Build Container Image in Dev Pod
Docker image is widely used as environment control. In this guide, we will show you how to build a container image in a Dev Pod.
Prerequisites
- A Node Group with privilege mode enabled
Steps
Make sure privilege mode is enabled
Go the the node group page, and make sure the privilege mode is enabled for the node group you want to use. You can enable the privilege mode by clicking the "Config" button and select the "Privilege Mode" checkbox. Make sure it's enabled for the node group you want to use.
Create a Dev Pod
Follow the create a dev pod guide, and create a dev pod with the node group you want to use. Make sure the dev pod is created with the privilege mode enabled in the Resource selection step. It is recommended to use the whole node for the dev pod with privilege mode enabled. Eg. select the shape with 8 gpus will use the whole node.
Install Docker and Required Plugins
Once the dev pod is created, you can connect to the dev pod via web terminal. The copy the following script to the terminal and run it.
#!/bin/bash
# Exit script on any error
set -e
################
# Install Docker
################
echo "Updating package lists..."
sudo apt-get update
echo "Installing dependencies..."
sudo apt-get install -y ca-certificates curl
echo "Adding Docker's official GPG key..."
sudo install -m 0755 -d /etc/apt/keyrings
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo tee /etc/apt/keyrings/docker.asc > /dev/null
sudo chmod a+r /etc/apt/keyrings/docker.asc
echo "Adding Docker repository to Apt sources..."
echo \
"deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.asc] https://download.docker.com/linux/ubuntu \
$(. /etc/os-release && echo "$VERSION_CODENAME") stable" | \
sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
echo "Updating package lists after adding Docker repository..."
sudo apt-get update
echo "Installing Docker packages..."
sudo apt-get install -y docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin
##################################
# Install nvidia-container-toolkit
##################################
echo "Adding NVIDIA container toolkit GPG key and repository..."
curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo tee /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg > /dev/null
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 > /dev/null
echo "Installing NVIDIA container toolkit..."
sudo apt-get install -y nvidia-container-toolkit
##################################
# Run NVIDIA CUDA Docker Container
##################################
# Start the docker daemon process with the following command
service docker start
Once the script is executed, you can verify the GPU setup by running the following command.
# Run a container with GPU support to verify the GPU setup
sudo docker run --gpus all nvidia/cuda:11.7.1-base-ubuntu22.04 /usr/bin/nvidia-smi
You should see the GPU setup information like the following.
Fri May 30 07:08:31 2025
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 570.124.06 Driver Version: 570.124.06 CUDA Version: 12.8 |
|-----------------------------------------+------------------------+----------------------+
| 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 H100 80GB HBM3 On | 00000000:61:00.0 Off | 0 |
| N/A 27C P0 74W / 700W | 1MiB / 81559MiB | 0% Default |
| | | Disabled |
+-----------------------------------------+------------------------+----------------------+
| 1 NVIDIA H100 80GB HBM3 On | 00000000:62:00.0 Off | 0 |
| N/A 28C P0 68W / 700W | 1MiB / 81559MiB | 0% Default |
| | | Disabled |
+-----------------------------------------+------------------------+----------------------+
| 2 NVIDIA H100 80GB HBM3 On | 00000000:63:00.0 Off | 0 |
| N/A 31C P0 72W / 700W | 1MiB / 81559MiB | 0% Default |
| | | Disabled |
+-----------------------------------------+------------------------+----------------------+
| 3 NVIDIA H100 80GB HBM3 On | 00000000:64:00.0 Off | 0 |
| N/A 26C P0 66W / 700W | 1MiB / 81559MiB | 0% Default |
| | | Disabled |
+-----------------------------------------+------------------------+----------------------+
| 4 NVIDIA H100 80GB HBM3 On | 00000000:6A:00.0 Off | 0 |
| N/A 26C P0 70W / 700W | 1MiB / 81559MiB | 0% Default |
| | | Disabled |
+-----------------------------------------+------------------------+----------------------+
| 5 NVIDIA H100 80GB HBM3 On | 00000000:6B:00.0 Off | 0 |
| N/A 28C P0 70W / 700W | 1MiB / 81559MiB | 0% Default |
| | | Disabled |
+-----------------------------------------+------------------------+----------------------+
| 6 NVIDIA H100 80GB HBM3 On | 00000000:6C:00.0 Off | 0 |
| N/A 29C P0 70W / 700W | 1MiB / 81559MiB | 0% Default |
| | | Disabled |
+-----------------------------------------+------------------------+----------------------+
| 7 NVIDIA H100 80GB HBM3 On | 00000000:6D:00.0 Off | 0 |
| N/A 25C P0 71W / 700W | 1MiB / 81559MiB | 0% Default |
| | | Disabled |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| No running processes found |
+-----------------------------------------------------------------------------------------+
Build Your Container Image
Now you can build your container image in the dev pod. First, create a Dockerfile with your desired configuration. Here's a simple sample Dockerfile:
# Create a Dockerfile
cat <<EOF > Dockerfile
FROM python:3.9-alpine
# Set working directory
WORKDIR /app
# Expose the port
EXPOSE 8000
# Set the default command to run the server
CMD ["python3", "-m", "http.server", "8000"]
EOF
Now you can build your container image using the following command:
# Build the image
sudo docker build -t my-http-server .
# Run the container
sudo docker run -tid -p 8000:8000 my-http-server
# Check the container is running
curl http://localhost:8000
You can change the Dockerfile based on your needs to build your own container image. For more information about the Dockerfile, please refer to the Dockerfile reference.
Push to Remote Repository
Once the container image is built, you can push it to a remote repository for further use with docker image push command.