GPU Support

Starting with v0.14, Flux doesn't force a specific GPU backend and the corresponding package dependencies on the users. Thanks to the package extension mechanism introduced in julia v1.9, Flux conditionally loads GPU specific code once a GPU package is made available (e.g. through using CUDA).

NVIDIA GPU support requires the packages CUDA.jl and cuDNN.jl to be installed in the environment. In the julia REPL, type ] add CUDA, cuDNN to install them. For more details see the CUDA.jl readme.

AMD GPU support is available since Julia 1.9 on systems with ROCm and MIOpen installed. For more details refer to the AMDGPU.jl repository.

Metal GPU acceleration is available on Apple Silicon hardware. For more details refer to the Metal.jl repository. Metal support in Flux is experimental and many features are not yet available.

In order to trigger GPU support in Flux, you need to call using CUDA, using AMDGPU or using Metal in your code. Notice that for CUDA, explicitly loading also cuDNN is not required, but the package has to be installed in the environment.

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

For AMD GPU:

julia> using AMDGPU

julia> AMDGPU.functional()
true

julia> AMDGPU.functional(:MIOpen)
true

For Metal GPU:

julia> using Metal

julia> Metal.functional()
true

Selecting GPU backend

Available GPU backends are: CUDA, AMDGPU and Metal.

Flux relies on Preferences.jl for selecting default GPU backend to use.

There are two ways you can specify it:

• From the REPL/code in your project, call Flux.gpu_backend!("AMDGPU") and restart (if needed) Julia session for the changes to take effect.
• In LocalPreferences.toml file in you project directory specify:

[Flux]
gpu_backend = "AMDGPU"

Current GPU backend can be fetched from Flux.GPU_BACKEND variable:

julia> Flux.GPU_BACKEND
"CUDA"

The current backend will affect the behaviour of methods like the method gpu described below.

Basic GPU Usage

Support for array operations on other hardware backends, like GPUs, is provided by external packages like CUDA.jl, AMDGPU.jl, and Metal.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 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)
m(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 a GPU library (e.g. CUDA) 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)      # 55 parameters

julia> x = rand(10) |> gpu
10-element CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}:
 0.066846445
 ⋮
 0.76706964

julia> m(x)
5-element CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}:
 -0.99992573
 ⋮
 -0.547261

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, CUDA.Mem.DeviceBuffer}:
 0.8019236
 ⋮
 0.7766742

julia> x |> cpu
10-element Vector{Float32}:
 0.8019236
 ⋮
 0.7766742

Transferring Training Data

In order to train the model using the GPU both model and the training data have to be transferred to GPU memory. Moving the data can be done in two different ways:

1. Iterating over the batches in a DataLoader object transferring each one of the training batches at a time to the GPU. This is recommended for large datasets. Done by hand, it might look like this:

   train_loader = Flux.DataLoader((X, Y), batchsize=64, shuffle=true)
   # ... model definition, optimiser setup
   for epoch in 1:epochs
       for (x_cpu, y_cpu) in train_loader
           x = gpu(x_cpu)
           y = gpu(y_cpu)
           grads = gradient(m -> loss(m, x, y), model)
           Flux.update!(opt_state, model, grads[1])
       end
   end

   Rather than write this out every time, you can just call gpu(::DataLoader):

   gpu_train_loader = Flux.DataLoader((X, Y), batchsize=64, shuffle=true) |> gpu
   # ... model definition, optimiser setup
   for epoch in 1:epochs
       for (x, y) in gpu_train_loader
           grads = gradient(m -> loss(m, x, y), model)
           Flux.update!(opt_state, model, grads[1])
       end
   end

   This is equivalent to DataLoader(MLUtils.mapobs(gpu, (X, Y)); keywords...). Something similar can also be done with CUDA.CuIterator, gpu_train_loader = CUDA.CuIterator(train_loader). However, this only works with a limited number of data types: first(train_loader) should be a tuple (or NamedTuple) of arrays.

2. Transferring all training data to the GPU at once before creating the DataLoader. This is usually performed for smaller datasets which are sure to fit in the available GPU memory.

   gpu_train_loader = Flux.DataLoader((X, Y) |> gpu, batchsize = 32)
   # ...
   for epoch in 1:epochs
       for (x, y) in gpu_train_loader
           # ...

   Here (X, Y) |> gpu applies gpu to both arrays, as it recurses into structures.

