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I'm actually curious if libraries like pytorch are even trying to move away from CUDA and if moving away from it is worth it. I get why newer ML toolchains woul
by laserbeam 3y ago
I'm actually curious if libraries like pytorch are even trying to move away from CUDA and if moving away from it is worth it. I get why newer ML toolchains would do that but do mainstream established MI frameeorks plan on sticking with nvidia exclusivity for now?
- zwaps 3y agoMove to where? AMD's Rocm doesn't even support their own cards.
- smoldesu 3y agoThere literally aren't other options. Start with Pytorch; if you removed CUDA support, all that's left is hacks honestly. Stuff like Olive and DirectML is equally as proprietary as CUDA while also less-developed, and Metal Performance Shaders are a hacked wrapper for GPU compute. See for yourself: https://pytorch.org/docs/stable/backends.html https://pytorch.org/docs/stable/backends.html There's also ONNX, a Pytorch alternative made by Microsoft that focuses on optimizing for as many targets as possible. It is very difficult to use though, and still under active development. Generally, the issue is that our FAANG companies hate each other too much to do anything about Nvidia. AMD and Apple did have a working relationship (and even a GPGPU acceleration library), but now they are bitter enemies.
- laserbeam 3y agoI'm thinking maybe WebGPU might get one far enough in terms of being able to express ML pipelines? Unsure. I'm not super close to the field.