Fix issues in handling flexible numpy datatypes in TensorSpec#4147
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arjundc-db merged 8 commits intomlflow:masterfrom Feb 26, 2021
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Fix issues in handling flexible numpy datatypes in TensorSpec#4147arjundc-db merged 8 commits intomlflow:masterfrom
arjundc-db merged 8 commits intomlflow:masterfrom
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wentinghu
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lgtm, thanks for working on this!!
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Looks good, I left some comments.
In addition, we should also test that schema enforcement works for all types. I think it would make sense to extend your test_all_numpy_types tests to also call _enforce_tensor_schema. It's a bit unfortunate that it is defined in pyfunc. I think we can consider moving it down the line but for now I would just call it from this test.
Signed-off-by: Arjun DCunha <arjun.dcunha@databricks.com>
Signed-off-by: Arjun DCunha <arjun.dcunha@databricks.com>
Signed-off-by: Arjun DCunha <arjun.dcunha@databricks.com>
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…#4147) * Improve Signed-off-by: Arjun DCunha <arjun.dcunha@databricks.com> * Comments Signed-off-by: Arjun DCunha <arjun.dcunha@databricks.com> * LINT Signed-off-by: Arjun DCunha <arjun.dcunha@databricks.com> * indent Signed-off-by: Arjun DCunha <arjun.dcunha@databricks.com> * Small improve Signed-off-by: Arjun DCunha <arjun.dcunha@databricks.com> * Tomas comments Signed-off-by: Arjun DCunha <arjun.dcunha@databricks.com> * retrigger tets Signed-off-by: Arjun DCunha <arjun.dcunha@databricks.com> * PLease Signed-off-by: Arjun DCunha <arjun.dcunha@databricks.com> Signed-off-by: harupy <17039389+harupy@users.noreply.github.com>
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…#4151) * Improve Signed-off-by: Arjun DCunha <arjun.dcunha@databricks.com> * Comments Signed-off-by: Arjun DCunha <arjun.dcunha@databricks.com> * LINT Signed-off-by: Arjun DCunha <arjun.dcunha@databricks.com> * indent Signed-off-by: Arjun DCunha <arjun.dcunha@databricks.com> * Small improve Signed-off-by: Arjun DCunha <arjun.dcunha@databricks.com> * Tomas comments Signed-off-by: Arjun DCunha <arjun.dcunha@databricks.com> * retrigger tets Signed-off-by: Arjun DCunha <arjun.dcunha@databricks.com> * PLease Signed-off-by: Arjun DCunha <arjun.dcunha@databricks.com> Signed-off-by: harupy <17039389+harupy@users.noreply.github.com> Co-authored-by: Arjun DCunha <61438563+arjundc-db@users.noreply.github.com>
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…#4147) * Improve Signed-off-by: Arjun DCunha <arjun.dcunha@databricks.com> * Comments Signed-off-by: Arjun DCunha <arjun.dcunha@databricks.com> * LINT Signed-off-by: Arjun DCunha <arjun.dcunha@databricks.com> * indent Signed-off-by: Arjun DCunha <arjun.dcunha@databricks.com> * Small improve Signed-off-by: Arjun DCunha <arjun.dcunha@databricks.com> * Tomas comments Signed-off-by: Arjun DCunha <arjun.dcunha@databricks.com> * retrigger tets Signed-off-by: Arjun DCunha <arjun.dcunha@databricks.com> * PLease Signed-off-by: Arjun DCunha <arjun.dcunha@databricks.com> Signed-off-by: harupy <17039389+harupy@users.noreply.github.com>
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…#4147) * Improve Signed-off-by: Arjun DCunha <arjun.dcunha@databricks.com> * Comments Signed-off-by: Arjun DCunha <arjun.dcunha@databricks.com> * LINT Signed-off-by: Arjun DCunha <arjun.dcunha@databricks.com> * indent Signed-off-by: Arjun DCunha <arjun.dcunha@databricks.com> * Small improve Signed-off-by: Arjun DCunha <arjun.dcunha@databricks.com> * Tomas comments Signed-off-by: Arjun DCunha <arjun.dcunha@databricks.com> * retrigger tets Signed-off-by: Arjun DCunha <arjun.dcunha@databricks.com> * PLease Signed-off-by: Arjun DCunha <arjun.dcunha@databricks.com> Signed-off-by: harupy <17039389+harupy@users.noreply.github.com>
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Signed-off-by: Arjun DCunha arjun.dcunha@databricks.com
What changes are proposed in this pull request?
The serialization and deserialization of flexible numpy data types was failing due to numpy adding the size dynamically to the dtype.
The PR strips the flexible numpy dtypes of all size based information before creating the tensorSpec.
How is this patch tested?
Unit tests.
Release Notes
Is this a user-facing change?
(Details in 1-2 sentences. You can just refer to another PR with a description if this PR is part of a larger 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/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: Local serving, model deployment tools, spark UDFsarea/server-infra: MLflow server, JavaScript dev serverarea/tracking: Tracking Service, tracking client APIs, autologgingInterface
area/uiux: Front-end, user experience, JavaScript, plottingarea/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