Dockerfile.vllm

# syntax=docker/dockerfile:1.10.0
# SPDX-FileCopyrightText: Copyright (c) 2024-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0

ARG BASE_IMAGE="nvcr.io/nvidia/cuda-dl-base"
# TODO OPS-612: NCCL will hang with 25.03, so use 25.01 for now
# Please check https://github.com/ai-dynamo/dynamo/pull/1065
# for details and reproducer to manually test if the image
# can be updated to later versions.
ARG BASE_IMAGE_TAG="25.01-cuda12.8-devel-ubuntu24.04"
ARG RELEASE_BUILD
ARG ENABLE_KVBM=false
ARG RUNTIME_IMAGE="nvcr.io/nvidia/cuda"
ARG RUNTIME_IMAGE_TAG="12.8.1-runtime-ubuntu24.04"
ARG CUDA_VERSION="12.8"

# Make sure to update the dependency version in pyproject.toml when updating this
ARG VLLM_REF="v0.10.2"
# FlashInfer only respected when building vLLM from source, ie when VLLM_REF does not start with 'v' or for arm64 builds
ARG FLASHINF_REF="v0.3.0"
ARG TORCH_BACKEND="cu128"

# If left blank, then we will fallback to vLLM defaults
ARG DEEPGEMM_REF=""

# sccache configuration - inherit from base build
ARG USE_SCCACHE
ARG SCCACHE_BUCKET=""
ARG SCCACHE_REGION=""

# Define general architecture ARGs for supporting both x86 and aarch64 builds.
#   ARCH: Used for package suffixes (e.g., amd64, arm64)
#   ARCH_ALT: Used for Rust targets, manylinux suffix (e.g., x86_64, aarch64)
#
# Default values are for x86/amd64:
#   --build-arg ARCH=amd64 --build-arg ARCH_ALT=x86_64
#
# For arm64/aarch64, build with:
#   --build-arg ARCH=arm64 --build-arg ARCH_ALT=aarch64
#
# NOTE: There isn't an easy way to define one of these values based on the other value
# without adding if statements everywhere, so just define both as ARGs for now.
ARG ARCH=amd64
ARG ARCH_ALT=x86_64
# Python configuration
ARG PYTHON_VERSION=3.12

ARG DYNAMO_BASE_IMAGE="dynamo:latest-none"
FROM ${DYNAMO_BASE_IMAGE} AS dynamo_base

########################################################
########## Framework Development Image ################
########################################################
#
# PURPOSE: Framework development and vLLM compilation
#
# This stage builds and compiles framework dependencies including:
# - vLLM inference engine with CUDA support
# - DeepGEMM and FlashInfer optimizations
# - All necessary build tools and compilation dependencies
# - Framework-level Python packages and extensions
#
# Use this stage when you need to:
# - Build vLLM from source with custom modifications
# - Develop or debug framework-level components
# - Create custom builds with specific optimization flags
#

# Use dynamo base image (see /container/Dockerfile for more details)
FROM ${BASE_IMAGE}:${BASE_IMAGE_TAG} AS framework

ARG PYTHON_VERSION

RUN apt-get update -y \
    && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
        # Python runtime - CRITICAL for virtual environment to work
        python${PYTHON_VERSION}-dev \
        build-essential \
        # vLLM build dependencies
        cmake \
        ibverbs-providers \
        ibverbs-utils \
        libibumad-dev \
        libibverbs-dev \
        libnuma-dev \
        librdmacm-dev \
        rdma-core \
    && apt-get clean \
    && rm -rf /var/lib/apt/lists/*

### VIRTUAL ENVIRONMENT SETUP ###

COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/
# Create virtual environment
RUN mkdir -p /opt/dynamo/venv && \
    uv venv /opt/dynamo/venv --python $PYTHON_VERSION

