Improve error message when code_path includes Databricks notebook #10859
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area/models
mlflow/utils/model_utils.py
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| message=( | ||
| f"Failed to copy the specified code path '{code_path}' into the model " | ||
| "artifacts. It appears that your code path includes file(s) that cannot " | ||
| "be copied, such as Databricks Notebooks. Please specify a code path " |
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This is a databricks-specific error,
what about checking is_in_databricks() firstly, if True then we include phrase of "such as Databricks Notebooks" ? Otherwise it might confuse OSS users.
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Nice idea! Will add a flag and switch error message accordingly.
Signed-off-by: B-Step62 <yuki.watanabe@databricks.com>
WeichenXu123
approved these changes
Jan 22, 2024
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What changes are proposed in this pull request?
When an user saves a model with
code_pathsthat includes Databricks notebooks, it will raise an OS error during file copy withshutil.copyfile. This is because DB notebooks cannot be read as normal files e.g.open()(general.ipynbfile works). Currently this error is surfaced like[Errno 95] Operation not supported: 'my_module/Notebook'", which doesn't really help. This PR adds more user-friendly error messages indicating the path includes non-copyable file(s).How is this PR tested?
On Databricks notebook:
Does this PR require documentation update?
Release Notes
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