--- source: sighthound-developer-portal url: https://dev.sighthound.com/sio/docs/sioSetupTutorial/ markdown-url: https://dev.sighthound.com/sio/docs/sioSetupTutorial.md title: "SIO Setup Tutorial (Docker)" description: "Install Docker, NVIDIA drivers, and the SIO container toolkit prerequisites on Ubuntu." content-type: text/markdown --- > For AI agents: a documentation index is available at [llms.txt](https://dev.sighthound.com/llms.txt). Markdown versions are available at matching `.md` URLs. # SIO Setup Tutorial (Docker)

SIO tutorial

Prepare an Ubuntu host for SIO Docker workloads with system updates, NVIDIA driver setup, Docker installation, and container toolkit validation.

[Start setup](#step-1-ubuntu-upgrades) [Open SIO quickstart](../quickstart/)

Host preparation

Update Ubuntu packages and confirm the system is ready for Docker-based workloads.

GPU support

Install NVIDIA drivers and the NVIDIA Container Toolkit when GPU acceleration is available.

SIO image

Authenticate with the private registry, pull the SIO image, and run a pipeline command.

This document walks through the steps required to run SIO Docker image on Ubuntu ## Step 1: Ubuntu Upgrades - Start with updating and upgrading Ubuntu packages ```sh sudo apt-get update sudo apt-get upgrade ``` ## Step 2: NVIDIA GPU Drivers Installation - Try the following command before continuing with the GPU driver installation instructions to see if they're already installed on the system: ```sh nvidia-smi ``` - If the command worked it will show an output similar to the following. If so, skip to Step 3. - Follow the instructions on the driver web page: [Advanced Driver Search official NVIDIA drivers.](https://www.nvidia.com/Download/Find.aspx) - Below is an example taken from installation instructions for a RTX 3060 GPU. - Once you have downloaded the driver, change to the directory containing the driver package and install the driver by running, as root, ```sh sudo sh ./NVIDIA-Linux-x86_64-525.60.11.run ``` - Reboot system ```sh sudo reboot ``` - After the reboot, make sure the GPU drivers were successfully installed by running the following command: ```sh nvidia-smi ``` ## Step 3: Docker Installation - Install the Docker Engine by following the [instructions on Docker's website](https://docs.docker.com/engine/install/ubuntu/), or use the following commands. If you run into any issues, Docker's page may have troubleshooting info that can help. - Uninstall any previous installations ```sh sudo apt-get remove docker docker-engine docker.io containerd runc ``` - Update and install required packages for Ubuntu ```sh sudo apt-get update sudo apt-get install \ ca-certificates \ curl \ gnupg \ lsb-release ``` - Add Docker GPG key ```sh sudo mkdir -p /etc/apt/keyrings curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg ``` - Repository setup ```sh echo \ "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/ubuntu \ $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null ``` - Install Docker ```sh sudo apt-get update sudo apt-get install docker-ce docker-ce-cli containerd.io docker-compose-plugin ``` - To verify if the installation worked try the following ```sh sudo docker run hello-world sudo docker compose version ``` - To use Docker commands without sudo ```sh sudo usermod -aG docker $USER ``` - The terminal needs to be restarted for this to take effect, I prefer a reboot ```sh sudo reboot ``` - Verify you can run commands without sudo ```sh docker ps ``` ## Step 4: NVIDIA Container Toolkit - This allows Docker to use the installed GPU. Full documentation can be found on [NVIDIA's Installation Guide](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html). - Setup package repository and GPG key ```sh distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \ && 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/$distribution/libnvidia-container.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 ``` - Install nvidia-docker2 ```sh sudo apt-get update sudo apt-get install -y nvidia-docker2 ``` - Restart Docker ```sh sudo systemctl restart docker ``` - To verify if the GPU can be used with Docker ```sh sudo docker run --rm --gpus all nvidia/cuda:11.0.3-base-ubuntu20.04 nvidia-smi ``` ## Step 5: Get SIO Docker Image - The SIO docker image can be obtained from our private registry, once we've created an account for you and provided you with `sighthound-keyfile.json`: ```sh cat sighthound-keyfile.json | docker login -u _json_key --password-stdin us-central1-docker.pkg.dev docker pull us-central1-docker.pkg.dev/ext-edge-analytics/docker/sio:{{ sio.docker.latest }} ``` - At the end of the command above, `{{ sio.docker.latest }}` represents the release that can be changed depending on the current release version. Latest release as of {{ sio.docker.releaseDate }} was `{{ sio.docker.latest }}`. - More details are available at [Working with SIO framework](https://dev.sighthound.com/sio/docs/quickstart/). ## Step 6: Run SIO pipelines - Here’s an example that uses the GPU and runs the folder watcher pipeline with the Docker image that we pulled in Step 4. ```sh docker run -it --gpus all --rm \ -v /data:/data \ -e SIO_DATA_DIR=/data \ us-central1-docker.pkg.dev/ext-edge-analytics/docker/sio:{{ sio.docker.latest }} \ /sighthound/sio/bin/runPipeline \ /sighthound/sio/share/pipelines/VehicleAnalytics/VehicleAnalyticsFolderWatch.yaml \ folderPath=/data/watchedFolder \ --license-path /data/sighthound-license ``` - More details are available at [Working with SIO framework](https://dev.sighthound.com/sio/docs/quickstart/). --- # Agent Instructions Use this Markdown page as context for Sighthound Developer Portal questions. For broader navigation, read https://dev.sighthound.com/llms.txt. 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