242 lines
8.4 KiB
Docker
242 lines
8.4 KiB
Docker
# syntax=docker/dockerfile:1.6
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# Limit build parallelism to reduce OOM situations
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ARG BUILD_JOBS=16
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# =========================================================
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# STAGE 1: Base Image (Installs Dependencies)
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# =========================================================
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FROM nvcr.io/nvidia/pytorch:26.01-py3 AS base
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# Build parallemism
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ARG BUILD_JOBS
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ENV MAX_JOBS=${BUILD_JOBS}
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ENV CMAKE_BUILD_PARALLEL_LEVEL=${BUILD_JOBS}
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ENV NINJAFLAGS="-j${BUILD_JOBS}"
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ENV MAKEFLAGS="-j${BUILD_JOBS}"
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# Set non-interactive frontend to prevent apt prompts
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ENV DEBIAN_FRONTEND=noninteractive
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# Allow pip to install globally on Ubuntu 24.04 without a venv
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ENV PIP_BREAK_SYSTEM_PACKAGES=1
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# Set pip cache directory
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ENV PIP_CACHE_DIR=/root/.cache/pip
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ENV UV_CACHE_DIR=/root/.cache/uv
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ENV UV_SYSTEM_PYTHON=1
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ENV UV_BREAK_SYSTEM_PACKAGES=1
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ENV UV_LINK_MODE=copy
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# Set the base directory environment variable
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ENV VLLM_BASE_DIR=/workspace/vllm
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# 1. Install Build Dependencies & Ccache
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# Added ccache to enable incremental compilation caching
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RUN apt update && \
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apt install -y --no-install-recommends \
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curl vim ninja-build git \
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ccache \
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&& rm -rf /var/lib/apt/lists/* \
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&& pip install uv && pip uninstall -y flash-attn
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# Configure Ccache for CUDA/C++
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ENV PATH=/usr/lib/ccache:$PATH
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ENV CCACHE_DIR=/root/.ccache
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# Limit ccache size to prevent unbounded growth (e.g. 50G)
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ENV CCACHE_MAXSIZE=50G
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# Enable compression to save space
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ENV CCACHE_COMPRESS=1
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# Tell CMake to use ccache for compilation
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ENV CMAKE_CXX_COMPILER_LAUNCHER=ccache
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ENV CMAKE_CUDA_COMPILER_LAUNCHER=ccache
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# Setup Workspace
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WORKDIR $VLLM_BASE_DIR
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# 2. Set Environment Variables
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ENV TORCH_CUDA_ARCH_LIST=12.1a
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ENV TRITON_PTXAS_PATH=/usr/local/cuda/bin/ptxas
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# --- CACHE BUSTER ---
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# Change this argument to force a re-download of FlashInfer
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ARG CACHEBUST_DEPS=1
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# 3. Install Python Dependencies with Cache Mounts
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# Using --mount=type=cache ensures that even if this layer invalidates,
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# pip reuses previously downloaded wheels.
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# # Install additional dependencies
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# RUN --mount=type=cache,id=uv-cache,target=/root/.cache/uv \
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# uv pip install fastsafetensors
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# # =========================================================
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# # STAGE 2: Triton Builder (Compiles Triton independently)
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# # =========================================================
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# FROM base AS triton-builder
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# WORKDIR $VLLM_BASE_DIR
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# # Initial Triton repo clone (cached forever)
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# RUN git clone https://github.com/triton-lang/triton.git
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# # We expect TRITON_REF to be passed from the command line to break the cache
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# # Set to v3.5.1 tag by default
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# ARG TRITON_REF=v3.6.0
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# WORKDIR $VLLM_BASE_DIR/triton
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# # This only runs if TRITON_REF differs from the last build
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# RUN --mount=type=cache,id=ccache,target=/root/.ccache \
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# --mount=type=cache,id=uv-cache,target=/root/.cache/uv \
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# git fetch origin && \
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# git checkout ${TRITON_REF} && \
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# git submodule sync && \
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# git submodule update --init --recursive && \
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# uv pip install -r python/requirements.txt && \
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# mkdir -p /workspace/wheels && \
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# rm -rf .git && \
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# uv build --no-build-isolation --out-dir=/workspace/wheels -v . && \
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# uv build --no-build-isolation --no-index --out-dir=/workspace/wheels python/triton_kernels
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# =========================================================
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# STAGE 3: vLLM Builder (Builds vLLM from Source)
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# =========================================================
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FROM base AS builder
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# --- VLLM SOURCE CACHE BUSTER ---
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# Change THIS argument to force a fresh git clone and rebuild of vLLM
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# without re-installing the dependencies above.
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ARG CACHEBUST_VLLM=1
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# Git reference (branch, tag, or SHA) to checkout
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ARG VLLM_REF=main
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# 4. Smart Git Clone (Fetch changes instead of full re-clone)
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# We mount a cache at /repo-cache. This directory persists on your host machine.
