Docker Dev Setup

🐳 Setting Up the JoeyLLM Docker Development Environment

This guide walks you through the base environment setup required to test and develop with the JoeyLLM model. The environment is built using a Docker container that includes all necessary dependencies and is where core development and testing take place.

⚠️ Important: This is the recommended and standardized way to work with JoeyLLM. Running the model in a different environment may lead to inconsistent results, as some parts of the code are dependent on specific configurations present in the container.

By using this base environment:

• We ensure consistency across all contributors and development systems

• We make it easier to scale training and inference across larger infrastructures

• We reduce issues that arise from differing package versions or system libraries

Stick with this setup to avoid frustrating bugs and to ensure your work is compatible with the main JoeyLLM development pipeline.

---

📦 1. Pull the Official JoeyLLM Docker Image

We provide a pre-built Docker image hosted on Docker Hub.

1. Pull the image:

   docker pull southerncrossai/joeyllm

2. Create the Container

   docker create -it --name joeyllm_dev southerncrossai/joeyllm /bin/bash

3. Start/Attach Container

   docker start -ai joeyllm_dev

4. Stop Container (do this once you are finished)

   docker stop joeyllm_dev

---

📂 3. Clone the JoeyLLM Repository

Choose one of the following options to get the code inside your Docker container:

Option A: Clone the Official Test Repo

git clone https://github.com/southern-cross-ai/JoeyLLM.git

Option B: Clone Your Own Fork

Replace the URL with your own GitHub fork if you're contributing changes:

git clone https://github.com/YOUR_USERNAME/JoeyLLM.git

Then navigate into the project folder:

cd JoeyLLM

---

🛠️ 4. Set Up a Python Virtual Environment and Install Dependencies

It's recommended to use a virtual environment to isolate dependencies. Run the following commands inside the container after cloning the repo:

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

This ensures all necessary Python packages for JoeyLLM are installed in a clean, contained environment.

---

🔍 5. Next Steps

In the next phase, we'll begin exploring and testing the contents of the JoeyLLM repository, including:

• Understanding the repo structure
• Running tests
• Making and reviewing changes

This environment setup is your starting point. From here, you’ll be able to contribute, debug, and validate model behavior directly inside a controlled containerized environment.