Onboard JetPack 5 Inference Backends

This note records the working onnx-gpu and tensorrt setup for a JetPack 5 onboard Orin such as g1-cable.

Platform

Use this path only for JetPack 5 / L4T R35 / Ubuntu 20.04 / aarch64. On the tested host:

L4T: R35.3.1
CUDA: 11.4
TensorRT: 8.5.2.2
System Python: 3.8

Do not install these Jetson wheels into the default project uv environment if that environment uses Python 3.10. The Jetson ORT wheel and system TensorRT binding used here are Python 3.8 artifacts. There is no supported JetPack 5 / Python 3.10 prebuilt route for ONNX Runtime GPU or TensorRT; use Python 3.8 on JetPack 5, or upgrade to JetPack 6 for a supported Python 3.10 stack.

Shared Environment

Code branches

Run the onboard Python 3.8 stack from the Python 3.8 compatibility branches. The backend wheels are not the only Python 3.8 constraint: full policy tracking also imports sim2real, any4hdmi, and mjhub, so the source trees must avoid Python 3.10-only typing syntax.

For git-managed checkouts:

cd ~/sim2real
git fetch origin py38
git switch py38

cd ~/any4hdmi
git fetch origin py38
git switch py38

For rsync-managed robot copies, sync from the local py38 branches rather than from the normal development branch when the target process runs inside .venv-jp5-ort.

Create a Python 3.8 venv that can see JetPack system packages:

cd ~/sim2real
python3 -m venv --system-site-packages .venv-jp5-ort
bash -lic 'proxy_on; .venv-jp5-ort/bin/python -m pip install -U pip'

Install common runtime packages:

cd ~/sim2real
bash -lic 'proxy_on; .venv-jp5-ort/bin/python -m pip install onnx==1.17.0 loguru pyyaml tyro cuda-python==12.3.0'

Install the Python 3.8-compatible mjhub branch when running full sim2real tracking scripts:

bash -lic 'proxy_on; .venv-jp5-ort/bin/python -m pip install --no-deps "mjhub @ git+https://github.com/EGalahad/mjhub.git@py38"'

Policy tracking also needs the normal runtime imports that the minimal backend benchmark may not touch:

cd ~/sim2real
bash -lic 'proxy_on; .venv-jp5-ort/bin/python -m pip install \
  mujoco==3.2.3 \
  pyzmq==25.1.2 \
  tqdm==4.66.6 \
  sshkeyboard==2.3.1 \
  huggingface-hub==0.36.2 \
  torch==2.4.1 \
  tensordict==0.1.2'

Install viser==0.2.23 only when the Python 3.8 process needs to create a Viser server. The current mjviser release uses Python 3.10 typing syntax, so run viewer-heavy base-sim processes from the normal environment or use a Python 3.8-compatible mjviser branch before moving them into .venv-jp5-ort.

ONNX GPU Backend

Install NVIDIA's Jetson ONNX Runtime GPU wheel:

mkdir -p /tmp/ort-gpu
bash -lic 'proxy_on; wget -O /tmp/ort-gpu/onnxruntime_gpu-1.16.0-cp38-cp38-linux_aarch64.whl https://nvidia.box.com/shared/static/iizg3ggrtdkqawkmebbfixo7sce6j365.whl'
.venv-jp5-ort/bin/python -m pip install /tmp/ort-gpu/onnxruntime_gpu-1.16.0-cp38-cp38-linux_aarch64.whl

Verify providers:

.venv-jp5-ort/bin/python -c "import onnxruntime as ort; print(ort.__version__, ort.get_available_providers())"

Expected providers include:

['TensorrtExecutionProvider', 'CUDAExecutionProvider', 'CPUExecutionProvider']

JetPack 5 ORT 1.16 does not accept newer exports such as IR 10 / opset 20. Use an IR 9 / opset 19 copy for onnx-gpu.

