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Comment by timzaman | Hacker News Reader

Get a decent NVIDIA GPU. Then install PyTorch and off you go. I advise to make all your own tooling, as you likely have a specific use-case, and so your tooling can be tailored to that. Most ML tools are very generic, or so simply you might as well do them yourself. The advantage if having your own box, is that (1) you'll learn some systems skills building it and (2) since you invested in it, you should feel obligated to use it! Good luck.

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I would say the exact same thing if the circumstances weren’t so dire, but an NVIDIA GPU is so expensive nowadays that it might be a bit better to use rented services (like the paid version of Google Colab) for now, if you have any monetary constraints.

Maybe GPU prices will stabilize after Ethereum switches to POS and manufacturing pipelines get back to normal, but then I’m not that sure after seeing US trying to go ham with sanctions all over the place.

I have a 3090 for serious (hobby) work and a 1070 from 4ish years back next to my bed. I think getting anything 1070 or better is good enough at the beginner level (training baby datasets/models from scratch such as MNIST/CIFAR, transfer learning the big models). I just don't understand the cost argument .. you can get this stuff used. Main thing is you need CUDA.

The 3090 machine gets about the same use as the 1070 in my case. While it is nice to have more GPU memory to have huge batches and train things faster, this is a quality of life improvement/bragging to be honest. Serious work in some sub-areas needs multi-GPUs or enterprise grade hardware (e.g. A100s).

Software-wise, I just use Pytorch/Pytorch lignting/keras, and anaconda.

Edit: I used to build my own machines in my younger days. The two machines I spoke of above are alienware. Got them on black friday sales. Cost-wise, they were ridiculously cheap for the power they give/impact on my career.

The problem is that even a 1070 is ridiculously expensive these days (About $400 on Newegg, that was the cost of a RTX 2070 a few years ago!) If you can get an used GPU from a friend that would be great, but other than that you’re going to have to shell hundreds of dollars for an old GPU that you will probably have to upgrade soon.

I don’t know the OP’s financial situation, but if you’re a poor student than these things certainly matter.

Good points .. however, my advice for the cash strapped students on HN: Buying 1 good machine will last you 4+ years easily these days. Buying a system like Alienware with a 3070-3080 was under 2K Canadian over the recent Black Friday shopping event. Over 4 years, that is $500. If you are studying CS, it is absolutely worth buying a decent machine. You don't need a top of the line machine but you need to be able to study your craft on something more powerful than a rasperry pi or junky old machine.

I was quite poor growing up, and I recall buying a 3K machine when I started undergrad (that was crap hardware by today's standard). And i have no doubt that having this machine helped me get my first job, and things got better from then. If you are in CS, think of it as an investment, and make it pay off!

Btw, I am trying to be positive .. pls don't construe anything here as negative. I appreciate that money is tight for a lot of folks. Paying 10% interest to buy a 3K machine is not a good idea!! I just skimped a lot as a student, and some of it was quite pointless. I wish someone explained this to me, and hence my comment.

If you are in a situation where you'd have to take a loan to buy a system, pls don't feel like you need a GPU to do anything useful. I am certain one can make do with just colab and a web browser. Good luck to all the students out there!! Life is hard at that stage .. it gets far easier once you have a paying job in the field.

You do not necessarily need a high performance GPU to step into ML. I'm running a couple of my hobby projects either on my notebook or an old discarded former Server hardware with onboard GPU.

+1 on that. I had a Mac Pro 5.1 that was surplus to requirements. Its 10 years old with 64GB of Ram and 2 x 6core processors. I stripped off the OS and installed Ubuntu along with an nVidia 750 - $100. For learning purposes and running hyper-parameter tuning it is pretty robust.

And you can run your own github/gitlab runner on the GPU box and set up CI in your projects. It's good practice (and good practise). And it's free. This unchains you from your PC so you can push code from anywhere and it'll crunch the numbers for you. You just need to install docker, run the runner and make sure you're using a runner container with the NVIDIA stuff installed and you pass through the GPU to the container.

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