My Jetson Nano is only giving Tensorflow 459MB or GPU RAM to work with. And this is because I closed down other applications - when I was running chrome, then the GPU RAM available to Tensorflow was less than 200MB, causing the benchmark to fail.
How can I create a fixed RAM allocation to the GPU of, say, 1 or 1.5 GB?
Also any way to increase performance? 1.7 on Resnet seems very slow. Perhaps more ram with a bigger batch size would help? I had to decrease the batch size to 1 from default of 32. (Also please keep in mind that if you replicate this you need to checkout the 1.13 branch of tensorflow benchmark, and use Python 3).
But on that benchmark it’s actually training resnet as far as I know. Will the above still work and, what do those two options allow_growth and gput_memory_fraction actually mean?
I have another question. If I add a swap file, can this also be used by the GPU? I assume it can since the GPU uses main memory? Obviously would be slow but perhaps I could configure the system so that tensorflow or python never swaps?