Re:Cuda.jl v3.3: union types, debug info, graph APIs
by version_five on Sunday June 13, 2021 @06:56PM
(#27497368)
I last experimented with CUDA.jl a year ago, and it was very useable then. This is a good reminder to re-evaluate the Julia deep learning ecosystem. If I were working for myself I would definitely try to do more with Julia (for machine learning). Realistically, python has such an established base that it will take some time to get orgs that are already all in on python to come over.
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Re:Cuda.jl v3.3: union types, debug info, graph APIs
by dnautics on Sunday June 13, 2021 @08:23PM
(#27497962)
I think it's not dumb to target Greenfield users: just installing python gpu wheels is often difficult enough that several companies exist (indirectly) because it's so difficult to do right (e.g. selling a gpu PC with that stuff preinstalled)
Re:Cuda.jl v3.3: union types, debug info, graph APIs
by queuebert on Sunday June 13, 2021 @11:05PM
(#27498853)
I just finished setting up a new machine to run some Kaggle stuff. Both Tensorflow and PyTorch had issues with CUDA versions and dependencies that weren't immediately fixed with a clean virtualenv, while both Knet.jl and Flux.jl installed flawlessly.
Re:Cuda.jl v3.3: union types, debug info, graph APIs
by wdroz on Monday June 14, 2021 @02:44AM
(#27499944)
For Pytorch and Tensorflow, you can use conda to install them with the right CUDA and cudnn versions.
Re:Cuda.jl v3.3: union types, debug info, graph APIs
by kwertzzz on Monday June 14, 2021 @03:18AM
(#27500155)
For Pytorch, I had no issues with conda. But with Tensorflow from conda, the training process just hangs (consuming 100% of CPU but no GPU resources, despite my GPUs are recognized). I got more luck with installing Tensorflow with pip. Given the fact that Tensorflow documentation does not mention conda, I wondering how well this is supported.