gpu docs

docs/src/gpu.md

@@ -1,6 +1,6 @@
 # GPU Support
 
-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). Flux is agnostic to array types, so we simply need to move model weights and data to the GPU and Flux will handle it.
 
 For example, we can use `CuArrays` (with the `cu` converter) to run our [basic example](models/basics.md) on an NVIDIA GPU.
 
@@ -32,4 +32,23 @@ m = mapleaves(cu, m)
 d(cu(rand(10)))
 ```
 
-The [mnist example](https://github.com/FluxML/model-zoo/blob/master/mnist/mlp.jl) contains the code needed to run the model on the GPU; just uncomment the lines after `using CuArrays`.
+As a convenience, Flux provides the `gpu` function to convert models and data to the GPU if one is available. By default, it'll do nothing, but loading `CuArrays` will cause it to move data to the GPU instead.
+
+```julia
+julia> using Flux, CuArrays
+
+julia> m = Dense(10,5) |> gpu
+Dense(10, 5)
+
+julia> x = rand(10) |> gpu
+10-element CuArray{Float32,1}:
+ 0.800225
+ ⋮
+ 0.511655
+
+julia> m(x)
+Tracked 5-element CuArray{Float32,1}:
+ -0.30535
+ ⋮
+ -0.618002
+```