# Activate virtual environment
ENV VIRTUAL_ENV=/opt/dynamo/venv \
    PATH="/opt/dynamo/venv/bin:${PATH}"

ARG ARCH
# Install vllm - keep this early in Dockerfile to avoid
# rebuilds from unrelated source code changes
ARG VLLM_REF
ARG VLLM_GIT_URL
ARG DEEPGEMM_REF
ARG FLASHINF_REF
ARG TORCH_BACKEND
ARG CUDA_VERSION

ARG MAX_JOBS=16
ENV MAX_JOBS=$MAX_JOBS
ENV CUDA_HOME=/usr/local/cuda

# Install sccache if requested
COPY container/use-sccache.sh /tmp/use-sccache.sh
# Install sccache if requested
ARG USE_SCCACHE
ARG ARCH_ALT
ARG SCCACHE_BUCKET
ARG SCCACHE_REGION

ENV ARCH_ALT=${ARCH_ALT}
RUN if [ "$USE_SCCACHE" = "true" ]; then \
        /tmp/use-sccache.sh install; \
    fi

# Set environment variables - they'll be empty strings if USE_SCCACHE=false
ENV SCCACHE_BUCKET=${USE_SCCACHE:+${SCCACHE_BUCKET}} \
    SCCACHE_REGION=${USE_SCCACHE:+${SCCACHE_REGION}} \
    SCCACHE_S3_KEY_PREFIX=${USE_SCCACHE:+${ARCH}} \
    CMAKE_C_COMPILER_LAUNCHER=${USE_SCCACHE:+sccache} \
    CMAKE_CXX_COMPILER_LAUNCHER=${USE_SCCACHE:+sccache} \
    CMAKE_CUDA_COMPILER_LAUNCHER=${USE_SCCACHE:+sccache}
# Install VLLM and related dependencies
RUN --mount=type=bind,source=./container/deps/,target=/tmp/deps \
    --mount=type=cache,target=/root/.cache/uv \
    --mount=type=secret,id=aws-key-id,env=AWS_ACCESS_KEY_ID \
    --mount=type=secret,id=aws-secret-id,env=AWS_SECRET_ACCESS_KEY \
        cp /tmp/deps/vllm/install_vllm.sh /tmp/install_vllm.sh && \
        chmod +x /tmp/install_vllm.sh && \
        /tmp/install_vllm.sh --editable --vllm-ref $VLLM_REF --max-jobs $MAX_JOBS --arch $ARCH --installation-dir /opt ${DEEPGEMM_REF:+--deepgemm-ref "$DEEPGEMM_REF"} ${FLASHINF_REF:+--flashinf-ref "$FLASHINF_REF"} --torch-backend $TORCH_BACKEND --cuda-version $CUDA_VERSION && \
        /tmp/use-sccache.sh show-stats "vLLM";

ENV LD_LIBRARY_PATH=\
/opt/vllm/tools/ep_kernels/ep_kernels_workspace/nvshmem_install/lib:\
$LD_LIBRARY_PATH

##################################################
########## Runtime Image ########################
##################################################
#
# PURPOSE: Production runtime environment
#
# This stage creates a lightweight production-ready image containing:
# - Pre-compiled vLLM and framework dependencies
# - Dynamo runtime libraries and Python packages
# - Essential runtime dependencies and configurations
# - Optimized for inference workloads and deployment
#
# Use this stage when you need:
# - Production deployment of Dynamo with vLLM
# - Minimal runtime footprint without build tools
# - Ready-to-run inference server environment
# - Base for custom application containers
#

FROM ${RUNTIME_IMAGE}:${RUNTIME_IMAGE_TAG} AS runtime

WORKDIR /workspace
ENV DYNAMO_HOME=/opt/dynamo
ENV VIRTUAL_ENV=/opt/dynamo/venv
ENV PATH="${VIRTUAL_ENV}/bin:${PATH}"

ARG ARCH_ALT
ARG PYTHON_VERSION
ENV NIXL_PREFIX=/opt/nvidia/nvda_nixl
ENV NIXL_LIB_DIR=$NIXL_PREFIX/lib/${ARCH_ALT}-linux-gnu
ENV NIXL_PLUGIN_DIR=$NIXL_LIB_DIR/plugins

