In the previous article, how to boot the Jetson Orin Nano from NVMe storage was explained. In this article, the PyTorch installation and running Yolov8 is described. If the setup has not been done yet, refer to the previous article to set it up in advance.
Introducing CUDA
Introduce CUDA
.
sudo apt update
sudo apt install -y cuda libcudnn8
Code language: Bash (bash)
Introducing other packages
Install other packages required to run Python
.
sudo apt install -y python3-pip python3-venv libopenblas-dev
Code language: Bash (bash)
To install PyTorch
and other libraries in a venv
environment, clone the Yolov8
repository and create a venv
virtual environment under it.
git clone https://github.com/ultralytics/ultralytics.git
cd ultralytics
python3 -m venv venv
source venv/bin/activate
pip install -U pip
Code language: Bash (bash)
Introducing PyTorch
Since we are using Jetson Linux 36.2 (=JetPack 6.0 DP (=Developer Preview)), access the https://developer.download.nvidia.cn/compute/redist/jp/v60dp/pytorch directory with a browser and check for the latest packages.

The latest package at this time was torch-2.2.0a0+81ea7a4.nv24.01-cp310-cp310-linux_aarch64.whl
.
wget https://developer.download.nvidia.cn/compute/redist/jp/v60dp/pytorch/torch-2.2.0a0+81ea7a4.nv24.01-cp310-cp310-linux_aarch64.whl
Code language: Bash (bash)
Install the downloaded package, as well as numpy
.
pip install ./torch-2.2.0a0+81ea7a4.nv24.01-cp310-cp310-linux_aarch64.whl
pip install numpy
Code language: Bash (bash)
Check PyTorch
is working once. It is successful if you see True and pytorch’s version as shown below.
$ python3 -c "import torch;print(torch.cuda.is_available(), torch.__version__)"
True 2.2.0a0+81ea7a4
Code language: Bash (bash)
Introducing PyTorchVision
Install PyTorchVision
by referring to the https://forums.developer.nvidia.com/t/pytorch-for-jetson/72048 section here. Note that the PyTorchVision
version torchvision==0.17
corresponding to PyTorch
2.2.0 should be installed based on https://github.com/pytorch/vision/blob/main/README.md.
sudo apt-get install libjpeg-dev zlib1g-dev libpython3-dev libopenblas-dev libavcodec-dev libavformat-dev libswscale-dev
git clone --branch v0.17.0 https://github.com/pytorch/vision torchvision
cd torchvision
pip install packaging
export BUILD_VERSION=0.17.0
python3 setup.py install
cd ..
Code language: Bash (bash)
Introducing Yolov8
Follow https://docs.ultralytics.com/ja/quickstart/#__tabbed_1_3 to install Yolov8
.
pip install -e .
Code language: Bash (bash)
Let’s run the Yolov8
segmentation on the webcam video.
yolo predict model=yolov8m-seg.pt source=0 show=True
Code language: Bash (bash)

It seems to be working well. The yolov8m-seg.pt
model above seems to be running at about 16FPS.
Finally, the output of pip freeze
is posted. If you encounter problems, please refer to the version information below.
$ pip freeze
certifi==2024.2.2
charset-normalizer==3.3.2
contourpy==1.2.0
cycler==0.12.1
filelock==3.13.1
fonttools==4.49.0
fsspec==2024.2.0
idna==3.6
Jinja2==3.1.3
kiwisolver==1.4.5
MarkupSafe==2.1.5
matplotlib==3.8.3
mpmath==1.3.0
networkx==3.2.1
numpy==1.26.4
opencv-python==4.9.0.80
packaging==23.2
pandas==2.2.0
pillow==10.2.0
psutil==5.9.8
py-cpuinfo==9.0.0
pyparsing==3.1.1
python-dateutil==2.8.2
pytz==2024.1
PyYAML==6.0.1
requests==2.31.0
scipy==1.12.0
seaborn==0.13.2
six==1.16.0
sympy==1.12
thop==0.1.1.post2209072238
torch @ file:///home/jetson/torch/ultralytics/torch-2.2.0a0%2B81ea7a4.nv24.01-cp310-cp310-linux_aarch64.whl#sha256=10b9966e419ab76b07912377da299a12ea3c49d310c81ff5af359a1de23e1afb
torchvision==0.17.0
tqdm==4.66.2
typing_extensions==4.9.0
tzdata==2024.1
-e git+https://github.com/ultralytics/ultralytics.git@fbed8499da8e499248c401cc5c1648a0a35c5a73#egg=ultralytics
urllib3==2.2.0
Code language: Bash (bash)
That’s it!
Reference
