Mention CUDAnative.jl's install instructions
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Flux is an elegant approach to machine learning. It's a 100% pure-Julia stack, and provides lightweight abstractions on top of Julia's native GPU and AD support. Flux makes the easy things easy while remaining fully hackable.
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You need to build Julia 0.6 from source and have CUDA available to use Flux with GPUs – please see the [CUDAnative.jl](https://github.com/JuliaGPU/CUDAnative.jl) instructions for more details.
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```julia
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julia> Pkg.add("Flux")
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```
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# GPU Support
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You need to build Julia 0.6 from source and have CUDA available to use Flux with GPUs – please see the [CUDAnative.jl](https://github.com/JuliaGPU/CUDAnative.jl) instructions for more details.
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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). Flux is agnostic to array types, so we simply need to move model weights and data to the GPU and Flux will handle it.
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For example, we can use `CuArrays` (with the `cu` converter) to run our [basic example](models/basics.md) on an NVIDIA GPU.
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```
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Start with the [basics](models/basics.md). The [model zoo](https://github.com/FluxML/model-zoo/) is also a good starting point for many common kinds of models.
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See [GPU support](gpu.md) for more details on installing and using Flux with GPUs.
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