# Install Python, build-essential and python3-dev as apt dependencies
RUN apt-get update && \
    DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
        # Python runtime - CRITICAL for virtual environment to work
        python${PYTHON_VERSION}-dev \
        build-essential \
        # jq and curl for polling various endpoints and health checks
        jq \
        curl \
        # Libraries required by UCX to find RDMA devices
        libibverbs1 rdma-core ibverbs-utils libibumad3 \
        libnuma1 librdmacm1 ibverbs-providers \
        # JIT Kernel Compilation, flashinfer
        ninja-build \
        g++ \
        # prometheus dependencies
        ca-certificates \
        # DeepGemm uses 'cuobjdump' which does not come with CUDA image
        cuda-command-line-tools-12-8 && \
    rm -rf /var/lib/apt/lists/*

# Copy CUDA development tools (nvcc, headers, dependencies, etc.) from base devel image
COPY --from=framework /usr/local/cuda/bin/nvcc /usr/local/cuda/bin/nvcc
COPY --from=framework /usr/local/cuda/bin/cudafe++ /usr/local/cuda/bin/cudafe++
COPY --from=framework /usr/local/cuda/bin/ptxas /usr/local/cuda/bin/ptxas
COPY --from=framework /usr/local/cuda/bin/fatbinary /usr/local/cuda/bin/fatbinary
COPY --from=framework /usr/local/cuda/include/ /usr/local/cuda/include/
COPY --from=framework /usr/local/cuda/nvvm /usr/local/cuda/nvvm
COPY --from=framework /usr/local/cuda/lib64/libcudart.so* /usr/local/cuda/lib64/

### COPY NATS & ETCD ###
# Copy nats and etcd from dev image
COPY --from=dynamo_base /usr/bin/nats-server /usr/bin/nats-server
COPY --from=dynamo_base /usr/local/bin/etcd/ /usr/local/bin/etcd/
# Add ETCD and CUDA binaries to PATH so cicc and other CUDA tools are accessible
ENV PATH=/usr/local/bin/etcd/:/usr/local/cuda/nvvm/bin:$PATH

# Copy UCX from dev image as plugin for NIXL
# Copy NIXL source from devr image
# Copy dynamo wheels for gitlab artifacts
COPY --from=dynamo_base /usr/local/ucx /usr/local/ucx
COPY --from=dynamo_base $NIXL_PREFIX $NIXL_PREFIX

# Copies vllm, DeepEP, DeepGEMM, PPLX repos (all editable installs) and nvshmem binaries
COPY --from=framework /opt/vllm /opt/vllm

ENV LD_LIBRARY_PATH=\
/opt/vllm/tools/ep_kernels/ep_kernels_workspace/nvshmem_install/lib:\
$NIXL_LIB_DIR:\
$NIXL_PLUGIN_DIR:\
/usr/local/ucx/lib:\
/usr/local/ucx/lib/ucx:\
$LD_LIBRARY_PATH

# DeepGemm runs nvcc for JIT kernel compilation, however the CUDA include path
# is not properly set for complilation. Set CPATH to help nvcc find the headers.
ENV CPATH=/usr/local/cuda/include:$CPATH

### VIRTUAL ENVIRONMENT SETUP ###

# Copy uv and entire virtual environment from framework container
COPY --from=framework /bin/uv /bin/uvx /bin/
COPY --from=framework ${VIRTUAL_ENV} ${VIRTUAL_ENV}

# Install dynamo, NIXL, and dynamo-specific dependencies
COPY benchmarks/ /opt/dynamo/benchmarks/
COPY --from=dynamo_base /opt/dynamo/wheelhouse/ /opt/dynamo/wheelhouse/
RUN uv pip install \
    /opt/dynamo/wheelhouse/ai_dynamo_runtime*cp312*.whl \
    /opt/dynamo/wheelhouse/ai_dynamo*any.whl \
    /opt/dynamo/wheelhouse/nixl/nixl*.whl \
    /opt/dynamo/benchmarks && \
    rm -rf /opt/dynamo/benchmarks

