Merge #1633

1633: Add section on Checking GPU Availability r=CarloLucibello a=logankilpatrick Follow up from this question I posted: https://stackoverflow.com/questions/68142837/how-to-check-if-flux-jl-is-using-the-gpu/68144877#68144877 which was inspired by the top PyTorch question on SO: https://stackoverflow.com/questions/48152674/how-to-check-if-pytorch-is-using-the-gpu Co-authored-by: Logan Kilpatrick <[email protected]>
FluxML · Aug 30, 2021 · 7ada01c · 7ada01c
2 parents cff8f03 + b51f9ac
commit 7ada01c
Showing 1 changed file with 11 additions and 0 deletions.
diff --git a/docs/src/gpu.md b/docs/src/gpu.md
@@ -2,6 +2,17 @@
 
 NVIDIA GPU support should work out of the box on systems with CUDA and CUDNN installed. For more details see the [CUDA](https://github.com/JuliaGPU/CUDA.jl) 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
+julia> using CUDA
+
+julia> CUDA.functional()
+true
+```
+
 ## GPU Usage
 
 Support for array operations on other hardware backends, like GPUs, is provided by external packages like [CUDA](https://github.com/JuliaGPU/CUDA.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.