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Version: ROS 2 Jazzy

CUDA Installation

note

CUDA is a proprietary framework developed by Nvidia. To use CUDA you must have a compatible GPU from Nvidia.

Verify your GPU supports CUDA​

To check that you have an Nvidia GPU, run the following command:

lspci | grep -i nvidia

Check that

  1. there is at least one graphics card listed, and
  2. that your GPU model supports CUDA

Add Nvidia package sources​

The easiest way to install CUDA is to add Nvidia's apt sources to your configuration. Run the following commands on the robot:

wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2404/x86_64/cuda-keyring_1.1-1_all.deb
sudo dpkg -i cuda-keyring_1.1-1_all.deb
sudo apt-get update

Install CUDA​

note

If you have a realtime Linux kernel installed (PREEMPT_RT) you may encounter errors when installing CUDA, as it does not officially support realtime kernels. To work around these errors add IGNORE_PREEMPT_RT_PRESENCE=1 before any apt or apt-get commands, e.g.

sudo IGNORE_PREEMPT_RT_PRESENCE=1 apt-get install cuda-toolkit

To install CUDA, run the following command:

sudo apt-get install cuda-toolkit

Optionally you can also install all GDS packages:

sudo apt-get install nvidia-gds

Reboot the computer after installing the packages.

Configure environment​

note

At the time of writing, CUDA 12.9 is the latest version. The instructions below assume this version is being installed, but if you installed a different version you will need to modify the instructions accordingly.

After rebooting, it is recommended to modify $HOME/.bashrc to update your environment variables to enable CUDA tools. Add the following to .bashrc:

export PATH=/usr/local/cuda-12.9/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/cuda-12.9/lib64

After modifying .bashrc either close the terminal and re-open it or run source $HOME/.bashrc to apply the changes.

CUDA samples​

You can optionally download and build the CUDA samples to verify your installation:

git clone https://github.com/NVIDIA/cuda-samples.git
cd cuda-samples
mkdir build
cd build
cmake ..
make

Run the deviceQuery sample from the build directory to make sure CUDA can run correctly on your system:

./Samples/1_Utilities/deviceQuery/deviceQuery

You should see Result = PASS at the bottom of the output, indicating a CUDA-capable GPU was is detected and usable.

Refer the CUDA samples documentation for details on additional sample programs.

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