Saving GPU-Trained Models

After the training process is done, one must always transfer the trained model back to the cpu memory scope before serializing or saving to disk. This can be done, as described in the previous section, with:

model = cpu(model) # or model = model |> cpu

and then

using BSON
# ...
BSON.@save "./path/to/trained_model.bson" model

# in this approach the cpu-transferred model (referenced by the variable `model`)
# only exists inside the `let` statement
let model = cpu(model)
   # ...
   BSON.@save "./path/to/trained_model.bson" model
end

# is equivalent to the above, but uses `key=value` storing directive from BSON.jl
BSON.@save "./path/to/trained_model.bson" model = cpu(model)

The reason behind this is that models trained in the GPU but not transferred to the CPU memory scope will expect CuArrays as input. In other words, Flux models expect input data coming from the same kind device in which they were trained on.

In controlled scenarios in which the data fed to the loaded models is garanteed to be in the GPU there's no need to transfer them back to CPU memory scope, however in production environments, where artifacts are shared among different processes, equipments or configurations, there is no garantee that the CUDA.jl package will be available for the process performing inference on the model loaded from the disk.

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 blog post.

Using device objects

As a more convenient syntax, Flux allows the usage of GPU device objects which can be used to easily transfer models to GPUs (and defaulting to using the CPU if no GPU backend is available). This syntax has a few advantages including automatic selection of the GPU backend and type stability of data movement. To do this, the Flux.get_device function can be used.

Flux.get_device first checks for a GPU preference, and if possible returns a device for the preference backend. For instance, consider the following example, where we load the CUDA.jl package to use an NVIDIA GPU ("CUDA" is the default preference):

julia> using Flux, CUDA;

julia> device = Flux.get_device(; verbose=true)   # returns handle to an NVIDIA GPU
[ Info: Using backend set in preferences: CUDA.
(::Flux.FluxCUDADevice) (generic function with 1 method)

julia> device.deviceID      # check the id of the GPU
CuDevice(0): NVIDIA GeForce GTX 1650

julia> model = Dense(2 => 3);

julia> model.weight     # the model initially lives in CPU memory
3×2 Matrix{Float32}:
 -0.984794  -0.904345
  0.720379  -0.486398
  0.851011  -0.586942

julia> model = model |> device      # transfer model to the GPU
Dense(2 => 3)       # 9 parameters

julia> model.weight
3×2 CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}:
 -0.984794  -0.904345
  0.720379  -0.486398
  0.851011  -0.586942

The device preference can also be set via the Flux.gpu_backend! function. For instance, below we first set our device preference to "CPU":

julia> using Flux; Flux.gpu_backend!("CPU")
┌ Info: New GPU backend set: CPU.
└ Restart your Julia session for this change to take effect!

Then, after restarting the Julia session, Flux.get_device returns a handle to the "CPU":

julia> using Flux, CUDA;    # even if CUDA is loaded, we'll still get a CPU device

julia> device = Flux.get_device(; verbose=true)   # get a CPU device
[ Info: Using backend set in preferences: CPU.
(::Flux.FluxCPUDevice) (generic function with 1 method)

julia> model = Dense(2 => 3);

julia> model = model |> device
Dense(2 => 3)       # 9 parameters

julia> model.weight     # no change; model still lives on CPU
3×2 Matrix{Float32}:
 -0.942968   0.856258
  0.440009   0.714106
 -0.419192  -0.471838

Clearly, this means that the same code will work for any GPU backend and the CPU.

If the preference backend isn't available or isn't functional, then Flux.get_device looks for a CUDA, AMDGPU or Metal backend, and returns a corresponding device (if the backend is available and functional). Otherwise, a CPU device is returned. In the below example, the GPU preference is "CUDA":

julia> using Flux;      # preference is CUDA, but CUDA.jl not loaded

julia> device = Flux.get_device(; verbose=true)       # this will resort to automatic device selection
[ Info: Using backend set in preferences: CUDA.
┌ Warning: Trying to use backend: CUDA but it's trigger package is not loaded.
│ Please load the package and call this function again to respect the preferences backend.
└ @ Flux ~/fluxml/Flux.jl/src/functor.jl:637
[ Info: Using backend: CPU.
(::Flux.FluxCPUDevice) (generic function with 1 method)

For detailed information about how the backend is selected, check the documentation for Flux.get_device.

