Linux

47337 readers

868 users here now

From Wikipedia, the free encyclopedia

Linux is a family of open source Unix-like operating systems based on the Linux kernel, an operating system kernel first released on September 17, 1991 by Linus Torvalds. Linux is typically packaged in a Linux distribution (or distro for short).

Distributions include the Linux kernel and supporting system software and libraries, many of which are provided by the GNU Project. Many Linux distributions use the word "Linux" in their name, but the Free Software Foundation uses the name GNU/Linux to emphasize the importance of GNU software, causing some controversy.

Rules

Posts must be relevant to operating systems running the Linux kernel. GNU/Linux or otherwise.
No misinformation
No NSFW content
No hate speech, bigotry, etc

Related Communities

Community icon by Alpár-Etele Méder, licensed under CC BY 3.0

founded 5 years ago

MODERATORS

AgreeableLandscape@lemmy.ml

nooter692@lemmy.ml

MarcellusDrum@lemmy.ml

cypherpunks@lemmy.ml

cyclohexane@lemmy.ml

d3Xt3r@lemmy.nz

Using an AMD GPU for NN training/inference? (lemmy.world)

submitted 1 year ago* (last edited 1 year ago) by leakybits@lemmy.world to c/linux@lemmy.ml

17 comments fedilink hide all child comments

I'm looking to buy a new GPU. My main use case will be training and running neural nets (tensorflow+pytorch); gaming isn't really a priority.

Thing is, I use wayland (via sway), and so I'd really prefer to get an AMD GPU. Nvidia doesn't seem very linux friendly at the moment, especially when it comes to wayland unfortunately.

On the other hand, Nvidia seems to be the clear frontrunner right now when it comes to NN acceleration. I'm worried that if I got an AMD GPU to accelerate my NN work, I'd just be wasting my money.

What do you all think?

Edit: I've used GPUs to accelerate NN models in the past, but they weren't my own, they were provided by my uni's research infra and/or google collab. So this would be the first time I'd be using my own GPU hardware for this purpose.

you are viewing a single comment's thread
view the rest of the comments

[–] empireOfLove@lemmy.one 2 points 1 year ago* (last edited 1 year ago)

the unfortunate truth is that NVidia has a complete stranglehold on the compute market. They recognized the capabilities of massively parallel compute early on and pushed CUDA super super hard to any organization doing compute. And it worked- CUDA is much easier to implement than openCL, and was released two years earlier too, so everyone ended up standardizing on it. They are currently reaping the benefits of that monopolization through their now huge enterprise GPGPU market and can basically piss down the backs of consumers and competitors alike without repercussions. OpenCL and AMD's implementation was a day late and harder to implement...

Do not buy AMD if you need to do any kind of compute- whether it be rendering ala Blender/AE, accelerated engineering CAD workflows, or big data handling. No tools are designed around anything but CUDA, and it sucks because Jensen is a greedy asshole, but you gotta pay your dues.