mirror of https://github.com/vllm-project/vllm
137 lines
4.6 KiB
Docker
137 lines
4.6 KiB
Docker
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
|
|
# to run the OpenAI compatible server.
|
|
|
|
# Please update any changes made here to
|
|
# docs/source/dev/dockerfile/dockerfile.rst and
|
|
# docs/source/assets/dev/dockerfile-stages-dependency.png
|
|
|
|
#################### BASE BUILD IMAGE ####################
|
|
# prepare basic build environment
|
|
FROM nvidia/cuda:12.4.1-devel-ubuntu22.04 AS dev
|
|
|
|
RUN apt-get update -y \
|
|
&& apt-get install -y python3-pip git
|
|
|
|
# Workaround for https://github.com/openai/triton/issues/2507 and
|
|
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
|
|
# this won't be needed for future versions of this docker image
|
|
# or future versions of triton.
|
|
RUN ldconfig /usr/local/cuda-12.4/compat/
|
|
|
|
WORKDIR /workspace
|
|
|
|
# install build and runtime dependencies
|
|
COPY requirements-common.txt requirements-common.txt
|
|
COPY requirements-cuda.txt requirements-cuda.txt
|
|
RUN --mount=type=cache,target=/root/.cache/pip \
|
|
pip install -r requirements-cuda.txt
|
|
|
|
# install development dependencies
|
|
COPY requirements-dev.txt requirements-dev.txt
|
|
RUN --mount=type=cache,target=/root/.cache/pip \
|
|
pip install -r requirements-dev.txt
|
|
|
|
# cuda arch list used by torch
|
|
# can be useful for both `dev` and `test`
|
|
# explicitly set the list to avoid issues with torch 2.2
|
|
# see https://github.com/pytorch/pytorch/pull/123243
|
|
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
|
|
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
|
|
#################### BASE BUILD IMAGE ####################
|
|
|
|
|
|
#################### WHEEL BUILD IMAGE ####################
|
|
FROM dev AS build
|
|
|
|
# install build dependencies
|
|
COPY requirements-build.txt requirements-build.txt
|
|
RUN --mount=type=cache,target=/root/.cache/pip \
|
|
pip install -r requirements-build.txt
|
|
|
|
# install compiler cache to speed up compilation leveraging local or remote caching
|
|
RUN apt-get update -y && apt-get install -y ccache
|
|
|
|
# files and directories related to build wheels
|
|
COPY csrc csrc
|
|
COPY setup.py setup.py
|
|
COPY cmake cmake
|
|
COPY CMakeLists.txt CMakeLists.txt
|
|
COPY requirements-common.txt requirements-common.txt
|
|
COPY requirements-cuda.txt requirements-cuda.txt
|
|
COPY pyproject.toml pyproject.toml
|
|
COPY vllm vllm
|
|
|
|
# max jobs used by Ninja to build extensions
|
|
ARG max_jobs=2
|
|
ENV MAX_JOBS=${max_jobs}
|
|
# number of threads used by nvcc
|
|
ARG nvcc_threads=8
|
|
ENV NVCC_THREADS=$nvcc_threads
|
|
# make sure punica kernels are built (for LoRA)
|
|
ENV VLLM_INSTALL_PUNICA_KERNELS=1
|
|
|
|
ENV CCACHE_DIR=/root/.cache/ccache
|
|
RUN --mount=type=cache,target=/root/.cache/ccache \
|
|
--mount=type=cache,target=/root/.cache/pip \
|
|
python3 setup.py bdist_wheel --dist-dir=dist
|
|
|
|
# check the size of the wheel, we cannot upload wheels larger than 100MB
|
|
COPY .buildkite/check-wheel-size.py check-wheel-size.py
|
|
RUN python3 check-wheel-size.py dist
|
|
|
|
#################### EXTENSION Build IMAGE ####################
|
|
|
|
#################### vLLM installation IMAGE ####################
|
|
# image with vLLM installed
|
|
FROM nvidia/cuda:12.4.1-base-ubuntu22.04 AS vllm-base
|
|
WORKDIR /vllm-workspace
|
|
|
|
RUN apt-get update -y \
|
|
&& apt-get install -y python3-pip git vim
|
|
|
|
# Workaround for https://github.com/openai/triton/issues/2507 and
|
|
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
|
|
# this won't be needed for future versions of this docker image
|
|
# or future versions of triton.
|
|
RUN ldconfig /usr/local/cuda-12.4/compat/
|
|
|
|
# install vllm wheel first, so that torch etc will be installed
|
|
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
|
|
--mount=type=cache,target=/root/.cache/pip \
|
|
pip install dist/*.whl --verbose
|
|
#################### vLLM installation IMAGE ####################
|
|
|
|
|
|
#################### TEST IMAGE ####################
|
|
# image to run unit testing suite
|
|
# note that this uses vllm installed by `pip`
|
|
FROM vllm-base AS test
|
|
|
|
ADD . /vllm-workspace/
|
|
|
|
# install development dependencies (for testing)
|
|
RUN --mount=type=cache,target=/root/.cache/pip \
|
|
pip install -r requirements-dev.txt
|
|
|
|
# doc requires source code
|
|
# we hide them inside `test_docs/` , so that this source code
|
|
# will not be imported by other tests
|
|
RUN mkdir test_docs
|
|
RUN mv docs test_docs/
|
|
RUN mv vllm test_docs/
|
|
|
|
#################### TEST IMAGE ####################
|
|
|
|
#################### OPENAI API SERVER ####################
|
|
# openai api server alternative
|
|
FROM vllm-base AS vllm-openai
|
|
|
|
# install additional dependencies for openai api server
|
|
RUN --mount=type=cache,target=/root/.cache/pip \
|
|
pip install accelerate hf_transfer modelscope
|
|
|
|
ENV VLLM_USAGE_SOURCE production-docker-image
|
|
|
|
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
|
|
#################### OPENAI API SERVER ####################
|