GPU Support

NVIDIA GPU support should work out of the box on systems with CUDA and CUDNN installed. For more details see the CUDA readme.

Checking GPU Availability

By default, Flux will run the checks on your system to see if it can support GPU functionality. You can check if Flux identified a valid GPU setup by typing the following:

julia> using CUDA

julia> CUDA.functional()
true

Support for array operations on other hardware backends, like GPUs, is provided by external packages like CUDA. 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 CUDA.CuArray (with the cu converter) to run our basic example on an NVIDIA GPU.

(Note that you need to have CUDA available to use CUDA.CuArray – please see the CUDA.jl instructions for more details.)

using CUDA

W = cu(rand(2, 5)) # a 2×5 CuArray
b = cu(rand(2))

predict(x) = W*x .+ b
loss(x, y) = sum((predict(x) .- y).^2)

x, y = cu(rand(5)), cu(rand(2)) # Dummy data
loss(x, y) # ~ 3

Note that we convert both the parameters (W, b) and the data set (x, y) to cuda arrays. Taking derivatives and training works exactly as before.

If you define a structured model, like a Dense layer or Chain, you just need to convert the internal parameters. Flux provides fmap, which allows you to alter all parameters of a model at once.

d = Dense(10, 5, σ)
d = fmap(cu, d)
d.weight # CuArray
d(cu(rand(10))) # CuArray output

m = Chain(Dense(10, 5, σ), Dense(5, 2), softmax)
m = fmap(cu, m)
d(cu(rand(10)))

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. So, you can safely call gpu on some data or model (as shown below), and the code will not error, regardless of whether the GPU is available or not. If the GPU library (CUDA.jl) loads successfully, gpu will move data from the CPU to the GPU. As is shown below, this will change the type of something like a regular array to a CuArray.

julia> using Flux, CUDA

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)
5-element CuArray{Float32,1}:
 -0.30535
 ⋮
 -0.618002

The analogue cpu is also available for moving models and data back off of the GPU.

julia> x = rand(10) |> gpu
10-element CuArray{Float32,1}:
 0.235164
 ⋮
 0.192538

julia> x |> cpu
10-element Array{Float32,1}:
 0.235164
 ⋮
 0.192538

Disabling CUDA or choosing which GPUs are visible to Flux

Sometimes it is required to control which GPUs are visible to julia on a system with multiple GPUs or disable GPUs entirely. This can be achieved with an environment variable CUDA_VISIBLE_DEVICES.

To disable all devices:

$ export CUDA_VISIBLE_DEVICES='-1'

To select specific devices by device id:

$ export CUDA_VISIBLE_DEVICES='0,1'

More information for conditional use of GPUs in CUDA.jl can be found in its documentation, and information about the specific use of the variable is described in the Nvidia CUDA blogpost.

GPU Support

Checking GPU Availability

GPU Usage

Disabling CUDA or choosing which GPUs are visible to Flux