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Mike J Innes 2017-09-28 11:11:11 +01:00
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docs/src/gpu.md
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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. 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.
For example, we can use `CuArrays` (with the `cu` array converter) to run our [basic example](models/basics.md) on an NVIDIA GPU. For example, we can use `CuArrays` (with the `cu` converter) to run our [basic example](models/basics.md) on an NVIDIA GPU.
```julia ```julia
using CuArrays using CuArrays
W = cu(rand(2, 5)) W = cu(rand(2, 5)) # a 2×5 CuArray
b = cu(rand(2)) b = cu(rand(2))
predict(x) = W*x .+ b predict(x) = W*x .+ b
@ -31,3 +31,5 @@ m = Chain(Dense(10, 5, σ), Dense(5, 2), softmax)
m = mapparams(cu, m) m = mapparams(cu, m)
d(cu(rand(10))) d(cu(rand(10)))
``` ```
The [mnist example](https://github.com/FluxML/model-zoo/blob/master/mnist/mnist.jl) contains the code needed to run the model on the GPU; just uncomment the lines after `using CuArrays`.