software-engineering/spark-vllm-docker
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spark-vllm-docker/Dockerfile

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# syntax=docker/dockerfile:1.6
# =========================================================
# STAGE 1: Base Image (Installs Dependencies)
# =========================================================
FROM nvidia/cuda:13.0.2-devel-ubuntu24.04 AS base
# Set non-interactive frontend to prevent apt prompts
ENV DEBIAN_FRONTEND=noninteractive
# Allow pip to install globally on Ubuntu 24.04 without a venv
ENV PIP_BREAK_SYSTEM_PACKAGES=1
# Set the base directory environment variable
ENV VLLM_BASE_DIR=/workspace/vllm
# 1. Install Build Dependencies & Ccache
# Added ccache to enable incremental compilation caching
RUN apt update && apt upgrade -y \
&& apt install -y --allow-change-held-packages --no-install-recommends \
curl vim cmake build-essential ninja-build \
libcudnn9-cuda-13 libcudnn9-dev-cuda-13 \
python3-dev python3-pip git wget \
libnccl-dev libnccl2 libibverbs1 libibverbs-dev rdma-core \
ccache \
&& rm -rf /var/lib/apt/lists/*
# Configure Ccache for CUDA/C++
ENV PATH=/usr/lib/ccache:$PATH
ENV CCACHE_DIR=/root/.ccache
# Tell CMake to use ccache for compilation
ENV CMAKE_CXX_COMPILER_LAUNCHER=ccache
ENV CMAKE_CUDA_COMPILER_LAUNCHER=ccache
# Setup Workspace
WORKDIR $VLLM_BASE_DIR
# 2. Set Environment Variables
ENV TORCH_CUDA_ARCH_LIST=12.1a
ENV TRITON_PTXAS_PATH=/usr/local/cuda/bin/ptxas
# --- CACHE BUSTER ---
# Change this argument to force a re-download of PyTorch/FlashInfer
ARG CACHEBUST_DEPS=1
# 3. Install Python Dependencies with Cache Mounts
# Using --mount=type=cache ensures that even if this layer invalidates,
# pip reuses previously downloaded wheels.
# Set pip cache directory
ENV PIP_CACHE_DIR=/root/.cache/pip
RUN --mount=type=cache,id=pip-cache,target=/root/.cache/pip \
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu130
# Install additional dependencies
RUN --mount=type=cache,id=pip-cache,target=/root/.cache/pip \
pip install xgrammar fastsafetensors
# Install FlashInfer packages
RUN --mount=type=cache,id=pip-cache,target=/root/.cache/pip \
pip install flashinfer-python --no-deps --index-url https://flashinfer.ai/whl && \
pip install flashinfer-cubin --index-url https://flashinfer.ai/whl && \
pip install flashinfer-jit-cache --index-url https://flashinfer.ai/whl/cu130 && \
pip install apache-tvm-ffi nvidia-cudnn-frontend nvidia-cutlass-dsl nvidia-ml-py tabulate
# =========================================================
# STAGE 2: Triton Builder (Compiles Triton independently)
# =========================================================
FROM base AS triton-builder
WORKDIR $VLLM_BASE_DIR
# Initial Triton repo clone (cached forever)
RUN git clone https://github.com/triton-lang/triton.git
# We expect TRITON_SHA to be passed from the command line to break the cache
# Set to v3.5.1 commit by default
ARG TRITON_SHA=0add68262ab0a2e33b84524346cb27cbb2787356
WORKDIR $VLLM_BASE_DIR/triton
# This only runs if TRITON_SHA differs from the last build
RUN --mount=type=cache,id=ccache,target=/root/.ccache \
--mount=type=cache,id=pip-cache,target=/root/.cache/pip \
git fetch origin && \
git checkout ${TRITON_SHA} && \
git submodule sync && \
git submodule update --init --recursive && \
pip install -r python/requirements.txt && \
mkdir -p /workspace/wheels && \
pip wheel --no-build-isolation . --wheel-dir=/workspace/wheels -v && \
pip wheel --no-build-isolation python/triton_kernels --no-deps --wheel-dir=/workspace/wheels
