Add documentation for the expanded code path inference feature #11997
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Signed-off-by: Ben Wilson <[email protected]>
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| saving or logging a model. This new feature utilizes import dependency analysis to automatically infer the code dependencies required by the model by checking which | ||
| modules are imported within the references of a Python Model's definition. | ||
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| In order to use this new feature, you can simply set the argument ``infer_code_paths`` (Default ``False``) to ``True`` when logging. You do not have to define |
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Note: currently the PR only supports MLflow python model (by mlflow.pyfunc.log_model / save_model), I will file follow-up PR for supporting all other flavors
docs/source/model/dependencies.rst
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| Only modules that are within the current working directory as accessible. Dependency inference will not work across module boundaries or if your | ||
| custom code is defined in an entirely different library. If your code base is structured in such a way that common modules are entirely external to | ||
| the path that your model logging code is executing in, the original ``code_paths`` option is required in order to capture these dependencies, as | ||
| dependency inference will not capture those requirements. |
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Shall we mention the following points ?
Please ensure that the custom python module code does not contain sensitive data such as credential token strings, otherwise they might be included in the automatic inferred code path files and be logged to MLflow artifact repository. If a python code file is loaded as the python ``__main__`` module, then this code file can't be inferred as the code path file. If your model depends on classes / functions defined in ``__main__`` module, you should use `cloudpickle` to dump your model instance in order to pickle classes / functions in ``__main__``.
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Added a clear warning for sensitive data and a new section that explains what is up with main module references
Signed-off-by: Ben Wilson <[email protected]>
harupy
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May 16, 2024
docs/source/model/dependencies.rst
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| - If you must define functions and classes in the ``__main__`` module, use ``cloudpickle`` to serialize your model to ensure that all dependencies are correctly handled. | ||
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| Saving Extra Code with an MLflow Model - Legacy Manual Declaration |
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Legacy sounds like it's not recommended. I think this is still a valid approach when:
- The number of custom modules is small (e.g. 1).
- you don't want to slow down your workflow. Code path inference is slow.
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Good point. I'll just leave it as "Manual Declaration"
BenWilson2
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May 16, 2024
Signed-off-by: Ben Wilson <[email protected]>
BenWilson2
commented
May 16, 2024
Signed-off-by: Ben Wilson <[email protected]>
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Related Issues/PRs
#xxxWhat changes are proposed in this pull request?
Adds documentation regarding the expanded code paths dependency inference feature
How is this PR tested?
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 notesShould this PR be included in the next patch release?
Yesshould be selected for bug fixes, documentation updates, and other small changes.Noshould be selected for new features and larger changes. If you're unsure about the release classification of this PR, leave this unchecked to let the maintainers decide.What is a minor/patch release?
Bug fixes, doc updates and new features usually go into minor releases.
Bug fixes and doc updates usually go into patch releases.