Hacker News Reader

Comment by mikealche | Hacker News Reader

Hey, so I know that 0.90 isn't much... But is there a difference between running the programs on AWS than on my local computer with my mediocre video card? Will they take much longer to trian? I honestly don't know the difference so I'm asking

View on HN

Paperspace (https://www.paperspace.com/ml) have ML instances starting at $0.27 per hour. Their more general GPU offering is $0.60 per hour for an 8-core machine with an 8GB GPU. See https://news.ycombinator.com/item?id=13185464 for previous discussion.

Your mediocre graphics card may or may not have CUDA support. If it doesn't, or it only supports an old version, then it's the same as not having a graphics card at all. And of course, Nvidia only.

If it supported, then the major difference is GPU memory, which limits the size of the network you can train. The newest models are faster than some 1-2 year old ones, but older hardware does the job fine.

In addition to this excellent advice, I'll just add that my experience is that a 2GB CUDA compatible card is (just) enough to complete this course. Although 4GB makes life much easier

Any recommendations for the cheapest (but not "penny wise, pound foolish") HW setup that meets these requirements for course completion?

This is what I have and it's been holding up great for everything I've wanted to do till now. Core i3 6100, Nvidia GTX 950 2GB, 8GB DDR4 RAM. Did not try running a neural net on the GPU, but it should do the job. The whole thing - http://lonesword.in/hardware/2015/11/10/Assembled-a-computer... - including the cheap table and chair cost me around 60K indian rupees which roughly translates to 1000 USD - but that's because computer components are around 30-40% more expensive in India than in the US.

A used Dell workstation off eBay plus a GTX 1070 will get you exceptional performance for under $1000. If you want to spend less, a used GTX 980 is also a good option.

In same boat here. Early this year I put together a PC with GTX 750 TI 2GB.

What do you think of this? Will this work fine?

I just set up CUDA and cudnn for python3 and tensorflow last night on my machine - with that same card. That card should have 640 cuda cores; every little bit counts, imho. I'm not really sure what the difference will be compared to what I was doing before, but anything has to be better than the quad-core cpu I'm currently using (some older AMD thing).

Reply on news.ycombinator.com