Podman

This guide covers pulling the RLC Pro AI OCI image from the CIQ Depot registry and running it with Podman, the native container runtime on Rocky Linux and RHEL family systems. Docker users should follow the Docker guide.

Prerequisites

A CIQ Portal account with access to the RLC Pro AI product, and your credentials from the Portal access token page.
Podman installed. On Rocky Linux family systems:
```
sudo dnf install podman
```
Roughly 7 GB of free disk space for the image.

Authenticate to the Depot registry


podman login depot.ciq.com -u <DEPOT_USERNAME> -p <TOKEN>


Login Succeeded!

Note

On an RLC Pro AI host that is already enrolled with depot login, the same user string and token used for enrollment work for the container registry.

Pull the image


podman pull depot.ciq.com/rlc-ai-9/rlc-9-ai-oci-images/rlc-pro-ai:9

Confirm the image is present:


podman images


REPOSITORY                                             TAG         IMAGE ID      CREATED       SIZE
depot.ciq.com/rlc-ai-9/rlc-9-ai-oci-images/rlc-pro-ai  9           a2592524245a  4 months ago  6.83 GB

Verify the contents

The container carries the same CIQ-built, RPM-managed AI stack as the full operating system. Check the OS identity and the key packages:


podman run --rm depot.ciq.com/rlc-ai-9/rlc-9-ai-oci-images/rlc-pro-ai:9 \
  bash -c "head -4 /etc/os-release; rpm -q python3-torch python3-torchvision"


NAME="Rocky Linux from CIQ"
VARIANT="Pro AI"
VARIANT_ID="pro_ai"
VERSION="9.7 (Blue Onyx)"
python3-torch-2.8.0-12.el9_ciqai.x86_64
python3-torchvision-0.23.0-1.el9_ciqai.x86_64

Run an interactive session


podman run -it --rm depot.ciq.com/rlc-ai-9/rlc-9-ai-oci-images/rlc-pro-ai:9 bash

From there, python3 has the pre-installed PyTorch stack available, exactly as described in the AI Frameworks guide.

Use as a base image

Reference the image in a Containerfile to build your own application images on top of the validated stack:


FROM depot.ciq.com/rlc-ai-9/rlc-9-ai-oci-images/rlc-pro-ai:9

RUN dnf install -y python3-pip && dnf clean all
COPY requirements.txt /app/
RUN pip3 install -r /app/requirements.txt

Build with podman build.

GPU access

To give containers access to NVIDIA GPUs on a GPU-equipped RLC Pro AI host, install the NVIDIA Container Toolkit, which CIQ delivers in the rlc-9-supplemental repository:


sudo dnf install nvidia-container-toolkit


nvidia-ctk --version


NVIDIA Container Toolkit CLI version 1.19.1

Podman uses the Container Device Interface (CDI). With the toolkit installed on a GPU host, generate a CDI specification and run containers with GPU devices attached:


sudo nvidia-ctk cdi generate --output=/etc/cdi/nvidia.yaml
podman run --rm --device nvidia.com/gpu=all \
  depot.ciq.com/rlc-ai-9/rlc-9-ai-oci-images/rlc-pro-ai:9 \
  python3 -c "import torch; print(torch.cuda.is_available())"

See the NVIDIA Container Toolkit documentation for CDI details.

Troubleshooting

authentication required when pulling. The registry rejects unauthenticated pulls. Run podman login depot.ciq.com with your Portal credentials and retry. For other registry issues, see the Depot container guide.

Pull fails partway through. Check free disk space; the image needs roughly 7 GB in the container storage location (/var/lib/containers for root, ~/.local/share/containers for rootless).

Overview Docker