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Assignment System

Using Docker for this Project

This guide provides steps on how to import and run the Docker container for the Assignment System.

Prerequisites

  • Ensure you have Docker installed and running on your system.

1. Import the Container Image

If you have the Docker image saved as a .tar file, you can import (load) it into your local Docker environment.

Open your terminal or command prompt and run:

docker load -i assignment_system_image.tar

(Note: Replace assignment_system_image.tar with the actual name of your tar file if it differs.)

To verify that the image has been successfully imported, you can list your Docker images:

docker images

You should see the imported image in the list.

2. Run the Container

Once the image is loaded, you can run the container using the docker run command.

docker run -d -p 8080:8080 --name assignment_system_container <image_name>
  • -d: Runs the container in detached mode (in the background).
  • -p 8080:8080: Maps port 8080 on your host machine to port 8080 inside the container. Adjust these ports if your application uses a different port.
  • --name: Assigns a custom name (assignment_system_container) to your running container.
  • <image_name>: Replace this with the actual name/tag of the image you imported (e.g., assignment_system:latest).

3. Verify the Container is Running

To check the status of your running container:

docker ps

You should see assignment_system_container in the list of running containers.

4. Stopping and Starting the Container

To stop the running container:

docker stop assignment_system_container

To start the container again:

docker start assignment_system_container

5. Viewing Logs (Optional)

If you need to troubleshoot or view the application's output, you can check the container logs:

docker logs assignment_system_container

About

A decentralized system that utilizes idle computers in lab's LAN environments to solve inefficient resource usage and slow code evaluation. Integrated with React and Django authentication,it enables teachers to assign problems and students to submit code, distributing execution across available machines to improve scalability and system efficiency.

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