34 lines
1.2 KiB
Markdown
34 lines
1.2 KiB
Markdown
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# GPU Support
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Support for array operations on other hardware backends, like GPUs, is provided by external packages like [CuArrays](https://github.com/JuliaGPU/CuArrays.jl) and [CLArrays](https://github.com/JuliaGPU/CLArrays.jl). Flux doesn't care what array type you use, so we can just plug these in without any other changes.
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For example, we can use `CuArrays` (with the `cu` array converter) to run our [basic example](models/basics.md) on an NVIDIA GPU.
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```julia
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using CuArrays
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W = cu(rand(2, 5))
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b = cu(rand(2))
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predict(x) = W*x .+ b
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loss(x, y) = sum((predict(x) .- y).^2)
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x, y = cu(rand(5)), cu(rand(2)) # Dummy data
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loss(x, y) # ~ 3
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```
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Note that we convert both the parameters (`W`, `b`) and the data set (`x`, `y`) to cuda arrays. Taking derivatives and training works exactly as before.
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If you define a structured model, like a `Dense` layer or `Chain`, you just need to convert the internal parameters. Flux provides `mapparams`, which allows you to alter all parameters of a model at once.
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```julia
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d = Dense(10, 5, σ)
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d = mapparams(cu, d)
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d.W # Tracked CuArray
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d(cu(rand(10))) # CuArray output
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m = Chain(Dense(10, 5, σ), Dense(5, 2), softmax)
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m = mapparams(cu, m)
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d(cu(rand(10)))
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```
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