Switch to NVIDIA NGC PyTorch 26.03 base image (PyTorch 2.11.0a0, CUDA 13.2.0, ARM SBSA support)

This commit is contained in:
2026-04-03 08:44:36 +00:00
parent 54e609b2c5
commit c92c4ec68a
2 changed files with 60 additions and 79 deletions

View File

@@ -1,68 +1,56 @@
ARG CUDA_VERSION=12.8.1
ARG IMAGE_DISTRO=ubuntu24.04
ARG PYTHON_VERSION=3.12
# ---------- Builder Base ----------
FROM nvcr.io/nvidia/cuda:${CUDA_VERSION}-devel-${IMAGE_DISTRO} AS base
# Using NVIDIA NGC PyTorch container (26.03) with:
# - PyTorch 2.11.0a0 (bleeding edge)
# - CUDA 13.2.0
# - cuDNN 9.20, NCCL 2.29.7, TensorRT 10.16, TransformerEngine 2.13
# - Multi-arch: x86 + ARM SBSA (GH200 support)
FROM nvcr.io/nvidia/pytorch:26.03 AS base
# Set arch lists for all targets
# 'a' suffix is not forward compatible but enables all optimizations
ARG TORCH_CUDA_ARCH_LIST="9.0a"
ENV TORCH_CUDA_ARCH_LIST=${TORCH_CUDA_ARCH_LIST}
ENV UV_TORCH_BACKEND=cu128
ARG VLLM_FA_CMAKE_GPU_ARCHES="90a-real"
ENV VLLM_FA_CMAKE_GPU_ARCHES=${VLLM_FA_CMAKE_GPU_ARCHES}
# Update apt packages and install dependencies
# Install additional build dependencies
ENV DEBIAN_FRONTEND=noninteractive
RUN apt update
RUN apt upgrade -y
RUN apt install -y --no-install-recommends \
RUN apt update && apt install -y --no-install-recommends \
curl \
git \
libibverbs-dev \
zlib1g-dev \
libnuma-dev
# Clean apt cache
RUN apt clean
RUN rm -rf /var/lib/apt/lists/*
RUN rm -rf /var/cache/apt/archives
libnuma-dev \
wget \
&& apt clean \
&& rm -rf /var/lib/apt/lists/* /var/cache/apt/archives
# Set compiler paths
ENV CC=/usr/bin/gcc
ENV CXX=/usr/bin/g++
ENV QEMU_CPU=max
# Install uv
# Install uv for faster package management
RUN curl -LsSf https://astral.sh/uv/install.sh | env UV_INSTALL_DIR=/usr/local/bin sh
# Setup build workspace
WORKDIR /workspace
# Prep build venv
ARG PYTHON_VERSION
RUN uv venv -p ${PYTHON_VERSION} --seed --python-preference only-managed
ENV VIRTUAL_ENV=/workspace/.venv
ENV PATH=${VIRTUAL_ENV}/bin:${PATH}
# Environment setup (PyTorch container already has CUDA paths set)
ENV CUDA_HOME=/usr/local/cuda
ENV LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}
ENV CPLUS_INCLUDE_PATH=${CUDA_HOME}/include/cccl
ENV C_INCLUDE_PATH=${CUDA_HOME}/include/cccl
ENV PATH=${CUDA_HOME}/cuda/bin:${PATH}
RUN apt-get update && apt install -y wget
RUN uv pip install numpy==2.0.0
# Install PyTorch nightly with CUDA 13.0 (bleeding edge)
RUN uv pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu130
# Use the Python environment from the container
# The NGC container already has a working Python/PyTorch setup
FROM base AS build-base
RUN mkdir /wheels
# Install build deps that aren't in project requirements files
# Pin setuptools to <81 for LMCache compatibility (needs >=77.0.3,<81.0.0)
RUN uv pip install -U build cmake ninja pybind11 "setuptools>=77.0.3,<81.0.0" wheel
RUN pip install -U build cmake ninja pybind11 "setuptools>=77.0.3,<81.0.0" wheel
# Use PyPI triton wheel instead of building (QEMU segfaults during triton build)
FROM build-base AS build-triton
