AI & Jellyfin

Docker Applications installed on Quince

Installation Strategy

Once the Blackwell GPU passthrough was verified on the Pear host, we transitioned to the Quince VM to set up the containerized environment. This allows us to run high-performance AI (Ollama) and media (Jellyfin) apps while keeping the base OS clean.

Docker Engine Installation

We use the official Docker repository to ensure access to v29+, which includes critical patches for Gen 5 PCIe and Blackwell architecture support.

sudo apt update
sudo apt install ca-certificates curl gnupg

Then setup the repository

sudo install -m 0755 -d /etc/apt/keyrings
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg
sudo chmod a+r /etc/apt/keyrings/docker.gpg
echo "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/ubuntu $(. /etc/os-release && echo "$VERSION_CODENAME") stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null

Next Install Engine & Compose

sudo apt update
sudo apt install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin

NVIDIA Container Toolkit (The "Magic Bridge")

This toolkit enables the libnvidia-container library, which maps the physical GPU device files (/dev/nvidia0, etc.) into the virtualized Docker namespace.

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
sudo apt update
sudo apt install -y nvidia-container-toolkit

and last we configure the Nvidia Container tool Kit and restart Docker

sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker

Final Integration Step

With Docker verified, we move from manual docker run commands to Docker Compose (.yaml). This allows for "Infrastructure as Code," where we can define our 16GB VRAM reservations and Pearpool log paths in a single, repeatable file.