Building from source, and I can’t get Jetson TX2, an ARM64, to compile properly.
I follow the reccommendations from nVidia’s forums here:

  
      gist.github.com
  
  
    https://gist.github.com/dusty-nv/ef2b372301c00c0a9d3203e42fd83426 109
pytorch_jetson_install.sh
#!/bin/bash
#
# pyTorch install script for NVIDIA Jetson TX1/TX2,
# from a fresh flashing of JetPack 2.3.1 / JetPack 3.0 / JetPack 3.1
#
# for the full source, see jetson-reinforcement repo:
#   https://github.com/dusty-nv/jetson-reinforcement/blob/master/CMakePreBuild.sh
#
# note:  pyTorch documentation calls for use of Anaconda,
#        however Anaconda isn't available for aarch64.
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I can get it to compile on Python2.7, but not Python3. I had to disable NCCL support, which is desktop only CUDA, and that got me to about 27% of the build complete. You can see the nVidia conversation here: https://devtalk.nvidia.com/default/topic/1042821/pytorch-install-broken/?offset=7#5291321 52
For now, Pytorch keeps dying at the same spot when compiling Onnx and onnx-tesnsorrt, obviously both of which would be required to deploy for production inferencing on the TX2. Latest errors are:
third_party/onnx-tensorrt/CMakeFiles/nvonnxparser.dir/build.make:134: recipe for target ‘third_party/onnx-tensorrt/CMakeFiles/nvonnxparser.dir/onnx2trt_utils.cpp.o’ failed
make[2]: *** [third_party/onnx-tensorrt/CMakeFiles/nvonnxparser.dir/onnx2trt_utils.cpp.o] Error 1
CMakeFiles/Makefile2:1488: recipe for target ‘third_party/onnx-tensorrt/CMakeFiles/nvonnxparser.dir/all’ failed
make[1]: *** [third_party/onnx-tensorrt/CMakeFiles/nvonnxparser.dir/all] Error 2
[ 27%] Built target python_copy_files
Makefile:160: recipe for target ‘all’ failed
make: *** [all] Error 2
Failed to run ‘bash …/tools/build_pytorch_libs.sh --use-cuda --use-nnpack caffe2 libshm gloo c10d THD’
I’d be open to not compiling it all, and simply using an Onnx exported model, but I have no idea how that would fit into my Python production code. After all, the model is only a  part of the PyTorch code, I still use Torch for translating image formats, evaluations, etc.
Not sure where to go from here, but I’d really like to stick with this library and figure out the inferencing for edge devices. Combined with fast.AI’s recent v1.0 release, there’s some pretty amazing work that can be done.