If the GPU card is detected but the drivers aren't installed, what should it do?
On Friday, August 31, 2018, Tzu-ping Chung <
uranusjr@gmail.com> wrote:
I’m not knowledgable about GPUs, but from limited conversations with others,
it is important to first decide what exactly the problem area is. Unlike currently
available environment markers, there’s currently not a very reliable way to
programmatically determine even if there is a GPU, let alone what that GPU can
actually do (not every GPU can be used by Tensorflow, for example).
IMO it would likely be a good route to first implement some interface for GPU
environment detection in Python. This interface can then be used in projects like
tensorflow-auto-detect. Projects like Tensorflow can also detect directly what
implementation it should use, like many projects do platform-specific things by
detection os.name of sys.platform. Once we’re sure we have all the things needed
for detection, markers can be drafted based on the detection interface.
TP
> On 01/9/2018, at 03:57, Dustin Ingram <di@python.org> wrote:
>
> 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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