# Install common and test dependencies
RUN --mount=type=bind,source=./container/deps/requirements.txt,target=/tmp/requirements.txt \
    --mount=type=bind,source=./container/deps/requirements.test.txt,target=/tmp/requirements.test.txt \
    uv pip install --requirement /tmp/requirements.txt --requirement /tmp/requirements.test.txt

# Copy benchmarks, examples, and tests for CI
COPY . /workspace/

# Copy attribution files
COPY ATTRIBUTION* LICENSE /workspace/
# Copy launch banner
RUN --mount=type=bind,source=./container/launch_message.txt,target=/workspace/launch_message.txt \
    sed '/^#\s/d' /workspace/launch_message.txt > ~/.launch_screen && \
    echo "cat ~/.launch_screen" >> ~/.bashrc && \
    echo "source $VIRTUAL_ENV/bin/activate" >> ~/.bashrc

ENTRYPOINT ["/opt/nvidia/nvidia_entrypoint.sh"]
CMD []

###########################################################
########## Development (run.sh, runs as root user) ########
###########################################################
#
# PURPOSE: Local development environment for use with run.sh (not Dev Container plug-in)
#
# This stage runs as root and provides:
# - Development tools and utilities for local debugging
# - Support for vscode/cursor development outside the Dev Container plug-in
#
# Use this stage if you need a full-featured development environment with extra tools,
# but do not use it with the Dev Container plug-in.

FROM runtime AS dev

# Don't want ubuntu to be editable, just change uid and gid.
ARG WORKSPACE_DIR=/workspace

# Install utilities as root
RUN apt-get update -y && \
    apt-get install -y --no-install-recommends  \
    # Install utilities
    nvtop \
    wget \
    tmux \
    vim \
    git \
    openssh-client \
    iproute2 \
    rsync \
    zip \
    unzip \
    htop \
    # Build Dependencies
    autoconf \
    automake \
    cmake \
    libtool \
    meson \
    net-tools \
    pybind11-dev \
    # Rust build dependencies
    clang \
    libclang-dev \
    protobuf-compiler && \
    rm -rf /var/lib/apt/lists/*

COPY --from=runtime /usr/local/bin /usr/local/bin

# Set workspace directory variable
ENV WORKSPACE_DIR=${WORKSPACE_DIR} \
    DYNAMO_HOME=${WORKSPACE_DIR} \
    RUSTUP_HOME=/usr/local/rustup \
    CARGO_HOME=/usr/local/cargo \
    CARGO_TARGET_DIR=/workspace/target \
    VIRTUAL_ENV=/opt/dynamo/venv \
    PATH=/usr/local/cargo/bin:$PATH

COPY --from=dynamo_base /usr/local/rustup /usr/local/rustup
COPY --from=dynamo_base /usr/local/cargo /usr/local/cargo

# This is a slow operation (~40s on my cpu)
# Much better than chown -R $USERNAME:$USERNAME /opt/dynamo/venv (~10min on my cpu)
COPY --from=runtime ${VIRTUAL_ENV} ${VIRTUAL_ENV}

# so we can use maturin develop
RUN uv pip install maturin[patchelf]

# Make sure to sync this with the one specified on README.md.
# This is a generic PYTHONPATH which works for all the frameworks, so some paths may not be relevant for this particular framework.
ENV PYTHONPATH=${WORKSPACE_DIR}/components/metrics/src:${WORKSPACE_DIR}/components/frontend/src:${WORKSPACE_DIR}/components/planner/src:${WORKSPACE_DIR}/components/backends/mocker/src:${WORKSPACE_DIR}/components/backends/trtllm/src:${WORKSPACE_DIR}/components/backends/vllm/src:${WORKSPACE_DIR}/components/backends/sglang/src:${WORKSPACE_DIR}/components/backends/llama_cpp/src

ENTRYPOINT ["/opt/nvidia/nvidia_entrypoint.sh"]
CMD []