docker/README.md

Getting Started Using Docker

This guide provides step-by-step instructions on how to set up and run the Docker environment for your GPT-Engineer project.

Prerequisites

• Docker installed on your machine.
• Git (for cloning the repository).

Setup Instructions

Using Docker CLI

1. Clone the Repository

   git clone https://github.com/gpt-engineer-org/gpt-engineer.git
   cd gpt-engineer
   

2. Build the Docker Image

   docker build --rm -t gpt-engineer -f docker/Dockerfile .
   

3. Run the Docker Container

   docker run -it --rm -e OPENAI_API_KEY="YOUR_OPENAI_KEY" -v ./your-project:/project gpt-engineer
   

   Replace YOUR_OPENAI_KEY with your actual OpenAI API key. The -v flag mounts your local your-project directory inside the container. Replace this with your actual project directory. Ensure this directory contains all necessary files, including the prompt file.

Using Docker Compose

1. Clone the Repository (if not already done)

   git clone https://github.com/gpt-engineer-org/gpt-engineer.git
   cd gpt-engineer
   

2. Build and Run using Docker Compose

   docker-compose -f docker-compose.yml build
   docker-compose run --rm gpt-engineer
   

   Set the OPENAI_API_KEY in the docker/docker-compose.yml using an .env file or as an environment variable. Mount your project directory to the container using volumes, e.g., "./projects/example:/project" where ./projects/example is the path to your project directory.

3. Another alternative using Docker Compose

   Since there is only one docker-compose.yml file, you could run it without the -f option.

   • docker compose up -d --build - To build and start the containers defined in your docker-compose.yml file in detached mode
   • docker compose up -d - To start the containers defined in your docker-compose.yml file in detached mode
   • docker compose down - To stop and remove all containers, networks, and volumes associated with the docker-compose.yml
   • docker compose restart - To restart the containers defined in the docker-compose.yml file

Debugging

To facilitate debugging, you can run a shell inside the built Docker image:

docker run -it --entrypoint /bin/bash gpt-engineer

This opens a shell inside the Docker container, allowing you to execute commands and inspect the environment manually.