Can flux handle multiple gpus

When working with Julia, it is important to know if the Flux library can handle multiple GPUs. In this article, we will explore three different ways to solve this question.

Option 1: Using the CUDA.jl Package

The first option is to use the CUDA.jl package, which provides support for GPU computing in Julia. To check if Flux can handle multiple GPUs, we can use the CUDA.functional() function. This function returns a boolean value indicating whether multiple GPUs are supported.

using CUDA
using Flux

if CUDA.functional()
    println("Flux can handle multiple GPUs.")
else
    println("Flux does not support multiple GPUs.")
end

Option 2: Checking the Number of Available GPUs

Another way to determine if Flux can handle multiple GPUs is by checking the number of available GPUs on the system. We can use the CUDA.device_count() function from the CUDA.jl package to get the number of available GPUs. If the count is greater than 1, it means that Flux can handle multiple GPUs.

using CUDA
using Flux

gpu_count = CUDA.device_count()

if gpu_count > 1
    println("Flux can handle multiple GPUs.")
else
    println("Flux does not support multiple GPUs.")
end

Option 3: Checking the Backend

Lastly, we can check the backend used by Flux to determine if it can handle multiple GPUs. The Flux.backend() function returns the current backend being used. If the backend is CUDA, it means that Flux can handle multiple GPUs.

using Flux

backend = Flux.backend()

if backend == Flux.CUDA
    println("Flux can handle multiple GPUs.")
else
    println("Flux does not support multiple GPUs.")
end

After exploring these three options, it is clear that the best option to determine if Flux can handle multiple GPUs is Option 1: Using the CUDA.jl Package. This option directly checks if the CUDA functionality is available, providing a more reliable and accurate result.