Hey all! I wanted to send out a bit of important information relating to the Dagger Workshop this year, to make sure anyone who plans to attend has a maximally enjoyable session 
Primarily, please have an up-to-date version of Julia 1.11 installed (preferably using juliaup) - this is required for running some examples with threads+distributed computing. If you use an earlier version of Julia (minimum 1.8), you may just get errors for certain examples (but you can still follow along). You are welcome to use a Jupyter kernel with multiple Julia threads - you can do using IJulia; IJulia.installkernel("Julia", "--threads=6") to create a kernel specification with 6 Julia threads (feel free to adapt this number to however many CPU cores your system has).
If you plan to participate in GPU exercises (they are optional, and I’ll keep the exercises somewhat brief in case some people don’t have GPUs):
- If using an M-Series Mac, please update to MacOS 13 or greater, and make sure you can run some basic code from Metal.jl (
using Metal; sum(MtlArray(rand(Float32, 4, 4)))) - If using a machine with an NVIDIA GPU, make sure that you can run basic code with CUDA.jl (
using CUDA; sum(CuArray(rand(4, 4)))) - If using a machine with an AMD GPU, make sure that you can run basic code with AMDGPU.jl (
using AMDGPU; sum(ROCArray(rand(4, 4)))) - If using a machine with an Intel GPU, make sure that you can run basic code with oneAPI.jl (
using oneAPI; sum(oneArray(rand(Float32, 4, 4))))
That’s it for now! I’ll update this post with further information as it becomes available. I’m looking forward to seeing y’all at JuliaCon in one week!
