Is GPU passthrough to WSL/docker working smoothly now?

Nvidia drivers for "simply" compute workloads are really good, just look at all the server with Nvidia GPUs. The problem is when you need to output an image to a screen and deal with Wayland or X11

Standard driver + cuda. Get yourself a docker container and your good to go 😊 this is probably the easiest way to do it

In my dept a mix of window/Linux. CUDA-GPU fitted machines, mostly TensorFlow /Python. No significant issues with either. But that is, in CS terms quite a limited remit.

Macs for deep-learning?

Maybe it was a pain years ago, I don't really know but nowadays you just click install nVidia drivers in the software management and it works. There's nothing painful or difficult about it.

I'll extend this: for the majority of real computer science/ engineering work (especially back-end stuff), *nix is the way to go. It's a huge reason Macs are the standard for CS folks (ever since the terminal became part of the MacOS). At every university I attended and taught, and at every company I've worked for, linux/macs have been used by essentially 100% of the people. Roughly once a year I'll hear/see someone using Windows.

I always found this weird. Getting Nvidia drivers to work on Linux is a big pain, yet most ML people use it. I use windows and WSL if I have to. I never go into my Linux dual boot.

yes

Windows + WSL with Ubuntu is great

Most DL hardware accelerator libraries are built and tested to run on servers which are more than likely Linux machines so yeah Linux would be the way to go.

I dual boot. I’m sure you could get things to work on Windows but it would be a horrible experience.

Yes

Write a custom function, and use it as a metric? Not sure what you mean by "What the training method says", but I think default metrics get summed just like losses.

I would rather use my own rmse this one does some weighting and it just does not make sense to me.

I dont care about the loss, i care about the rmse metric. When i calculate it for each output it just does not add out to what the training method says. 🫤

Thank you!

It may be worth talking to a specialised PC shop, you may also need to check your house electrics will support that kind of setup. You may need an electrocution to come in to do some work for you.

https://www.pugetsystems.com/labs/articles/1-7x-nvidia-geforce-rtx-4090-gpu-scaling/

Puget has some info on power use that may be of interest, that link is for 1-7 RTX 4090's scaling. They may also be worth talking to if you want some one to make your setup for you.

Thanks for your input. Yes, it's a home project, but I'd possibly start using it for commercial in the future. Otherwise why do you think 3090s would give me a better ROI?

I think you might find more info on such a rig, if you search about building a "mining rig", it is quite the same. I have seen they have used multiple 1200 W server PSU's connected together with interface board.

> power spikes can cause each of them to go up to 1000W

:O that's quite a lot, is this for a commercial application or home project, otherwise you might be able to find 4 used 3090s with better ROI

You can specify several losses, or have multi-output with a single loss - in both cases Keras will average them out (I think its non-weighted by default, and you can specify the weights, but I don't remember 100%).

You can't really have 3 different loss values for a single network - otherwise it won't know how to use that to backpropagate. The best you can do is write a custom loss function, and mix them in a way that makes sense for your problem (You will still need to provide a singular value at the end), or provide the weights (You'd need to look up APIs docs for that).

You'll probably have to look hard, as a power supply needed to do this, its installation and modification for computer components is anywhere from 2-5k$ alone. Server components cost way more than PC components. Good luck.

Ah okay, thanks for the input. Yes, my goal is to build a $10-15k server, so looking if someone knows here how to buikd such machines

The solution for 2 power supplies refers to the 2 computers solution. For a singular 4kW power supply you'll need to go beyond consumer products, into industrial power supplies for high performance servers or supercomputers. At that point you're no longer building a PC, and I don't know how you'd handle it, sorry. IMO it's just better to build 2 machines.

I've read that 2 PSUs is not recommended. What do you think?