Docker Installation
You can use the official Docker image to run GPUStack in a container. Installation using docker is supported on:
- Linux with Nvidia GPUs
Prerequisites
Run GPUStack with Docker
Run the following command to start the GPUStack server:
docker run -d --gpus all -p 80:80 --ipc=host \
-v gpustack-data:/var/lib/gpustack gpustack/gpustack
Note
You can either use the --ipc=host
flag or --shm-size
flag to allow the container to access the host’s shared memory. It is used by vLLM and pyTorch to share data between processes under the hood, particularly for tensor parallel inference.
You can set additional flags for the gpustack start
command by appending them to the docker run command.
For example, to start a GPUStack worker:
docker run -d --gpus all --ipc=host --network=host \
gpustack/gpustack --server-url http://myserver --token mytoken
Note
The --network=host
flag is used to ensure that server is accessible to the worker and inference services running on it. Alternatively, you can set --worker-ip <host-ip> -p 10150:10150 -p 40000-41024:40000-41024
to expose relevant ports.
For configuration details, please refer to the CLI Reference.
Run GPUStack with Docker Compose
Get the docker-compose file from GPUStack repository, run the following command to start the GPUStack server:
docker-compose up -d
You can update the docker-compose.yml
file to customize the command while starting a GPUStack worker.
Build Your Own Docker Image
The official Docker image is built with CUDA 12.4. If you want to use a different version of CUDA, you can build your own Docker image.
# Example Dockerfile
ARG CUDA_VERSION=12.4.1
FROM nvidia/cuda:$CUDA_VERSION-cudnn-runtime-ubuntu22.04
ENV DEBIAN_FRONTEND=noninteractive
RUN apt-get update && apt-get install -y \
wget \
tzdata \
python3 \
python3-pip \
&& rm -rf /var/lib/apt/lists/*
RUN pip3 install gpustack[all] && \
pip3 cache purge
ENTRYPOINT [ "gpustack", "start" ]
Run the following command to build the Docker image:
docker build -t my/gpustack --build-arg CUDA_VERSION=12.0.0 .