How to Build a vLLM Container Image?

vLLM (Very Large Language Model) is an optimized inference engine designed for running efficiently large-scale transformer-based models. vLLM achieves high throughput with reduced latency through advanced memory management techniques such as continuous batching and paged attention. Building a container image for vLLM facilitates its deployment, scalability, and reproducibility. On a local machine, Kubernetes cluster, or cloud environment, deploying a containerized vLLM setup would ease the setup and make it consistent across different platforms.

This guide will walk you through a step-by-step process of building a vLLM container image using Docker!

Why Build a vLLM Container Image?

Portability – The environment stays the same on different machines and cloud platforms with a containerized vLLM setup.
Scalability – A Containerized Deployment of vLLM would allow easy horizontal scaling via Kubernetes or Docker Swarm.
Simplified Dependency Management – Containers encapsulate dependencies and promote easier installation and maintenance.
Reproducibility – An already defined container image guarantees consistent model running and decreases unexpected errors.
Ease of Deployment – Deploying vLLM via a container facilitates smooth integrations with cloud services such as AWZ, Google Cloud, and Azure.

Prerequisites to Build a vLLM Container Image

Before you begin, make sure you have the following installed on your system!
Docker
NVIDIA Container Toolkit in case of GPU acceleration
Git
Proper Understanding of Docker and containerization.

Steps to Build a vLLM Container Image

Here is the step-wise tutorial to build a vLLM container image!

Step 1: Clone the vLLM Repository
The primary step is to clone the vLLM repository from GitHub.

git clone https://github.com/vllm-project/vllm.git
cd vllm

Step 2: Dockerfile Creation
One needs to create a Dockerfile inside the vLLM directory.

touch Dockerfile
nano Dockerfile

Then, simply add the content to the Dockerfile.

# Use an official deep learning base image with CUDA support
FROM nvidia/cuda:11.8.0-devel-ubuntu20.04

# Set environment variables
ENV DEBIAN_FRONTEND=noninteractive

# Install required dependencies
RUN apt update && apt install -y \
python3 python3-pip git \
&& rm -rf /var/lib/apt/lists/*

# Set Python3 as default
RUN ln -s /usr/bin/python3 /usr/bin/python

# Create a working directory
WORKDIR /app

# Install vLLM
RUN git clone https://github.com/vllm-project/vllm.git /app/vllm \
&& pip install –upgrade pip \
&& pip install -e /app/vllm

# Expose the necessary ports (e.g., 8000 for REST API)
EXPOSE 8000

# Set the entrypoint
CMD [“python3”, “-m”, “vllm.entrypoints.openai.api_server”]

Step 3: Create a Docker Image
The next step is to build the vLLM container image with the help of Docker.

docker build -t vllm:latest

Step 4: Run the vLLM Container
Once you build the container, you can easily run the vLLM container.

docker run --gpus all -p 8000:8000 vllm:latest

if you are using the CPU environment, remove all the –gpus all flags.

docker run -p 8000:8000 vllm:latest

Step 5: Check Deployment
Verify that vLLM is running correctly by running the following command:

curl http://localhost:8000/v1/models

It will display a return response indicating that the vLLM service runs efficiently.

Step 6: Push the Image to the Docker Hub
Push the image to the Docker Hub to share or deploy it in the cloud environment.

docker tag vllm:latest yourdockerhubusername/vllm:latest
docker login
docker push yourdockerhubusername/vllm:latest

Conclusion

Once you have successfully built the vLLM container image through this guide, the advantages of containerizing vLLM include portability, scalability, and easy deployments. Suppose you run it on your machine, some server, or the cloud. Docker has made your life easy by ensuring your environment is consistent and easy to manage! Now that the image is ready, integrate vLLM into your AI pipelines, deploy it as a REST API, or scale it to multiple GPUs with Kubernetes.

Frequently Asked Questions

Is it possible to run a vLLM on CPU-only machines and GPUs?
Yes, vLLM can run on a CPU and is optimized for GPUs. One can remove – -gpus all flags while running the container for CPU usage.

How can I deploy vLLM on Kubernetes?
Yes, one can easily deploy vLLM with the help of Kubernetes deployment file and expose it using a service. One can even leverage Helm charts for easier deployment.

What if I need a specific vLLM version?
One can change the Dockerfile to ensure a specific Git commit or branch before building the image:

RUN git clone –branch <specific-branch> https://github.com/vllm-project/vllm.git /app/vllm

Is it possible to customize the vLLM container image?
Yes, one can easily modify the Dockerfile and include the additional dependencies, libraries, and configurations as required.

Can I easily update the container image?
One can easily update the container image by pulling the latest changes from the vLLM repository and building the image:

git pull origin main
docker build -t vllm:latest .