name: yolov8-deepstream-trt description: Deploy YOLOv8 on NVIDIA Jetson using TensorRT and DeepStream SDK for high-performance video inference. Covers JetPack flashing, DeepStream installation, PyTorch/torchvision setup, DeepStream-Yolo configuration, INT8 calibration, and multistream benchmarks. Supports JetPack 4.6+ with DeepStream 6.0+.

YOLOv8 with TensorRT and DeepStream SDK on Jetson

Deploy YOLOv8 models on Jetson using DeepStream SDK and TensorRT for maximum inference performance. Supports FP32, FP16, and INT8 precision with single and multistream configurations.

Execution model

Run one phase at a time. After each phase:

• Relay all output to the user.
• If output contains [STOP] → stop immediately, consult the failure decision tree.
• If output ends with [OK] → tell the user "Phase N complete" and proceed to the next phase.

Prerequisites

Phase 1 — Install DeepStream dependencies (~5 min)

After JetPack is flashed with DeepStream SDK via SDK Manager, install additional dependencies:

sudo apt install \
  libssl1.1 \
  libgstreamer1.0-0 \
  gstreamer1.0-tools \
  gstreamer1.0-plugins-good \
  gstreamer1.0-plugins-bad \
  gstreamer1.0-plugins-ugly \
  gstreamer1.0-libav \
  libgstreamer-plugins-base1.0-dev \
  libgstrtspserver-1.0-0 \
  libjansson4 \
  libyaml-cpp-dev

[OK] when all packages install. [STOP] if packages not found (check JetPack/DeepStream version).

Phase 2 — Install Ultralytics and PyTorch (~15 min)

sudo apt update
sudo apt install -y python3-pip
pip3 install --upgrade pip
git clone https://github.com/ultralytics/ultralytics.git
cd ultralytics

Edit requirements.txt to comment out torch/torchvision:

# torch>=1.7.0
# torchvision>=0.8.1

pip3 install -r requirements.txt

Install PyTorch for Jetson (example for JP5.0.2 / PyTorch 1.12):

sudo apt-get install -y libopenblas-base libopenmpi-dev
wget https://developer.download.nvidia.com/compute/redist/jp/v50/pytorch/torch-1.12.0a0+2c916ef.nv22.3-cp38-cp38-linux_aarch64.whl -O torch-1.12.0.whl
pip3 install torch-1.12.0.whl

Install matching torchvision:

sudo apt install -y libjpeg-dev zlib1g-dev
git clone --branch v0.13.0 https://github.com/pytorch/vision torchvision
cd torchvision
python3 setup.py install --user

[OK] when import torch and import torchvision succeed. [STOP] if wheel install fails.

Phase 3 — Configure DeepStream for YOLOv8 (~10 min)

cd ~
git clone https://github.com/marcoslucianops/DeepStream-Yolo
cd DeepStream-Yolo
git checkout 68f762d5bdeae7ac3458529bfe6fed72714336ca

Generate config and weights:

cp utils/gen_wts_yoloV8.py ~/ultralytics
cd ~/ultralytics
wget https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt
python3 gen_wts_yoloV8.py -w yolov8s.pt

Copy generated files and compile:

cp yolov8s.cfg ~/DeepStream-Yolo
cp yolov8s.wts ~/DeepStream-Yolo
cp labels.txt ~/DeepStream-Yolo
cd ~/DeepStream-Yolo
CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo

Edit config_infer_primary_yoloV8.txt:

custom-network-config=yolov8s.cfg
model-file=yolov8s.wts
num-detected-classes=80

Edit deepstream_app_config.txt to set:

config-file=config_infer_primary_yoloV8.txt

[OK] when make completes and config files are updated. [STOP] if CUDA_VER mismatch or compile errors.

Phase 4 — Run inference

deepstream-app -c deepstream_app_config.txt

[OK] when video plays with detection overlays. [STOP] if DeepStream crashes or engine build fails.

Phase 5 — (Optional) INT8 calibration for higher performance

sudo apt-get install libopencv-dev
cd ~/DeepStream-Yolo
CUDA_VER=11.4 OPENCV=1 make -C nvdsinfer_custom_impl_Yolo

Download COCO val2017, select calibration images:

mkdir calibration
for jpg in $(ls -1 val2017/*.jpg | sort -R | head -1000); do cp ${jpg} calibration/; done
realpath calibration/*jpg > calibration.txt
export INT8_CALIB_IMG_PATH=calibration.txt
export INT8_CALIB_BATCH_SIZE=1

Update config_infer_primary_yoloV8.txt:

model-engine-file=model_b1_gpu0_int8.engine
int8-calib-file=calib.table
network-mode=1

deepstream-app -c deepstream_app_config.txt

[OK] when INT8 inference runs with higher FPS. [STOP] if calibration fails.

Failure decision tree

Reference files

• references/source.body.md — Full Seeed Wiki tutorial with DeepStream/JetPack version table, multistream configuration, trtexec benchmarking, INT8 calibration walkthrough, and performance benchmarks across Jetson devices (reference only)