Data movement across GPU devices

Flux also supports getting handles to specific GPU devices, and transferring models from one GPU device to another GPU device from the same backend. Let's try it out for NVIDIA GPUs. First, we list all the available devices:

julia> using Flux, CUDA;

julia> CUDA.devices()
CUDA.DeviceIterator() for 3 devices:
0. GeForce RTX 2080 Ti
1. GeForce RTX 2080 Ti
2. TITAN X (Pascal)

Then, let's select the device with id 0:

julia> device0 = Flux.get_device("CUDA", 0)        # the currently supported values for backend are "CUDA" and "AMDGPU"
(::Flux.FluxCUDADevice) (generic function with 1 method)

Then, let's move a simple dense layer to the GPU represented by device0:

julia> dense_model = Dense(2 => 3)
Dense(2 => 3)       # 9 parameters

julia> dense_model = dense_model |> device0;

julia> dense_model.weight
3×2 CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}:
  0.695662   0.816299
 -0.204763  -0.10232
 -0.955829   0.538412

julia> CUDA.device(dense_model.weight)      # check the GPU to which dense_model is attached
CuDevice(0): GeForce RTX 2080 Ti

Next, we'll get a handle to the device with id 1, and move dense_model to that device:

julia> device1 = Flux.get_device("CUDA", 1)
(::Flux.FluxCUDADevice) (generic function with 1 method)

julia> dense_model = dense_model |> device1;    # don't directly print the model; see warning below

julia> CUDA.device(dense_model.weight)
CuDevice(1): GeForce RTX 2080 Ti

Due to a limitation in Metal.jl, currently this kind of data movement across devices is only supported for CUDA and AMDGPU backends.

Flux.AbstractDevice <: Function

Flux.FluxCPUDevice <: Flux.AbstractDevice

FluxCUDADevice <: AbstractDevice

FluxAMDGPUDevice <: AbstractDevice

FluxMetalDevice <: AbstractDevice

Flux.supported_devices()

julia> using Flux;

julia> Flux.supported_devices()
("CUDA", "AMDGPU", "Metal", "CPU")

Flux.get_device(; verbose=false)::Flux.AbstractDevice

julia> using Flux;

julia> model = Dense(2 => 3)
Dense(2 => 3)       # 9 parameters

julia> device = Flux.get_device(; verbose=true)       # this will just load the CPU device
[ Info: Using backend set in preferences: AMDGPU.
┌ Warning: Trying to use backend: AMDGPU but it's trigger package is not loaded.
│ Please load the package and call this function again to respect the preferences backend.
└ @ Flux ~/fluxml/Flux.jl/src/functor.jl:638
[ Info: Using backend: CPU.
(::Flux.FluxCPUDevice) (generic function with 1 method)

julia> model = model |> device
Dense(2 => 3)       # 9 parameters

julia> model.weight
3×2 Matrix{Float32}:
 -0.304362  -0.700477
 -0.861201   0.67825
 -0.176017   0.234188

julia> using Flux, CUDA;

julia> model = Dense(2 => 3)
Dense(2 => 3)       # 9 parameters

julia> device = Flux.get_device(; verbose=true)
[ Info: Using backend set in preferences: AMDGPU.
┌ Warning: Trying to use backend: AMDGPU but it's trigger package is not loaded.
│ Please load the package and call this function again to respect the preferences backend.
└ @ Flux ~/fluxml/Flux.jl/src/functor.jl:637
[ Info: Using backend: CUDA.
(::Flux.FluxCUDADevice) (generic function with 1 method)

julia> model = model |> device
Dense(2 => 3)       # 9 parameters

julia> model.weight
3×2 CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}:
  0.820013   0.527131
 -0.915589   0.549048
  0.290744  -0.0592499

Flux.get_device(backend::String, idx::Int = 0)::Flux.AbstractDevice

julia> using Flux, CUDA;

julia> CUDA.devices()
CUDA.DeviceIterator() for 3 devices:
0. GeForce RTX 2080 Ti
1. GeForce RTX 2080 Ti
2. TITAN X (Pascal)

julia> device0 = Flux.get_device("CUDA", 0)
(::Flux.FluxCUDADevice) (generic function with 1 method)

julia> device0.deviceID
CuDevice(0): GeForce RTX 2080 Ti

julia> device1 = Flux.get_device("CUDA", 1)
(::Flux.FluxCUDADevice) (generic function with 1 method)

julia> device1.deviceID
CuDevice(1): GeForce RTX 2080 Ti

julia> cpu_device = Flux.get_device("CPU")
(::Flux.FluxCPUDevice) (generic function with 1 method)

gpu_backend!(backend::String)