vllm/docs/source/contributing/dockerfile/dockerfile.rst

Dockerfile
====================

See `here <https://github.com/vllm-project/vllm/blob/main/Dockerfile>`__ for the main Dockerfile to construct 
the image for running an OpenAI compatible server with vLLM. More information about deploying with Docker can be found `here <https://docs.vllm.ai/en/stable/serving/deploying_with_docker.html>`__.

Below is a visual representation of the multi-stage Dockerfile. The build graph contains the following nodes:

- All build stages
- The default build target (highlighted in grey)
- External images (with dashed borders)
   
The edges of the build graph represent:

- FROM ... dependencies (with a solid line and a full arrow head)
- COPY --from=... dependencies (with a dashed line and an empty arrow head)
- RUN --mount=(.*)from=... dependencies (with a dotted line and an empty diamond arrow head)

   .. figure:: ../../assets/dev/dockerfile-stages-dependency.png
      :alt: query
      :width: 100%
      :align: center

   Made using: https://github.com/patrickhoefler/dockerfilegraph

   Commands to regenerate the build graph (make sure to run it **from the `root` directory of the vLLM repository** where the dockerfile is present):

   .. code:: bash

      dockerfilegraph -o png --legend --dpi 200 --max-label-length 50 --filename Dockerfile

   or in case you want to run it directly with the docker image:
   
   .. code:: bash

      docker run \
         --rm \
         --user "$(id -u):$(id -g)" \
         --workdir /workspace \
         --volume "$(pwd)":/workspace \
         ghcr.io/patrickhoefler/dockerfilegraph:alpine \
         --output png \
         --dpi 200 \
         --max-label-length 50 \
         --filename Dockerfile \
         --legend

   (To run it for a different file, you can pass in a different argument to the flag `--filename`.)