None of them are particularly difficult to get running, the trick is to search the project’s github issue tracker. 99% of the time your problem will be in there with steps to fix it.
What ever happened to the crazy notion of Dockerfiles that simply build successfully?
Isn’t half the point of containerisation that it papers over the madness of the Python module ecosystem?
Complaining that people won’t work for you for free is a bit much, don’t you think?
Now it is apparently seen as "working for free for ungrateful people"
That has nothing to do with Python tooling being bad. A safe assumption is that Python package managers are being developed by developers, who have no excuse.
If a C++ codebase developed by scientists had null pointer exceptions in it, then I could excuse things. But if the C++ compiler itself introduced unforced null pointer errors, then it absolutely deserves criticism.
It shouldn't be possible for a ML researcher to use Conda or whatever package manager in a way that despite using a formally specified "requirements.txt", it won't build a week later because of how loose the specification of module versions is allowed to be.
The Python attitude and more specifically Conda is at fault here, not the ML researching trying to get his job done.
> Conda is at fault here
Conda is so so bad. But trying to explain why to people who have fallen into it’s trap is difficult. People don’t realize the packages are not signed on enough information to reproduce them. The optimizer to find matching versions to make an environment that satisfies your constraints is really bad idea.
As an experienced C++ developer unfortunately and fortunately I’ve concluded the most “correct” solution is to use nixpkgs.
It’s a problem.
Facebook seems to be pretty hands off (as is expected since the code is open source) unless you distribute the model weights and then they drop the dmca banhammer.
So, yeah, simply complaining with no effort to understand the problem is kind of ungrateful.