From d3419c943bd8c8c3d06ffa8ea5618625b3b14613 Mon Sep 17 00:00:00 2001 From: Mike J Innes Date: Thu, 28 Sep 2017 11:11:11 +0100 Subject: [PATCH] example link --- docs/src/gpu.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/docs/src/gpu.md b/docs/src/gpu.md index db21db3665..4452e856ab 100644 --- a/docs/src/gpu.md +++ b/docs/src/gpu.md @@ -2,12 +2,12 @@ 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`.