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Oh man, I deal with CUDA version nuances all the time. ML dependency management in particular is always extra fun. Between all the different CUDA, CuDNN, NCCL v
by alfalfasprout 3y ago
Oh man, I deal with CUDA version nuances all the time. ML dependency management in particular is always extra fun. Between all the different CUDA, CuDNN, NCCL versions and versions of TF frameworks and numpy dependencies, etc. it can quickly become a mess.
We've started really investing into a better solution-- always interesting to see just how big a difference getting the right CUDA version for a given build of eg; torch is.
- magixx 3y agoAnytime I want try out some ML stuff I run into this driver and app version hell. Have you given Docker a try? I'm considering going this route for portability reasons but don't know if it will actually help.
- fnands 3y agoDocker is good, but I also haven't had any issues with conda. At least for most ML projects.
- alfalfasprout 3y agoDocker comes with its own problems (especially if you need to mutate the image under the hood). And multi arch quickly becomes a pain. Also much harder to make it truly portable. We're rolling something around conda+conda-pack and plan to contribute it upstream. Look out for a blog post on HN later this year :)