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Flux.jl
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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.
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
using CuArrays
W = cu(rand(2, 5))
W = cu(rand(2, 5)) # a 2×5 CuArray
b = cu(rand(2))
predict(x) = W*x .+ b
@ -31,3 +31,5 @@ m = Chain(Dense(10, 5, σ), Dense(5, 2), softmax)
m = mapparams(cu, m)
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`.