Add support for minor version incremental increase for pyspark

[BenWilson2]

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

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[dbczumar]

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)

Suggested change

	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}"

[BenWilson2]

excellent idea. Thanks!

[dbczumar]

Can we add a short docstring to this function explaining what it does and why it's necessary?

[BenWilson2]

added along with an example to explain why we're doing this.

[dbczumar]

LGTM with a couple comments! Thanks Ben!

[WeichenXu123]

In future we'd better improve this by in "ml-package-versions.yml" maximun version support format like "3.2.*"
CC @harupy

[WeichenXu123]

also upgrade pyspark maximum version to 3.2.1 in ml-package-versions.yml