3 ms·

Do you recommend using rented GPU instances, or building a some kind of Nvidia rig?

cr0sh 9y ago
I'd personally say it depends on your budget, what you want to do, and the amount of data you plan to process. It may also depend on how comfortable you are setting up your own rig, if you go that route. For instance, last year I completed Udacity's "Self-Driving Car Engineer Nanodegree" course. Large parts of the course needed us to train neural nets (tensorflow and similar) on data we either generated or downloaded. We were provided the option to use AWS instances for training (free credits), but I opted to use my local box. It took me about a day or so of playing around before I finally got everything working properly (CUDA, etc) with my NVidia 750ti GPU. This is a very low-end GPU, but it honestly performed quite well for the course. It could only handle a limited amount of data, and sometimes training cycles took a while for turnaround (depending on the task), but it ran the resulting models quite easily (while still handling the 3D rendering tasks of the vehicle simulator). For learning purposes, it will all depend - if you already have a machine with a decent GPU, CPU and RAM (say something equivalent to the 750 or better, 4 cores or more, and say 8 gig of RAM), it might make sense to try to do things locally - if you think your skills are up to the configuration challenges (I got my system working, but I ended up breaking Ubuntu's update system, because I'm on 14.04 LTS, and I had to hand-install many things to fix dependencies and such for Python, Tensorflow, C/C++, etc - in order to complete the course). However, if you are planning on processing a huge amount of data for a large model, but aren't planning on doing this constantly - then an AWS instance might be a better option, as a custom rig for this kind of thing I'd imagine would be a bear to spec out and configure - not to mention cost. I'm certainly not an expert on all of this, though...ymmv.

I'd personally say it depends on your budget, what you want to do, and the amount of data you plan to process. It may also depend on how comfortable you are setting up your own rig, if you go that route. For instance, last year I completed Udacity's "Self-Driving Car Engineer Nanodegree" course. Large parts of the course needed us to train neural nets (tensorflow and similar) on data we either generated or downloaded. We were provided the option to use AWS instances for training (free credits), but I opted to use my local box. It took me about a day or so of playing around before I finally got everything working properly (CUDA, etc) with my NVidia 750ti GPU. This is a very low-end GPU, but it honestly performed quite well for the course. It could only handle a limited amount of data, and sometimes training cycles took a while for turnaround (depending on the task), but it ran the resulting models quite easily (while still handling the 3D rendering tasks of the vehicle simulator). For learning purposes, it will all depend - if you already have a machine with a decent GPU, CPU and RAM (say something equivalent to the 750 or better, 4 cores or more, and say 8 gig of RAM), it might make sense to try to do things locally - if you think your skills are up to the configuration challenges (I got my system working, but I ended up breaking Ubuntu's update system, because I'm on 14.04 LTS, and I had to hand-install many things to fix dependencies and such for Python, Tensorflow, C/C++, etc - in order to complete the course). However, if you are planning on processing a huge amount of data for a large model, but aren't planning on doing this constantly - then an AWS instance might be a better option, as a custom rig for this kind of thing I'd imagine would be a bear to spec out and configure - not to mention cost. I'm certainly not an expert on all of this, though...ymmv.