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CUDA and cuDNN (Install)
Pop!_OS 22.04 LTS
Basic CUDA runtime functionality is installed automatically with the NVIDIA driver (in the libnvidia-compute-* and nvidia-compute-utils-* packages). The maximum CUDA version supported by the libraries included with the driver can be seen using the nvidia-smi command.
Additional tools for using and developing with CUDA can be installed with the nvidia-cuda-toolkit package:
sudo apt install nvidia-cuda-toolkit
The nvidia-cuda-toolkit package is maintained by Ubuntu, and may contain an older version of CUDA than what the driver supports.
Other Versions of CUDA
The nvidia-container-toolkit package uses Docker containers to allow alternate versions of the CUDA libraries to be installed alongside the one included with the NVIDIA driver. You can see the different Docker images that are published by NVIDIA here: https://hub.docker.com/r/nvidia/cuda/
This example installs a development enviroment with CUDA version 12.4.
Install Software
After making sure the system is up-to-date, install the NVIDIA container toolkit. In this example, Docker will also be installed using the docker.io package.
sudo apt update
sudo apt full-upgrade
sudo apt install nvidia-container-toolkit docker.io
The user account working with the Container Toolkit must be added to the docker group if that hasn't been done already:
sudo usermod -aG docker $USER
The last step is to add a kernel parameter:
sudo kernelstub --add-options "systemd.unified_cgroup_hierarchy=0"
...and reboot.
Configure the Docker daemon for the NVIDIA Container Runtime
Use the NVIDIA Container Toolkit CLI to configure Docker to use the NVIDIA libraries, then restart Docker:
sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker
Test Configuration
Run this command to check the Docker configuration for CUDA:
docker run --rm --runtime=nvidia --gpus all nvidia/cuda:12.4.0-devel-ubuntu22.04 nvidia-smi
The output displays the CUDA version supported by the container:
Thu Jun 13 14:43:51 2024
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 550.67. Driver Version: 550.67 CUDA Version: 12.4 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA GeForce ... Off | 00000000:01:00.0 On | N/A |
| 30% 37C P5 N/A / 75W | 789MiB / 4096MiB | 16% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
+-----------------------------------------------------------------------------+
Run the Container
Start a shell within the container:
docker run -it --rm --runtime=nvidia --gpus all nvidia/cuda:12.4.0-devel-ubuntu22.04 bash
Commands can then be run with CUDA support:
root@5397e7ea7f57:/# nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2024 NVIDIA Corporation
Built on Thu_Mar_28_02:18:24_PDT_2024
Cuda compilation tools, release 12.4, V12.4.131
Build cuda_12.4.r12.4/compiler.34097967_0
The container can be viewed and managed using docker ps in another terminal or tab:
system76@pop-os:~$ docker ps
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
5397e7ea7f57 nvidia/cuda:12.4.0-devel-ubuntu22.04 "/opt/nvidia/nvidia_…" 2 minutes ago Up 2 minutes boring_tesla
The container ID can be referenced to copy files into and out of the container:
system76@pop-os:~$ git clone https://github.com/NVIDIA/cuda-samples.git
system76@pop-os:~$ docker cp cuda-samples/ 5397e7ea7f57:/root/cuda-samples/
Now, from within the container, an example project can be built:
root@5397e7ea7f57# cd /root/cuda-samples/Samples/0_Introduction/c++11_cuda/
root@5397e7ea7f57:~/cuda-samples/Samples/0_Introduction/c++11_cuda# make
The binary (c++11_cuda) is built:
root@5397e7ea7f57:~/cuda-samples/Samples/0_Introduction/c++11_cuda# ls -l
total 6108
-rw-rw-r-- 1 1000 1000 13951 Mar 24 16:45 Makefile
-rw-rw-r-- 1 1000 1000 2090 Mar 24 16:45 NsightEclipse.xml
-rw-rw-r-- 1 1000 1000 3556 Mar 24 16:45 README.md
-rwxr-xr-x 1 root root 2147784 Mar 24 16:48 c++11_cuda
...
Pop!_OS 20.04 LTS
Install the Latest NVIDIA CUDA Toolkit
To install the CUDA toolkit, run this command:
sudo apt install system76-cuda-latest
To install the cuDNN library, run this command:
sudo apt install system76-cudnn-11.2
To verify installation, run this command after a reboot:
nvcc -V
Versions in Pop!_OS 20.04 LTS
To install CUDA 10.0:
sudo apt install system76-cuda-10.0
For the respective cuDNN library:
sudo apt install system76-cudnn-10.0
To install CUDA 10.1:
sudo apt install system76-cuda-10.1
For the respective cuDNN library:
sudo apt install system76-cudnn-10.1
To install CUDA 10.2:
sudo apt install system76-cuda-10.2
For the respective cuDNN library:
sudo apt install system76-cudnn-10.2
Switch Between CUDA Versions
You can switch between each CUDA version with the following command:
sudo update-alternatives --config cuda
To verify installation, run this command to see the current version of the NVIDIA CUDA compiler:
nvcc -V
You can check the version of cuDNN with this command:
cat /usr/lib/cuda/include/cudnn_version.h | grep CUDNN_MAJOR -A 2
Not Running Pop!_OS?
The previous instructions will work with Pop!_OS out of the box, Ubuntu and other Debian derivatives require additional commands.
ℹ️ These packages have only been tested with the System76 NVIDIA driver.
Ubuntu 20.04 LTS
echo "deb http://apt.pop-os.org/proprietary focal main" | sudo tee -a /etc/apt/sources.list.d/pop-proprietary.list
sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-key 204DD8AEC33A7AFF
sudo apt update
The following article will go over installing the System76 NVIDIA driver.