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ramalama cuda.7

Setting Up RamaLama with CUDA Support on Linux systems

This guide walks through the steps required to set up RamaLama with CUDA support.

Install the NVIDIA Container Toolkit​

Follow the installation instructions provided in the NVIDIA Container Toolkit installation guide.

Installation using dnf/yum (For RPM based distros like Fedora)​

  • Install the NVIDIA Container Toolkit packages

sudo dnf install -y nvidia-container-toolkit


:::note
 The NVIDIA Container Toolkit is required on the host for running CUDA in containers.
:::
:::note
 If the above installation is not working for you and you are running Fedora, try removing it and using the [COPR](https://copr.fedorainfracloud.org/coprs/g/ai-ml/nvidia-container-toolkit/).
:::

### Installation using APT (For Debian based distros like Ubuntu)

* Configure the Production Repository

   ```bash
curl -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

  • Update the packages list from the repository

sudo apt-get update


* Install the NVIDIA Container Toolkit packages

   ```bash
sudo apt-get install -y nvidia-container-toolkit
note

The NVIDIA Container Toolkit is required for WSL to have CUDA resources while running a container.

Setting Up CUDA Support​

For additional information see: Support for Container Device Interface

Generate the CDI specification file

sudo nvidia-ctk cdi generate --output=/etc/cdi/nvidia.yaml

Check the names of the generated devices

Open and edit the NVIDIA container runtime configuration:

nvidia-ctk cdi list
INFO[0000] Found 1 CDI devices
nvidia.com/gpu=all
note

Generate a new CDI specification after any configuration change most notably when the driver is upgraded!

Testing the Setup​

Based on this Documentation: Running a Sample Workload

Test the Installation

Run the following command to verify setup:

podman run --rm --device=nvidia.com/gpu=all fedora nvidia-smi

Expected Output

Verify everything is configured correctly, with output similar to this:

Thu Dec  5 19:58:40 2024
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 565.72                 Driver Version: 566.14         CUDA Version: 12.7     |
|-----------------------------------------+------------------------+----------------------+
| 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 RTX 3080        On  |   00000000:09:00.0  On |                  N/A |
| 34%   24C    P5             31W /  380W |     867MiB /  10240MiB |      7%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|    0   N/A  N/A        35      G   /Xwayland                                   N/A      |
|    0   N/A  N/A        35      G   /Xwayland                                   N/A      |
+-----------------------------------------------------------------------------------------+
note

On systems that have SELinux enabled, it may be necessary to turn on the container_use_devices boolean in order to run the nvidia-smi command successfully from a container.

To check the status of the boolean, run the following:

getsebool container_use_devices

If the result of the command shows that the boolean is off, run the following to turn the boolean on:

sudo setsebool -P container_use_devices 1

CUDA_VISIBLE_DEVICES​

RamaLama respects the CUDA_VISIBLE_DEVICES environment variable if it's already set in your environment. If not set, RamaLama will default to using all the GPU detected by nvidia-smi.

You can specify which GPU devices should be visible to RamaLama by setting this variable before running RamaLama commands:

export CUDA_VISIBLE_DEVICES="0,1"  # Use GPUs 0 and 1
ramalama run granite

This is particularly useful in multi-GPU systems where you want to dedicate specific GPUs to different workloads.

If the CUDA_VISIBLE_DEVICES environment variable is set to an empty string, RamaLama will default to using the CPU.

export CUDA_VISIBLE_DEVICES=""  # Defaults to CPU
ramalama run granite

To revert to using all available GPUs, unset the environment variable:

unset CUDA_VISIBLE_DEVICES

Troubleshooting​

CUDA Updates​

On some CUDA software updates, RamaLama stops working complaining about missing shared NVIDIA libraries for example:

ramalama run granite
Error: crun: cannot stat `/lib64/libEGL_nvidia.so.565.77`: No such file or directory: OCI runtime attempted to invoke a command that was not found

Because the CUDA version is updated, the CDI specification file needs to be recreated.

sudo nvidia-ctk cdi generate --output=/etc/cdi/nvidia.yaml

See Also​

ramalama(1), podman(1)

Jan 2025, Originally compiled by Dan Walsh <dwalsh@redhat.com>