PyTorch is a new deep learning framework that runs very well on the Jetson TX1 and TX2 boards. It is relatively simple and quick to install. Unlike TensorFlow, it requires no external swap partition to build on the TX1.

Although the TX2 has an ample 32 GB of eMMC, the TX1 has only half that, and it is easy to run out of space due to cruft from JetPack, Ubuntu packages, and installation artifacts. The cleanup section below lists ways to slim things down, and the steps here lean in the direction of minimalism.

The PyTorch developers recommend the Anaconda distribution. I was unable to find a recent Anaconda setup for ARM64, so I used the global python libraries.

Tip: On the TX2, running ~/jetson_clocks.sh throttles up the CPUs and enables two more cores. This reduces the PyTorch compilation time from 45 to 37 minutes. I didn't test on the TX1, but would expect a less dramatic speedup.

To avoid issues with system-wide installation as superuser, I appended --user to all pip3 install commands below. This puts packages in $HOME/.local/lib/python3.5/site-packages, which I added to my PYTHONPATH.

• sudo apt install libopenblas-dev libatlas-dev liblapack-dev
• sudo apt install liblapacke-dev checkinstall # For OpenCV
• pip3 install numpy scipy # ~20-30 min

• pip3 install pyyaml
• pip3 install scikit-build
• sudo apt install ninja-build

Check cmake --version. It looks like cmake >= 3.6 is required for the python bindings used in PyTorch. I followed http://askubuntu.com/a/865294 (and used --no-check-certificate) to get 3.7.2. The cmake executable was installed to /usr/local/bin. Make sure cmake --version reports the new version after installation.

• sudo apt install python3-dev
• sudo apt install libffi-dev
• pip3 install cffi

I followed a subset of these excellent instructions for Python 3 from the pyimagesearch blog. I skipped CUDA and OpenCL integration, and went system-wide with the Python 3 bindings (no virtualenvs). Under the OpenCV source directory, I created build/ and ran this:

cmake \
    -D ENABLE_PRECOMPILED_HEADERS=OFF \
    -D WITH_OPENCL=OFF \
    -D WITH_CUDA=OFF \
    -D WITH_CUFFT=OFF \
    -D WITH_CUBLAS=OFF \
    -D CMAKE_BUILD_TYPE=RELEASE \
    -D CMAKE_INSTALL_PREFIX=/usr/local \
    -D INSTALL_PYTHON_EXAMPLES=OFF \
    -D INSTALL_C_EXAMPLES=OFF \
    -D PYTHON_EXECUTABLE=/usr/bin/python3.5 \
    -D BUILD_EXAMPLES=OFF ..

The first option significantly reduces the storage requirements during the build, at the expense of a slightly longer build time. Before discovering this option, I encountered errors like "failed to copy PCH file" on the TX1.

With this setup, OpenCV builds quickly--19 minutes on the TX2.

The Jetson does not ship ready to run deep learning models on the GPU. Unfortunately CUDA and cuDNN cannot simply be downloaded directly as for x86 systems; NVIDIA directs us to the JetPack SDK, which currently runs only on Ubuntu 14.04. In a VirtualBox instance, I downloaded the JetPack .run file. The default full installation is massive and takes hours to download, build, and flash. Moreover, it copies many GB of files to the local VM which is not what I want! I realized I could avoid the overkill and just select the CUDA and cuDNN packages, then cancel the installation once the .deb files are downloaded. Only the following 4 files are needed to install CUDA and cuDNN:

1. cuda-repo-l4t-8-0-local_8.0.64-1_arm64.deb
2. libcudnn5_5.1.10-1+cuda8.0_arm64.deb
3. libcudnn5-dev_5.1.10-1+cuda8.0_arm64.deb
4. cuda-l4t.sh

I created ~/cuda-l4t on the Jetson and copied these 4 files there.

• sudo ./cuda-l4t.sh cuda-repo...arm64.deb 8.0 8-0 to install the CUDA libs. Note that this script prepends CUDA directories to PATH and LD_LIBRARY_PATH, then slaps the hard-coded redefinitions onto ~/.bashrc. If it matters to you, now is a good time to tidy this up.
• To get cuDNN, do sudo dpkg -i <debfile> for files 2 and 3 above. Then do

sudo apt install -f

• Check: nvcc -V
• ldconfig -p | grep cu and grep dnn can be used to show the library locations. I also see /usr/include/cudnn.h

Since tools/setup_helpers/cuda.py assumes /usr/local/cuda, CUDA_HOME need not be set. But the TX1/TX2 install locations are missing from the search list in tools/setup_helpers/cudnn.py. In that script, there is a check for os.getenv(CUDNN_LIB_DIR) and another for the include dir. So I added the following to ~/.profile:

export CUDNN_LIB_DIR=/usr/lib/aarch64-linux-gnu
export CUDNN_INCLUDE_DIR=/usr/include

Note: Echoing at the prompt is not sufficient to test if environment variables are visible to the PyTorch build setup scripts. If CUDNN_LIB_DIR is properly set for the bash environment, but you also see this:

[~]$ sudo python3 -c 'import os; print(os.getenv("CUDNN_LIB_DIR"))'
None

then cuDNN will fail to be included. Here are three options:

• Skip the problem and install PyTorch locally with the --user option (see next section).
• use sudo -E to make user environment variables available to root.
• Export from ~/.profile, then log out/in.

git clone https://github.com/pytorch/pytorch.git
git checkout -b v0.1.10 v0.1.10

Now build PyTorch:

time python3 setup.py install --user

For a system-wide install,

sudo time python3 setup.py install # or sudo -E

Test out in the python3 repl (outside pytorch direcory):

import torch
torch.backends.cudnn.is_acceptable(torch.cuda.FloatTensor(1))

If True, congratulations!

pip3 install torchvision

The postFlashTX1 repo contains some useful cleanup scripts. In addition:

sudo apt clean
sudo apt autoremove --purge
sudo rm /usr/src/*.tbz2 ## I had 6.9 GB of zip files
sudo rm /var/cuda-repo-8.0-local/*.deb
rm ~/temp # From my CMake 3.7 install

The OpenCV sources can also be removed if necessary.