Dockerize Large Language Models for Any Language without Prebuilding Containers 🚀

Introduction

Large language models (LLMs) are powerful tools that can be used to generate human-like text, answer questions, and even create new content. However, setting up an environment to run these models can be complex and time-consuming, especially when dealing with multiple programming languages or varying hardware configurations. This tutorial will guide you through the process of running LLMs in Docker containers for any language without prebuilding them, leveraging recent advancements like Math-Shepherd, Step-DPO, and FlowSteer.

This approach not only simplifies setup but also makes it easier to experiment with different models and languages, enhancing accessibility and flexibility for developers and researchers alike.

📺 Watch: Intro to Large Language Models

Video by Andrej Karpathy

Prerequisites

Before we start, ensure you have the following installed:

• Python 3.10+ (latest version recommended)
• Docker Engine (version >=24.0)
• Git (version >=2.38)
• pip (Python package installer)

To install Docker and Git on your system, use the following commands:

# Install Docker using official installation guide: https://docs.docker.com/get-docker/
sudo apt-get update
sudo apt-get install docker.io

# Install Git using APT package manager
sudo apt-get install git

Step 1: Project Setup

First, we need to clone a repository containing the necessary Dockerfiles and scripts for our project. This will include configurations for running LLMs in various languages.

git clone https://github.com/example-llm/llm-docker.git
cd llm-docker

Next, install Python dependencies using pip:

pip install --upgrade pip
pip install torch==1.12.0 transformers [6]==4.26.0 datasets==2.9.0

This will ensure you have the latest stable versions of PyTorch [9], Transformers, and Datasets libraries required for LLMs.

Step 2: Core Implementation

The core of our project involves creating a Dockerfile that can run any language’s environment without needing to build a container from scratch each time. We’ll use multi-stage builds and RUN commands inside the Dockerfile to download dependencies dynamically based on language selection.

# Start with an official Python image as the base image
FROM python:3.10-slim

# Set environment variables for flexibility in choosing Python packages
ENV PYTHONUNBUFFERED=TRUE \
    PYTHONDONTWRITEBYTECODE=TRUE

# Install necessary system dependencies
RUN apt-get update && apt-get install -y \
    curl \
    build-essential && \
    rm -rf /var/lib/apt/lists/*

# Copy requirements file and install dependencies from it
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Expose the port your application will run on
EXPOSE 8080

# Set working directory inside container
WORKDIR /app

# Specify entrypoint script (for handling language runtime)
ENTRYPOINT ["python", "entry_point.py"]

In entry_point.py, we dynamically load a specified Python package based on the command-line argument passed when running the Docker image:

import sys
from transformers import AutoModelForCausalLM, AutoTokenizer

def main():
    model_name = sys.argv[1]
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForCausalLM.from_pretrained(model_name)

if __name__ == "__main__":
    main()

Step 3: Configuration

To configure your Docker setup for a specific language or LLM model, modify the requirements.txt file to include necessary Python packages. For example:

torch==1.12.0
transformers==4.26.0
datasets==2.9.0

Also, specify the exact model name in your entrypoint script command when running Docker.

Step 4: Running the Code

To run the LLM container for a specific language or model (e.g., distilgpt [8]2), use:

docker build -t llm-app .
docker run -p 8080:8080 llm-app distilgpt2

Expected output:

INFO: Successfully loaded tokenizer and model.
INFO: Starting application on port 8080...

Troubleshooting:

• Ensure you have internet access to download dependencies during build time.
• Verify that your Docker daemon is running.

Step 5: Advanced Tips

For more efficient setup and faster deployment, consider creating a script that automates the process of building and launching containers for different models. Also, explore using docker-compose to manage multiple services and configurations in complex setups.

Results

By following this tutorial, you have successfully set up Docker containers capable of running large language models without prebuilding them, making it easier to experiment with various models and languages in your development or research environment.

Going Further

• Explore different LLMs available on Hugging Face Model Hub.
• Use docker-compose.yml files for managing multi-service configurations.
• Dive into the Math-Shepherd paper from ArXiv for enhancing verification methods of model outputs without human annotations.

Conclusion

This guide has walked you through creating a flexible Docker setup to run large language models dynamically. With this infrastructure, you can easily switch between different languages and experiment with various LLMs without the hassle of prebuilding containers.

Happy coding!