-
Notifications
You must be signed in to change notification settings - Fork 4k
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We鈥檒l occasionally send you account related emails.
Already on GitHub? Sign in to your account
Add documentation for the expanded code path inference feature #11997
Conversation
Signed-off-by: Ben Wilson <benjamin.wilson@databricks.com>
Documentation preview for edaa24c will be available when this CircleCI job More info
|
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. | ||
|
||
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 |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
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
Outdated
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. |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
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__``.
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Added a clear warning for sensitive data and a new section that explains what is up with main module references
docs/source/model/dependencies.rst
Outdated
- 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. | ||
|
||
|
||
Saving Extra Code with an MLflow Model - Legacy Manual Declaration |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
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.
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Good point. I'll just leave it as "Manual Declaration"
Signed-off-by: Ben Wilson <39283302+BenWilson2@users.noreply.github.com>
Signed-off-by: Ben Wilson <39283302+BenWilson2@users.noreply.github.com>
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
LGTM!
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
LGTM!
馃洜 DevTools 馃洜
Install mlflow from this PR
Checkout with GitHub CLI
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?
Yes
should be selected for bug fixes, documentation updates, and other small changes.No
should 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.