AIIA Docker Environment

This project sets up a Docker environment based on NVIDIA's CUDA 11.8 with cuDNN 8 and Ubuntu 22.04. The environment is configured with Python, PyTorch, and essential machine learning libraries. It also includes SSH access and restrictions on the ability to modify passwords.

Prerequisites

• Docker installed on your machine
• GPU support for Docker (NVIDIA Docker support)

How to Use

Follow the steps below to set up and run the Docker container:

1. Pull the Base CUDA Image

First, pull the base CUDA image from Docker Hub:

docker pull nvidia/cuda:11.8.0-cudnn8-runtime-ubuntu22.04

2. Build the Docker Image

Build the Docker image from the provided Dockerfile. Replace aiia_docker with your desired image name:

docker build -t aiia_docker .

3. Run the Docker Container

Once the image is built, you can run the Docker container. This command runs the container with GPU support, limits the CPU usage to 5 cores, and exposes port 2222 for SSH access:

docker run -d -p 2222:22 --gpus all --cpus='5' --name aiia_pc aiia_docker

4. Access the Container via SSH

After the container is running, you can access it via SSH using the following command. Make sure to replace your_ip_address with the correct IP address:

The default password for the aiialab user is 00000000. Root login is also enabled with the password t3-csie-420.

ssh aiialab@your_ip_address -p 2222

5. Stop the Container

To stop the running container, use the following command:

docker stop aiia_pc

6. Restart the Container

If you need to restart the container, use the command below:

docker start aiia_pc

Notes

• This setup restricts the aiialab user from changing their own password.
• SSH access is enabled for both the root and aiialab users.
• The container is configured with the following Python libraries:
  • numpy
  • pandas
  • matplotlib
  • scikit-learn
  • tqdm
  • scipy
  • PyTorch (torch, torchvision, torchaudio)