Python version should be enforced in python MLprojects

[aarondav]

When using the mlflow tracking API, I may use a conda environment to enforce reproducibility. One potentially non-obvious dependency is the Python version I use (in particular, 2 vs 3), especially pertinent since the default pickling version changed such that pickled objects in python3 are not readable by python2.

We should strive to help users remember to specify their python version, potentially by printing a warning if they provide a conda env that specifies no python version. The only problem with this particular proposal is if we later leverage the conda environment for other languages (e.g., R), although if we use reticulate, then we may anyway need Python.

As an example, not remembering to include the Python version in our tutorial example made it difficult for @tomasatdatabricks and I to collaborate, as he had miniconda3 installed and I had miniconda2 installed, which only showed up at model deploy time.

[aarondav]

@mateiz any thoughts on this problem / proposal?

[mateiz]

It's definitely a good idea, but how should we do it exactly? Just warn if the environment doesn't have one?

One thing we can certainly do is make sure to include it in the default environments we create for pyfunc, sklearn, etc models. I think @tomasatdatabricks has already been thinking about that.

[tomasatdatabricks]

The default conda environments generated by mlflow models now include python version. In case of projects it's up to the project author as is the rest of the dependencies.