023
mlflow/transformers.py
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| def is_uri(_input): | ||
| try: | ||
| result = urlparse(_input) | ||
| return all([result.scheme, result.netloc]) | ||
| except ValueError: | ||
| return False |
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Suggested change
| def is_uri(_input): | |
| try: | |
| result = urlparse(_input) | |
| return all([result.scheme, result.netloc]) | |
| except ValueError: | |
| return False | |
| def is_uri(s): | |
| try: | |
| result = urlparse(s) | |
| return all([result.scheme, result.netloc]) | |
| except ValueError: | |
| return False |
nit: _input can lead to an unused argument because pylint ignores arguments prefixed with _.
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ah I forgot about that. changing!
harupy
reviewed
May 26, 2023
| pyfunc_model = mlflow.pyfunc.load_model(model_info.model_uri) | ||
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| with pytest.raises(MlflowException, match="An invalid string input was provided. String"): | ||
| pyfunc_model.predict("An invalid path") |
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Can we also test a URI without scheme or netloc?
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Signed-off-by: Ben Wilson <benjamin.wilson@databricks.com>
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Related Issues/PRs
#xxxWhat changes are proposed in this pull request?
Fix the whisper example and add uri validation to string input
How is this patch tested?
Manual execution of example
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