# =========================================================
# STAGE 3: vLLM Builder (Builds vLLM from Source)
# =========================================================
FROM base AS builder
# --- VLLM SOURCE CACHE BUSTER ---
# Change THIS argument to force a fresh git clone and rebuild of vLLM
# without re-installing the dependencies above.
ARG CACHEBUST_VLLM=1
# 4. Smart Git Clone (Fetch changes instead of full re-clone)
# We mount a cache at /repo-cache. This directory persists on your host machine.
RUN --mount=type=cache,id=repo-cache,target=/repo-cache \
# 1. Go into the persistent cache directory
cd /repo-cache && \
# 2. Logic: Clone if missing, otherwise Fetch & Reset
if [ ! -d "vllm" ]; then \
echo "Cache miss: Cloning vLLM from scratch..." && \
git clone --recursive https://github.com/vllm-project/vllm.git; \
else \
echo "Cache hit: Fetching updates..." && \
cd vllm && \
git fetch --all && \
git reset --hard origin/main && \
git submodule update --init --recursive; \
fi && \
# 3. Copy the updated code from the cache to the actual container workspace
# We use 'cp -a' to preserve permissions
cp -a /repo-cache/vllm $VLLM_BASE_DIR/
WORKDIR $VLLM_BASE_DIR/vllm
# Prepare build requirements
RUN --mount=type=cache,id=pip-cache,target=/root/.cache/pip \
python3 use_existing_torch.py && \
sed -i "/flashinfer/d" requirements/cuda.txt && \
sed -i '/^triton\b/d' requirements/test.txt && \
sed -i '/^fastsafetensors\b/d' requirements/test.txt && \
pip install -r requirements/build.txt
# Apply Patches
# TEMPORARY PATCH for fastsafetensors loading in cluster setup - tracking https://github.com/foundation-model-stack/fastsafetensors/issues/36
COPY fastsafetensors.patch .
RUN patch -p1 < fastsafetensors.patch
# Final Compilation
# We mount the ccache directory here. Ideally, map this to a host volume for persistence
# across totally separate `docker build` invocations.
RUN --mount=type=cache,id=ccache,target=/root/.ccache \
--mount=type=cache,id=pip-cache,target=/root/.cache/pip \
pip install --no-build-isolation . -v
# Install custom Triton from triton-builder
COPY --from=triton-builder /workspace/wheels /workspace/wheels
RUN --mount=type=cache,id=pip-cache,target=/root/.cache/pip \
pip install /workspace/wheels/*.whl
# =========================================================
# STAGE 4: Runner (Transfers only necessary artifacts)
# =========================================================
FROM nvidia/cuda:13.0.2-devel-ubuntu24.04 AS runner
ENV DEBIAN_FRONTEND=noninteractive
ENV PIP_BREAK_SYSTEM_PACKAGES=1
ENV VLLM_BASE_DIR=/workspace/vllm
# Set pip cache directory
ENV PIP_CACHE_DIR=/root/.cache/pip
# Install minimal runtime dependencies (NCCL, Python)
# Note: "devel" tools like cmake/gcc are NOT installed here to save space
RUN apt update && apt upgrade -y \
&& apt install -y --allow-change-held-packages --no-install-recommends \
python3 python3-pip python3-dev vim curl git wget \
libcudnn9-cuda-13 \
libnccl-dev libnccl2 libibverbs1 libibverbs-dev rdma-core \
&& rm -rf /var/lib/apt/lists/*
# Set final working directory
WORKDIR $VLLM_BASE_DIR
# Download Tiktoken files
RUN mkdir -p tiktoken_encodings && \
wget -O tiktoken_encodings/o200k_base.tiktoken "https://openaipublic.blob.core.windows.net/encodings/o200k_base.tiktoken" && \
wget -O tiktoken_encodings/cl100k_base.tiktoken "https://openaipublic.blob.core.windows.net/encodings/cl100k_base.tiktoken"
# Copy artifacts from Builder Stage
# We copy the python packages and executables
# No need to copy source code, as it's already in the site-packages
COPY --from=builder /usr/local/lib/python3.12/dist-packages /usr/local/lib/python3.12/dist-packages
COPY --from=builder /usr/local/bin /usr/local/bin
# Setup Env for Runtime
ENV TORCH_CUDA_ARCH_LIST=12.1a
ENV TRITON_PTXAS_PATH=/usr/local/cuda/bin/ptxas
ENV TIKTOKEN_ENCODINGS_BASE=$VLLM_BASE_DIR/tiktoken_encodings
ENV PATH=$VLLM_BASE_DIR:$PATH
# Copy scripts
COPY run-cluster-node.sh $VLLM_BASE_DIR/
RUN chmod +x $VLLM_BASE_DIR/run-cluster-node.sh
# Final extra deps
RUN --mount=type=cache,id=pip-cache,target=/root/.cache/pip \
pip install ray[default]
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