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Add support for minor version incremental increase for pyspark #5366
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…abricks Signed-off-by: Ben Wilson <benjamin.wilson@databricks.com>
if Version(ver) > Version(min_ver): | ||
ver = _reset_minor_version(ver) |
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The following might be less brittle (let Version()
do the work of identifying major and minor) and is contained within this single function, since the logic isn't needed elsewhere. For version 3
, minor is interpreted as 0
(Version("3").minor == 0
)
if Version(ver) > Version(min_ver): | |
ver = _reset_minor_version(ver) | |
parsed_ver = Version(ver) | |
if parsed_ver > Version(min_ver): | |
ver = f"{parsed_ver.major}.{parsed_ver.minor}" |
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excellent idea. Thanks!
@@ -28,6 +28,12 @@ def _check_version_in_range(ver, min_ver, max_ver): | |||
return Version(min_ver) <= Version(ver) <= Version(max_ver) | |||
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def _check_spark_version_in_range(ver, min_ver, max_ver): |
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Can we add a short docstring to this function explaining what it does and why it's necessary?
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added along with an example to explain why we're doing this.
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LGTM with a couple comments! Thanks Ben!
Signed-off-by: Ben Wilson <benjamin.wilson@databricks.com>
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In future we'd better improve this by in "ml-package-versions.yml" maximun version support format like "3.2.*"
CC @harupy
also upgrade pyspark maximum version to 3.2.1 in ml-package-versions.yml |
Signed-off-by: Ben Wilson benjamin.wilson@databricks.com
What changes are proposed in this pull request?
Modify the version validation logic for pyspark if running on Databricks to allow for minor (or micro) version updates to still be compatible with the version validation checks.
How is this patch tested?
unit tests
Does this PR change the documentation?
ci/circleci: build_doc
check. If it's successful, proceed to thenext step, otherwise fix it.
Details
on the right to open the job page of CircleCI.Artifacts
tab.docs/build/html/index.html
.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
: 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