This repository contains an implementation of Model Predictive Path Integral Control (MPPI) with PyTorch to accelerate computations on the GPU.
- Ubuntu Focal 20.04 (LTS)
- NVIDIA Driver 510 or later due to PyTorch 2.x
- cuda 11.8
- Python 3.10
- PyTorch 2.0
Docker Setup
# Install from get.docker.com
curl -fsSL https://get.docker.com -o get-docker.sh
sudo sh get-docker.sh
sudo groupadd docker
sudo usermod -aG docker $USERcurl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
&& curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
sudo apt-get update
sudo apt-get install -y nvidia-container-toolkit nvidia-container-runtime
sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker# build container
make build
# Open remote container via Vscode (Recommend)
# 1. Open the folder using vscode
# 2. Ctrl+P and select 'devcontainer rebuild and reopen in container'
# Then, you can skip the following commands
# Or Run container via terminal
make bashpython3 -m venv .venv
source .venv/bin/activate
pip3 install -e .[dev]python3 app/navigation2d.py
python3 app/pendulum.py
python3 app/cartpole.py
python3 app/mountaincar.py
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