I am working on a project that aims to solve home inventory evaluations (that is to say what is your stuff worth helping with filing homeowners insurance claims, estate planning, etc.) in part with a neural net.
Taking the initiative, a colleague and I are going to build our own servers since AWS has been a real stumbling block. My coworker (assuming this is the same buddy you refer to in the previous sentence? If so, carry whatever language you initially use to describe him to be consistent) is taking a different class and combined we are going to have the following stack
AWS is very over-priced for GPUs. You can buy a GTX 1070 for around $300 that gives better performance than the AWS P2’s GPU. So I think it’s a good idea to build your own deep learning machine if you can.
It is definitely possible to put together a home system – mine is Ubuntu based. I log into it remotely, interacting with jupyter from another machine to minimize resource usage on the GPU server.
NVidia has a GPU grant program for academics – not students, but PIs ( https://developer.nvidia.com/academic_gpu_seeding). I’m not eligible, but I’m working on inspiring a PI to write a grant and let me put together a machine for them. Will keep you posted.
The pain with aws was getting registered (still not after a long support chain), followed by the constant vigilance of turning the machine off.
It only makes moderate economic sense. At some level this a gym membership where I have to promise myself that I use the server quite a bit to justify - basically a few months worth. There isn’t that much difference between weight training and training weights from a cash perspective.
The coworker is in the udacity program and is pretty excited about it.
@lin.crampton frankly I’m not sure that program is worth it at the moment, since they don’t give out Pascal-based cards; also, all they provide is the card, not the rest of the server. Pascal cards are such a big step in performance, and (if you get a 1070 or even a 1080) not a huge chunk of the cost of the server - so I’m not sure how much benefit it provides to get their grant…
That sounds reasonable. You could compare runtimes / memory issues with our AWS GPUs since we know those are working for most use cases in this course.
Setting up your own machine is really easy. Even a 1060 (sub $200) will blow the doors of AWS P2 instance and will be no monthly costs. The 1070 is the sweet spot but will go for around $400. I highly recommend using Linux as Windows is always fighting against the grain when installing dependencies and linux just works faster and smoother. Especially Jupyter notebooks, the difference is huge.
Consensus is that the CPU doesn’t need to be that powerful, as long as you can support enough processes per GPU (and depending on what kind of preprocessing you need to do).
I’ve seen the Intel 6700 in a bunch of builds so that seems reasonable (and you could probably get by with less if necessary).