Implement issue-5501 - add mlflow_version info to logged model

[r3stl355]

Signed-off-by: Nikolay Ulmasov ulmasov@hotmail.com

What changes are proposed in this pull request?

Implement issue #5501 - Add an mlflow_version field to the MLModel spec that is automatically populated with the current MLflow version when an MLflow Model is saved / logged.

Models logged with previous versions will have mlflow_version=None when loaded from a file.

How is this patch tested?

Extended existing tests and added a test to load previously logged models without mlflow_version info

Does this PR change the documentation?

• [ x] No. You can skip the rest of this section.
• Yes. Make sure the changed pages / sections render correctly by following the steps below.

1. Check the status of the ci/circleci: build_doc check. If it's successful, proceed to the
   next step, otherwise fix it.
2. Click Details on the right to open the job page of CircleCI.
3. Click the Artifacts tab.
4. Click docs/build/html/index.html.
5. Find the changed pages / sections and make sure they render correctly.

Release Notes

Is this a user-facing change?

• No. You can skip the rest of this section.
• Yes. Give a description of this change to be included in the release notes for MLflow users.

Users can check the version of MLFlow the model with logged with.

(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 logging
• area/build: Build and test infrastructure for MLflow
• area/docs: MLflow documentation pages
• area/examples: Example code
• area/model-registry: Model Registry service, APIs, and the fluent client calls for Model Registry
• [ x] area/models: MLmodel format, model serialization/deserialization, flavors
• area/projects: MLproject format, project running backends
• area/scoring: MLflow Model server, model deployment tools, Spark UDFs
• area/server-infra: MLflow Tracking server backend
• area/tracking: Tracking Service, tracking client APIs, autologging

Interface

• area/uiux: Front-end, user experience, plotting, JavaScript, JavaScript dev server
• area/docker: Docker use across MLflow's components, such as MLflow Projects and MLflow Models
• area/sqlalchemy: Use of SQLAlchemy in the Tracking Service or Model Registry
• area/windows: Windows support

Language

• language/r: R APIs and clients
• language/java: Java APIs and clients
• language/new: Proposals for new client languages

Integrations

• integrations/azure: Azure and Azure ML integrations
• integrations/sagemaker: SageMaker integrations
• integrations/databricks: Databricks integrations

How should the PR be classified in the release notes? Choose one:

• rn/breaking-change - The PR will be mentioned in the "Breaking Changes" section
• rn/none - No description will be included. The PR will be mentioned only by the PR number in the "Small Bugfixes and Documentation Updates" section
• [x ] rn/feature - A new user-facing feature worth mentioning in the release notes
• rn/bug-fix - A user-facing bug fix worth mentioning in the release notes
• rn/documentation - A user-facing documentation change worth mentioning in the release notes

[r3stl355]

Oops, I just noticed that ./dev/run-small-python-tests.sh was failing to finish collecting tests due to missing google.cloud.storage on my machine. I thought it was failing 1 test which had nothing to do with my changes so assumed my changes were fine. I'll fix, do a full test and re-submit

[dbczumar]

@r3stl355 Looks great! LGTM once remaining comments have been addressed!

[BenWilson2]

Thanks for the addition! LGTM!