typesend
I recently ordered a Lambda Vector Workstation for machine/deep learning experiments as well as blender-related video rendering.
- Four RTX A4000 GPUs (without NV Link)
- AMD Threadripper 3960X: 24 cores, 3.80 GHz, 128 MB cache
- 256 GB RAM
- ASRock TRX40 Creator motherboard
- Ubuntu preinstalled
Deeply appreciate any advice you can share that could help me make the most of this hardware using Elixir and avoid any gotchas or suboptimal performance!
Should I build the BEAM from source? Any special configuration details or build flags I should pay special attention? I’ve always had difficulting getting jinterface and wxWidgets set up in the past but I should probably get those right this time.
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garazdawi
If you want the best performance then you want to pass the
-march=nativeflag to gcc, this means building from source. I would also throw in the--enable-jitflag to make sure that you get the jit (if you don’t give the flag, then configure will silently select the non-jit if the correct tools to build the jit are not available)../configure --enable-jit CFLAGS="-O2 -g -march=native" && make && make installbdarla
(Side question)
Is your plan to use the Nx framework for ML/DL or to use Elixir for orchestrating execution in other languages/frameworks e.g. in Python?
typesend
Good question. Both!
I’m new to this and the vast majority of learning materials are Python-based, but of course I also want to have a runtime environment that smoothly takes advantage of all cores and is more resilient.
The BEAM will be quite helpful in scheduling/queuing jobs and keeping track of benchmark stats.
Baby steps—but someday I hope to be able to help move Elixir-based ML tooling forward. There’s no good reason Python should have a complete monopoly over the ML world, especially when you consider the Python code isn’t really doing the heavy lifting.