Deployment Scripts
Inference with Python TensorRT on X86
Export ONNX model
python deployment_scripts/export_onnx.py
Build TensorRT engine
bash deployment_scripts/build_engine.sh
Inference with TensorRT
python deployment_scripts/gr00t_inference.py --inference_mode=tensorrt
Jetson Deployment
A detailed guide for deploying GR00T N1.5 on Orin is available in orin/README.md.
Prerequisites
- Jetson Thor installed with Jetpack 7.0
1. Installation Guide
Clone the repo:
git clone https://github.com/NVIDIA/Isaac-GR00T
cd Isaac-GR00T
Deploy Isaac-GR00T with Container
Build Container
To build a container for Isaac-GR00T:
Build container for Jetson Thor:
docker build -t isaac-gr00t-n1.5:l4t-jp7.0 -f thor.Dockerfile .
Run Container
To run the container:
Run container for Thor:
docker run --rm -it --runtime nvidia -v "$PWD":/workspace -w /workspace -p 5555:5555 isaac-gr00t-n1.5:l4t-jp7.0
2. Inference
- The GR00T N1.5 model is hosted on Huggingface
- Example cross embodiment dataset is available at demo_data/robot_sim.PickNPlace
- This project supports to run the inference with PyTorch or Python TensorRT as instructions below
- Add Isaac-GR00T to PYTHONPATH:
export PYTHONPATH=/path/to/Isaac-GR00T:$PYTHONPATH
2.1 Inference with PyTorch
python deployment_scripts/gr00t_inference.py --inference-mode=pytorch
2.2 Inference with Python TensorRT
Export ONNX model
python deployment_scripts/export_onnx.py
Build TensorRT engine
bash deployment_scripts/build_engine.sh
Inference with TensorRT
python deployment_scripts/gr00t_inference.py --inference-mode=tensorrt
3. Performance
3.1 Pipline Performance
Here's comparison of E2E performance between PyTorch and TensorRT on Thor:
3.2 Models Performance
Model latency measured by trtexec with batch_size=1.
| Model Name | Thor benchmark perf (ms) (FP16) | Thor benchmark perf (ms) (FP8+FP4) |
|---|---|---|
| Action_Head - process_backbone_output | 2.35 | / |
| Action_Head - state_encoder | 0.04 | / |
| Action_Head - action_encoder | 0.10 | / |
| Action_Head - DiT | 5.46 | 3.41 |
| Action_Head - action_decoder | 0.03 | / |
| VLM - ViT | 5.21 | 4.10 |
| VLM - LLM | 8.53 | 5.81 |
Note: The module latency (e.g., DiT Block) in pipeline is slightly longer than the model latency in benchmark table above because the module (e.g., Action_Head - DiT) latency not only includes the model latency in table above but also accounts for the overhead of data transfer from PyTorch to TRT and returning from TRT to PyTorch.