Replace mlflow.pyfunc.load_pyfunc with mlflow.pyfunc.load_model
#5290
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Signed-off-by: harupy <hkawamura0130@gmail.com>
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Signed-off-by: harupy <hkawamura0130@gmail.com>
harupy
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Jan 21, 2022
mlflow/pyfunc/__init__.py
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| @@ -668,31 +667,6 @@ def load_model(model_uri: str, suppress_warnings: bool = True, dst_path: str = N | |||
| return PyFuncModel(model_meta=model_meta, model_impl=model_impl) | |||
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| @deprecated("mlflow.pyfunc.load_model", 1.0) | |||
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FYI: MLflow 1.0 was released on Jun 4, 2019:
https://pypi.org/project/mlflow/1.0.0
Signed-off-by: harupy <17039389+harupy@users.noreply.github.com>
Signed-off-by: harupy <17039389+harupy@users.noreply.github.com>
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mlflow.pyfunc.load_pyfuncmlflow.pyfunc.load_pyfunc with mlflow.pyfunc.load_model
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Apr 11, 2022
…lflow#5290) * Remove deprecated mlflow.pyfunc.load_pyfunc Signed-off-by: harupy <hkawamura0130@gmail.com> * fix doc Signed-off-by: harupy <hkawamura0130@gmail.com> * lint Signed-off-by: harupy <hkawamura0130@gmail.com> * import load_model Signed-off-by: harupy <hkawamura0130@gmail.com> * revert Signed-off-by: harupy <17039389+harupy@users.noreply.github.com> * replace load_pyfunc in test_transformer_model_export Signed-off-by: harupy <17039389+harupy@users.noreply.github.com>
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…se image (#356) <!-- Thanks for sending a pull request! Here are some tips for you: 1. Run unit tests and ensure that they are passing 2. If your change introduces any API changes, make sure to update the e2e tests 3. Make sure documentation is updated for your PR! --> **What this PR does / why we need it**: <!-- Explain here the context and why you're making the change. What is the problem you're trying to solve. ---> The current implementation of the new base image for batch predictor upgrade the MLflow version to 1.30.0, however, there's a backward incompatibility in the loading model of SparkModel: mlflow/mlflow#5290 The SparkModelCache.get_or_load in mlflow>=1.25.0 returns a tuple instead of a pyfunc object. To make it more predictable, we also pin mlflow version in pyfunc's base image to 1.23.0 to match merlin-sdk's requirements. **Does this PR introduce a user-facing change?**: <!-- If no, just write "NONE" in the release-note block below. If yes, a release note is required. Enter your extended release note in the block below. If the PR requires additional action from users switching to the new release, include the string "action required". For more information about release notes, see kubernetes' guide here: http://git.k8s.io/community/contributors/guide/release-notes.md --> ```release-note NONE ``` **Checklist** - [ ] Added unit test, integration, and/or e2e tests - [ ] Tested locally - [ ] Updated documentation - [ ] Update Swagger spec if the PR introduce API changes - [ ] Regenerated Golang and Python client if the PR introduce API changes
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Signed-off-by: harupy hkawamura0130@gmail.com
What changes are proposed in this pull request?
Replace
mlflow.load_pyfunc(deprecated) withmlflow.pyfunc.load_model.How is this patch tested?
Existing tests
Does this PR change the documentation?
ci/circleci: build_doccheck. If it's successful, proceed to thenext step, otherwise fix it.
Detailson the right to open the job page of CircleCI.Artifactstab.docs/build/html/index.html.Release Notes
Is this a user-facing change?
mlflow.pyfunc.load_pyfunchas been removed.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