Example benchmark:

cd ~/sim2real
PYTHONPATH=$PWD:~/any4hdmi/src .venv-jp5-ort/bin/python scripts/test_policy_inference.py \
  --policy_config /tmp/ort-test/policy-op19.yaml \
  --inference_backend onnx-gpu \
  --warmup 50 --runs 1000

Example tracking command:

cd ~/sim2real
PYTHONPATH=$PWD:~/any4hdmi/src .venv-jp5-ort/bin/python sim2real/rl_policy/tracking.py \
  --robot g1 \
  --policy_config /tmp/ort-test/policy-sonic-release-op19.yaml \
  --motion_path ../any4hdmi/output/root_tracking_test/motions/forward_1.npz \
  --inference_backend onnx-gpu

TensorRT Backend

TensorRT itself comes from JetPack system packages, for example /usr/lib/python3.8/dist-packages/tensorrt. The venv sees it because it was created with --system-site-packages.

TensorRT 8.5's Python binding still references np.bool, so use NumPy 1.23:

cd ~/sim2real
.venv-jp5-ort/bin/python -m pip install numpy==1.23.5

Verify TensorRT:

.venv-jp5-ort/bin/python -c "import tensorrt as trt; print(trt.__version__)"

Expected output:

8.5.2.2

TensorRT 8.5 may reject exported ONNX graphs containing LayerNormalization and Mish. Use a TensorRT-friendly copy that lowers to IR 9 / opset 13 and expands those ops into primitive ONNX ops before building the engine.

Example benchmark:

cd ~/sim2real
PYTHONPATH=$PWD:~/any4hdmi/src HDMI_TRT_FP16=1 HDMI_TRT_FORCE_REBUILD=0 .venv-jp5-ort/bin/python scripts/test_policy_inference.py \
  --policy_config /tmp/ort-test/policy-trt.yaml \
  --inference_backend tensorrt \
  --warmup 50 --runs 1000

Set HDMI_TRT_FORCE_REBUILD=1 when changing the ONNX or when the cached .plan should be regenerated.

Example tracking command:

cd ~/sim2real
PYTHONPATH=$PWD:~/any4hdmi/src HDMI_TRT_FP16=1 HDMI_TRT_FORCE_REBUILD=0 .venv-jp5-ort/bin/python sim2real/rl_policy/tracking.py \
  --robot g1 \
  --policy_config /tmp/ort-test/policy-sonic-release-trt.yaml \
  --motion_path ../any4hdmi/output/root_tracking_test/motions/forward_1.npz \
  --inference_backend tensorrt

Known Issues

pip install onnxruntime-gpu from PyPI does not provide a JetPack 5 aarch64 wheel. Use the NVIDIA Jetson wheel above.
proxy_on is most reliable when run through bash -lic.
Python 3.10 project venvs cannot load the cp38 Jetson ORT wheel or Python 3.8 TensorRT binding.
The JetPack 5 package indexes do not provide a supported cp310 ONNX Runtime GPU wheel, and JetPack 5 TensorRT Python bindings are built for system Python 3.8. A Python 3.10 route means source builds or JetPack 6.
ORT 1.16 rejects IR 10 models with Unsupported model IR version: 10, max supported IR version: 9.
ORT 1.16 warns that opset 20 is newer than its guaranteed support. Use opset 19 for JetPack 5.
TensorRT 8.5 parser can reject LayerNormalization and Mish as missing plugins. Expand them into primitive ops.
TensorRT 8.5 legacy binding execution should use execute_async_v2(bindings, stream). Passing bindings to execute_async_v3 causes an incompatible-arguments error.
Full tracking on JetPack 5 Python 3.8 may expose Python 3.10-only annotations in project code or dependencies. Keep Python 3.8 compatibility patches on dedicated py38 branches and merge normal development changes into py38 when they are needed onboard.
A minimal inference benchmark can pass before full policy tracking works. Missing runtime packages usually surface later as imports such as mujoco, zmq, sshkeyboard, huggingface_hub, torch, or tensordict.

Platform​

Shared Environment​

Code branches​

ONNX GPU Backend​

TensorRT Backend​

Known Issues​