One thing is python's dependency management is just insane in itself. When you then additionally have to have it install basically drivers for the gpu, compile loads of native code etc it becomes a hot mess. And half the ML things you want to try are academic experiments not really made for distribution, so they were made to work and that's it. So if you have some different computer setup, minor version mismatch of a dependency etc it will just break. Datasets or models excist on some url that only works for a month.

It's a shame, I think the field is one where reproduction of results should be really welcome and feasible.

Yeah, a docker image would be nice. Even a Dockerfile, even though that in itself may not guarantee reproducibility if you try to build it later. (And may have issues with gpu drivers etc). But at least it documents all assumptions about the setup.

No. This would take a very long post to explain why, but in short, it depends heavily on which NVIDIA Cards / CUDA drivers / versions of Linux you're using. Or you're running on AWS GPUs or TPUs and paying a lot more either in the form of straight up dollars or optionality.

> What is the solution?

There are languages ecosystem that solved these issues and never have a problem with it. Look at it, and copy their tooling.

Cloud services.

Someone sets it up, leases out instances etc..

Or someone works to ensure that everything gels together and provides support etc..

Or in other words: 'products'