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’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Autologging functionality for scikit-learn integration with XGBoost (Part 1) #4954
Conversation
Signed-off-by: Junwen Yao <jwyiao@gmail.com>
Hi @dbczumar, as per our discussion, I separate the autologging functionality and model saving / loading with model class specification into two PRs. This is the first one to address model saving / loading. Please let me know if I missed anything. Thanks! |
Signed-off-by: Junwen Yao <jwyiao@gmail.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.
@jwyyy Looks awesome! I left a few tiny comments - I think this is very close!
Signed-off-by: Junwen Yao <jwyiao@gmail.com>
Signed-off-by: Junwen Yao <jwyiao@gmail.com>
Hi @harupy @dbczumar, thank you for initializing the auto check! According to the error messages, two unsuccessful checks were due to network connection exception, not related to the changes in this PR. I am not sure if I can fix these two problems efficiently. Can you take a look and re-try the check? Thanks! Also please let me know if the latest commit look good. |
Signed-off-by: Junwen Yao <jwyiao@gmail.com>
Signed-off-by: Junwen Yao <jwyiao@gmail.com>
Hi @harupy @dbczumar, I notice that this PR #4997 tries to add a new argument to (The previous failed check (35/36) was due to Keras 2.7 compatibility, which should be fixed now.) |
Yes it does, but the new argument doesn't change how to load the model, it just allows users to determine where to load the model. Therefore, it should not conflict with this PR. |
Signed-off-by: Junwen Yao <jwyiao@gmail.com>
Signed-off-by: Junwen Yao <jwyiao@gmail.com>
Signed-off-by: Junwen Yao <jwyiao@gmail.com>
Signed-off-by: Junwen Yao <jwyiao@gmail.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.
Looks good to me once #4954 (comment) is addressed!
Signed-off-by: Junwen Yao <jwyiao@gmail.com>
@harupy I just updated the comments in this PR, including #4954 (review). |
@harupy @dbczumar Thank you very much for your feedback and suggestions! It seems we are at the final stage of this PR. Once it is merged, I will make the second PR which completes the autologging for XGBoost sklearn models based on our previous discussion. It should be ready within the next few days. Meanwhile I will also submit the first PR for LightGBM models (similar to this PR). Thanks again! |
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! Thanks so much @jwyyy !
Yep, we're already investigating them. Let's merge this PR! |
Signed-off-by: Junwen Yao jwyiao@gmail.com
What changes are proposed in this pull request?
This is the first PR to add autologging for XGBoost sklearn models. It revises model saving / loading and adds
model_class
to specify XGBoost model class, such as Booster, or XGBRegressor.A separate PR will be made to add autologging for XGBoost sklearn models using
mlflow.sklearn
routine.(Draft + discussion: #4885)
How is this patch tested?
New test functions are added. See PR files.
Release Notes
Is this a user-facing change?
This PR will enable saving / loading XGBoost models, including sklearn models, with model class specification.
Functions
save_model()
/load_model()
inmlflow.xgboost
can be used as before.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/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