Feature/pipeline ml inputs

[Galileo-Galilei]

Description

Closes #71 and #100.

Development notes

• PipelineML.extract_pipeline_catalog is renamed PipelineML._extract_pipeline_catalog to show it is private
• Change the doc to deprecate using extract_pipeline_catalog in favor of extract_pipeline_artifacts
• PipelineML now has a logger property
• PipelineML now accepts that inference inputs may be in training inputs (and not only in all outputs

Checklist

• Read the contributing guidelines
• Open this PR as a 'Draft Pull Request' if it is work-in-progress
• Update the documentation to reflect the code changes
• Add a description of this change and add your name to the list of supporting contributions in the CHANGELOG.md file. Please respect Keep a Changelog guidelines.
• Add tests to cover your changes

Notice

• I acknowledge and agree that, by checking this box and clicking "Submit Pull Request":

• I submit this contribution under the Apache 2.0 license and represent that I am entitled to do so on behalf of myself, my employer, or relevant third parties, as applicable.

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• I certify that the use of this contribution as authorised by the Apache 2.0 license does not violate the intellectual property rights of anyone else.

[codecov-io]

Codecov Report

Merging #101 into develop will decrease coverage by 1.22%.
The diff coverage is 100.00%.

@@             Coverage Diff             @@
##           develop     #101      +/-   ##
===========================================
- Coverage    98.53%   97.31%   -1.23%     
===========================================
  Files           20       20              
  Lines          616      632      +16     
===========================================
+ Hits           607      615       +8     
- Misses           9       17       +8     

Impacted Files	Coverage Δ
kedro_mlflow/framework/hooks/pipeline_hook.py	98.79% <100.00%> (ø)
kedro_mlflow/mlflow/kedro_pipeline_model.py	100.00% <100.00%> (ø)
kedro_mlflow/pipeline/pipeline_ml.py	92.30% <100.00%> (-7.70%)	⬇️

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[Galileo-Galilei]

Known pb with test coverage which includes test folder. It will be solved once #98 is merged, so we should merge it before and I'll rebase on it.

[takikadiri]

I point out some typo errors and the management of the kedro parameters by the PipelieML

[takikadiri]

Do we allow the use of parameters as inference / training inputs?
kedro create params:xxx inputs as a MemoryDataSet. The following PipelineML code exclude them from our inference pipelines :

if isinstance(data_set, MemoryDataSet):
     raise KedroMlflowPipelineMLDatasetsError(...)

[Galileo-Galilei]

I hesitated to deal with parameters automatically, but:

• it is quite complicated: there are a some edge case situation to deal with, we have to decide how / when / where to persist them
• it is error prone: I don't want to persist parameters that are not explictly intended to.

On the other hand, it is very easy for a user to enforce a parameter just by persisting either as an input or output of a "training" node, e.g. by creating a YAMLDataSet, so I think we can just let it to the user to be sure that it voluntary.

[Galileo-Galilei]

It seems coverage has decreased when I reabsed, I may have skipped a test. Do not merge it yet.

[takikadiri]

Ok ! For merging multi FIX PRs, do you prefer to pack the commits with a PR merge commit, or i just rebase and merge?

[Galileo-Galilei]

I always "rebase and merge" to the develop branch. The only merges are from develop to master.

[Galileo-Galilei]

@takikadiri It's good to go!