GPU docs #2510
GPU docs #2510
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Once the build has completed, you can preview any updated documentation at this URL: https://fluxml.ai/Flux.jl/previews/PR2510/ in ~20 minutes In particular, this page: https://fluxml.ai/Flux.jl/previews/PR2510/guide/gpu/ |
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removing the milestone as this shouldn't be blocking |
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The point is, in part, to think through whatever interface we're adopting by trying to explain it clearly. If it's a mess then 0.15 is when to fix it. That's why it was on the milestone. |
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Can we get this done so that it doesn't delay the 0.15 release then? I would like to tag in a few days |
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Sure. What do you think this lacks? |
| !!! compat "Flux ≤ 0.13" | ||
| Old versions of Flux automatically loaded CUDA.jl to provide GPU support. Starting from Flux v0.14, it has to be loaded separately. Julia's [package extensions](https://pkgdocs.julialang.org/v1/creating-packages/#Conditional-loading-of-code-in-packages-(Extensions)) allow Flux to automatically load some GPU-specific code when needed. |
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| !!! compat "Flux ≤ 0.13" | |
| Old versions of Flux automatically loaded CUDA.jl to provide GPU support. Starting from Flux v0.14, it has to be loaded separately. Julia's [package extensions](https://pkgdocs.julialang.org/v1/creating-packages/#Conditional-loading-of-code-in-packages-(Extensions)) allow Flux to automatically load some GPU-specific code when needed. |
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| ## Manually selecting devices | ||
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| I thought there was a whole `Flux.gpu_backend!` and Preferences.jl story we had to tell?? |
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gpu_backend! affects the return from gpu_device like this:
- If no GPU is available, it returns a CPUDevice object.
- If a LocalPreferences file is present, then the backend specified in the file is used. If the trigger package corresponding to the device is not loaded, then a warning is displayed.
- If no LocalPreferences file is present, then the first working GPU with loaded trigger package is used.
This is already described in the docstring of gpu_device. I think we shouldn't mention gpu_backend! at all in this guide because it is useless in practice.
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Maybe we should put a TLDR at the top just saying something like using CUDA # or AMDGPU or Metal
device = gpu_device()
model = model |> device
for epoch in 1:num_epochs
for (x, y) in dataloader
x, y = device((x, y))
... compute gradients and update model ...
end
end |
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I will finish this |
This is what we're trying not to do. Not "here's the magic boilerplate you should copy", but instead the guide is "here's how things actually work". |
This re-writes the start of the GPU documentation page. It aims to use simpler examples, and stress that
model |> cujust works, before talking about more exotic non-CUDA packages, and the automaticmodel |> gpu.Rendered MD: https://github.com/FluxML/Flux.jl/blob/gpu_docs/docs/src/guide/gpu.md
Current docs: http://fluxml.ai/Flux.jl/stable/guide/gpu/