Considerations when setting up deep learning hardware
pyimagesearch.com
pyimagesearch.com
We built one recently, I got a big lump of dram (256GB), a 12 core processor 6 cards and a 2TB ssd on pci + some ssd's as a backend. We then mapped it onto HDFS, and our pipeline is from HDFS to the ssds for project work and then when the data's ready you can map it onto the pci disc for the actual run - hopefully the cpu has enough cores to run the discs and the GPUs at once.
Make sure you have enough PCI lanes. Motherboards might say they have x16 lanes for all four slots, but in practice it can drop down to as little as 4 each.
Unless something has changed recently, dual processor machines suffer a hit in speed when transferring data to and from GPU memory. However They might have fixed card affinity.
Each graphics card should be able to pull in at least 2 gigabytes a second. You therefore need to max out system ram (assuming your dataset changes that much....)
Power.
You need a PSU that is really up to it. Your wiring needs to be over specced.
Cooling.
4x 400 watts, plus 2x 150 watts for each proc, is a lot of power to dissipate. PSU efficiency really saves money
Where can I get one for next week?