File size: 3,571 Bytes
a82517c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 | # Deployment Scripts
## Inference with Python TensorRT on X86
Export ONNX model
```bash
python deployment_scripts/export_onnx.py
```
Build TensorRT engine
```bash
bash deployment_scripts/build_engine.sh
```
Inference with TensorRT
```bash
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`](orin/README.md).
### Prerequisites
- Jetson Thor installed with Jetpack 7.0
### 1. Installation Guide
Clone the repo:
```sh
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:
```sh
docker build -t isaac-gr00t-n1.5:l4t-jp7.0 -f thor.Dockerfile .
```
#### Run Container
To run the container:
Run container for Thor:
```sh
docker run --rm -it --runtime nvidia -v "$PWD":/workspace -w /workspace -p 5555:5555 isaac-gr00t-n1.5:l4t-jp7.0
```
### 2. Inference
* The GR00T N1.5 model is hosted on [Huggingface](https://huggingface.co/nvidia/GR00T-N1.5-3B)
* Example cross embodiment dataset is available at [demo_data/robot_sim.PickNPlace](./demo_data/robot_sim.PickNPlace)
* This project supports to run the inference with PyTorch or Python TensorRT as instructions below
* Add Isaac-GR00T to PYTHONPATH: `export PYTHONPATH=/path/to/Isaac-GR00T:$PYTHONPATH`
### 2.1 Inference with PyTorch
```bash
python deployment_scripts/gr00t_inference.py --inference-mode=pytorch
```
### 2.2 Inference with Python TensorRT
Export ONNX model
```bash
python deployment_scripts/export_onnx.py
```
Build TensorRT engine
```bash
bash deployment_scripts/build_engine.sh
```
Inference with TensorRT
```bash
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:
<div align="center">
<img src="../media/thor-perf.png" width="1200" alt="thor-perf">
</div>
### 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.
|