Environment markers for GPU/CUDA availibility

Hi all, trying to pull together a few separate discussions into a
single thread here.

The main issue is that currently PEP 508 does not provide environment
markers for GPU/CUDA availability, which leads to problems for
projects that want to provide distributions for environments with and
without GPU support.

As far as I can tell, there's been multiple suggestions to bring this
issue to distutils-sig, but no one has actually done it.

Relevant issues:

(closed) "How should Python packages depending on TensorFlow structure
their requirements?"
https://github.com/tensorflow/tensorflow/issues/7166

(closed) "Adding gpu or cuda specification in PEP 508"
https://github.com/python/peps/issues/581

(closed) "More support for conditional installation"
https://github.com/pypa/pipenv/issues/1353

(no response) "Adding gpu or cuda markers in PEP 508"
https://github.com/pypa/interoperability-peps/issues/68

There is now a third-party project which attempts to amend this for
tensorflow (https://github.com/akatrevorjay/tensorflow-auto-detect)
but this approach is somewhat fragile (depends on version numbers
being in sync), doesn't directly scale to all similar projects, and
would require maintainers for a given project to maintain _three_
separate projects, instead of just one.

I'm not intimately familiar with PEP 508, so my questions for this list:

* Is the demand sufficient to justify supporting this use case?
* Is it possible to add support for GPU Environment markers?
* If so, what would need to be done?
* If implemented, what should the transition look like for projects
like tensorflow?

Thanks!
D.
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