Hi @r3krut,
This category is for Glow, which is a different PyTorch backend from Caffe2 (which "natively integrates QNNPACK" 69). Glow primarily targets neural network accelerators, though it does have a CPU backend and supports automatic profiling + quantization. If you want to use QNNPACK, I believe all you need to do is make sure your model (e.g. predict_net) is using operators such as Int8Conv, Int8FC, etc. and your Caffe2 model would use it.
However, if your network is not quantized and/or you don’t want to install Caffe2 on your Raspberry Pi, you could use try to use Glow to profile your model, quantize it, and then save what we call an ahead-of-time compiled “bundle”, which is just a binary to copy to your Raspberry Pi3 to run (see docs here 64 and here 31). Note that it may not perform as well as QNNPACK; we are more focused on accelerator backends right now.
Thanks,
Jordan