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RUN --mount=type=cache,id=repo-cache,target=/repo-cache \
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# 1. Go into the persistent cache directory
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cd /repo-cache && \
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# 2. Logic: Clone if missing, otherwise Fetch & Reset
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if [ ! -d "vllm" ]; then \
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echo "Cache miss: Cloning vLLM from scratch..." && \
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git clone --recursive https://github.com/vllm-project/vllm.git; \
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if [ "$VLLM_REF" != "main" ]; then \
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cd vllm && \
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git checkout ${VLLM_REF}; \
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fi; \
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else \
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echo "Cache hit: Fetching updates..." && \
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cd vllm && \
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git fetch --all && \
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git checkout ${VLLM_REF} && \
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if [ "${VLLM_REF}" = "main" ]; then \
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git reset --hard origin/main; \
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fi && \
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git submodule update --init --recursive && \
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# Optimize git repo size
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git gc --auto; \
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fi && \
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# 3. Copy the updated code from the cache to the actual container workspace
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# We use 'cp -a' to preserve permissions
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cp -a /repo-cache/vllm $VLLM_BASE_DIR/
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WORKDIR $VLLM_BASE_DIR/vllm
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ARG PRE_TRANSFORMERS=0
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# Prepare build requirements
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RUN --mount=type=cache,id=uv-cache,target=/root/.cache/uv \
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python3 use_existing_torch.py && \
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sed -i "/flashinfer/d" requirements/cuda.txt && \
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sed -i '/^fastsafetensors\b/d' requirements/test.txt && \
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if [ "$PRE_TRANSFORMERS" = "1" ]; then \
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sed -i '/^transformers\b/d' requirements/common.txt; \
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sed -i '/^transformers\b/d' requirements/test.txt; \
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fi && \
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uv pip install -r requirements/build.txt
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# Apply Patches
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# TEMPORARY PATCH for fastsafetensors loading in cluster setup - tracking https://github.com/foundation-model-stack/fastsafetensors/issues/36
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COPY fastsafetensors.patch .
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RUN patch -p1 < fastsafetensors.patch
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# Final Compilation
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# We mount the ccache directory here. Ideally, map this to a host volume for persistence
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# across totally separate `docker build` invocations.
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RUN --mount=type=cache,id=ccache,target=/root/.ccache \
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--mount=type=cache,id=uv-cache,target=/root/.cache/uv \
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uv build --no-build-isolation --wheel . --out-dir=/workspace/wheels -v
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# # Install custom Triton from triton-builder
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# COPY --from=triton-builder /workspace/wheels /workspace/wheels
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# RUN --mount=type=cache,id=uv-cache,target=/root/.cache/uv \
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# uv pip install /workspace/wheels/*.whl
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# =========================================================
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# STAGE 4: Runner (Transfers only necessary artifacts)
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# =========================================================
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FROM nvcr.io/nvidia/pytorch:26.01-py3 AS runner
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ENV DEBIAN_FRONTEND=noninteractive
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ENV PIP_BREAK_SYSTEM_PACKAGES=1
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ENV VLLM_BASE_DIR=/workspace/vllm
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# Set pip cache directory
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ENV PIP_CACHE_DIR=/root/.cache/pip
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ENV UV_CACHE_DIR=/root/.cache/uv
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ENV UV_SYSTEM_PYTHON=1
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ENV UV_BREAK_SYSTEM_PACKAGES=1
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ENV UV_LINK_MODE=copy
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# Install runtime dependencies
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RUN apt update && \
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apt install -y --no-install-recommends \
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curl vim git \
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libxcb1 \
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&& rm -rf /var/lib/apt/lists/* \
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&& pip install uv && pip uninstall -y flash-attn
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# Set final working directory
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WORKDIR $VLLM_BASE_DIR
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# Download Tiktoken files
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RUN mkdir -p tiktoken_encodings && \
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wget -O tiktoken_encodings/o200k_base.tiktoken "https://openaipublic.blob.core.windows.net/encodings/o200k_base.tiktoken" && \
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wget -O tiktoken_encodings/cl100k_base.tiktoken "https://openaipublic.blob.core.windows.net/encodings/cl100k_base.tiktoken"
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ARG FLASHINFER_PRE=""
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# Install FlashInfer packages
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RUN --mount=type=cache,id=uv-cache,target=/root/.cache/uv \
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uv pip install ${FLASHINFER_PRE} flashinfer-python --no-deps --index-url https://flashinfer.ai/whl && \
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uv pip install ${FLASHINFER_PRE} flashinfer-cubin --index-url https://flashinfer.ai/whl && \
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uv pip install ${FLASHINFER_PRE} flashinfer-jit-cache --index-url https://flashinfer.ai/whl/cu130
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ARG PRE_TRANSFORMERS=0
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RUN --mount=type=cache,id=uv-cache,target=/root/.cache/uv \
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if [ "$PRE_TRANSFORMERS" = "1" ]; then \
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uv pip install -U transformers --pre; \
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fi
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# Copy artifacts from Builder Stage
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COPY --from=builder /workspace/wheels /workspace/wheels
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RUN --mount=type=cache,id=uv-cache,target=/root/.cache/uv \
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uv pip install /workspace/wheels/*.whl
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# Setup Env for Runtime
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ENV TORCH_CUDA_ARCH_LIST=12.1a
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ENV FLASHINFER_CUDA_ARCH_LIST="12.1f"
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ENV TRITON_PTXAS_PATH=/usr/local/cuda/bin/ptxas
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ENV TIKTOKEN_ENCODINGS_BASE=$VLLM_BASE_DIR/tiktoken_encodings
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ENV PATH=$VLLM_BASE_DIR:$PATH
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# Copy scripts
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COPY run-cluster-node.sh $VLLM_BASE_DIR/
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RUN chmod +x $VLLM_BASE_DIR/run-cluster-node.sh
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# Final extra deps
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RUN --mount=type=cache,id=uv-cache,target=/root/.cache/uv \
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uv pip install ray[default] fastsafetensors "apache-tvm-ffi<0.2"
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