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Originally published on Medium.

Building PyTorch from source becomes necessary when using Python 3.8 on JetPack 4.6.3, since Nvidia doesn’t provide pre-built wheels for this configuration. Downgrading to Python 3.6 was possible, but ROS Foxy requires Python 3.8, which made building from source the only viable option.

Setup specifications

• Python 3.8
• PyTorch 1.10.2
• Jetson Xavier with JetPack 4.6.3 and L4T 32.7.3
• CUDA 10.2

Build steps

Clone and checkout:

git clone --recursive https://github.com/pytorch/pytorch
cd pytorch
git checkout tags/v1.10.2
git submodule sync
git submodule update --init --recursive --jobs 0

Maximize performance:

sudo nvpmodel -m 0
sudo jetson_clocks

Configure the build environment:

export USE_NCCL=1
export USE_DISTRIBUTED=1
export USE_QNNPACK=0
export USE_PYTORCH_QNNPACK=0
export TORCH_CUDA_ARCH_LIST="7.2"
export PYTORCH_BUILD_VERSION=1.10.2
export PYTORCH_BUILD_NUMBER=1

Install dependencies:

sudo apt-get install cmake libopenblas-dev libopenmpi-dev
python3 -m pip install -r requirements.txt
python3 -m pip install scikit-build ninja
python3 -m pip install setuptools==59.5.0

Set the compiler version:

sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-8 1
sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-8 1

Build and install:

python3 setup.py bdist_wheel
cd dist
python3 -m pip install --no-cache <wheel_name>
python3 -c "import torch; torch.cuda.is_available()"

Checking the CUDA architecture

To verify CUDA capability using the deviceQuery utility:

1. Navigate to /usr/local/cuda/samples/1_Utilities/deviceQuery
2. Run sudo make to build
3. Run ./deviceQuery to display the specifications

Jetson Xavier should report compute capability 7.2.