@@ -76,19 +64,19 @@ RUN mkdir -p /wheels && \
# RUN cd xformers && \
# git submodule sync && \
# git submodule update --init --recursive -j 8 && \
# MAX_JOBS=8 uv build --wheel --no-build-isolation -o /wheels
# MAX_JOBS=8 pip build --wheel --no-build-isolation -o /wheels
FROM build-base AS build-flashinfer
ARG FLASHINFER_ENABLE_AOT=1
ARG FLASHINFER_REF=v0.6.6
ARG FLASHINFER_BUILD_SUFFIX=cu130
ARG FLASHINFER_BUILD_SUFFIX=cu132
ENV FLASHINFER_LOCAL_VERSION=${FLASHINFER_BUILD_SUFFIX:-}
RUN git clone https://github.com/flashinfer-ai/flashinfer.git
RUN cd flashinfer && \
git checkout ${FLASHINFER_REF} && \
git submodule sync && \
git submodule update --init --recursive -j 8 && \
uv build --wheel --no-build-isolation -o /wheels
pip build --wheel --no-build-isolation -o /wheels
FROM build-base AS build-lmcache
# Bleeding edge: build from dev branch (v0.4.2+)
@@ -102,7 +90,7 @@ RUN git clone https://github.com/LMCache/LMCache.git && \
echo ">>> DATE: $(git log -1 --format=%cd --date=short)" && \
echo "========================================\n\n" && \
sed -i '/torch/d' pyproject.toml && \
uv pip install setuptools_scm && \
pip install setuptools_scm && \
MAX_JOBS=8 python -m build --wheel --no-isolation && \
cp dist/*.whl /wheels/
@@ -124,9 +112,7 @@ RUN apt-get update && apt-get install -y build-essential cmake gcc && \
cp wheels/*.whl /wheels/
# ==============================================================================
# NOTE: Temporarily using PyPI vLLM wheel for QEMU testing
# To restore native build on GH200, uncomment the block below and comment out
# the PyPI download section.
# Build vLLM from source
# ==============================================================================
FROM build-base AS build-vllm
# Bleeding edge: build from main branch
@@ -136,20 +122,21 @@ RUN apt-get update && apt-get install -y ccache
RUN git clone https://github.com/vllm-project/vllm.git
RUN cd vllm && \
git checkout ${VLLM_REF} && \
echo "\n\n========================================" && \
echo ">>> BUILDING VLLM FROM:" && \
echo ">>> BRANCH: $(git rev-parse --abbrev-ref HEAD)" && \
echo ">>> COMMIT: $(git rev-parse HEAD)" && \
echo ">>> DATE: $(git log -1 --format=%cd --date=short)" && \
echo ">>> TAG: $(git describe --tags --always 2>/dev/null || echo 'no tag')" && \
echo "========================================\n\n" && \
git submodule sync && \
git submodule update --init --recursive -j 8 && \
sed -i 's/GIT_TAG [a-f0-9]\{40\}/GIT_TAG main/' cmake/external_projects/vllm_flash_attn.cmake && \
export MAX_JOBS=8 && \
export CMAKE_BUILD_PARALLEL_LEVEL=$MAX_JOBS && \
python use_existing_torch.py && \
uv pip install -r requirements/build.txt && \
CCACHE_NOHASHDIR="true" uv build --wheel --no-build-isolation -o /wheels
# Use PyPI vLLM wheel (QEMU cmake fails during try_compile)
# FROM build-base AS build-vllm
# ARG VLLM_VERSION=0.18.1
# RUN mkdir -p /wheels && \
# pip download vllm==${VLLM_VERSION} --platform manylinux_2_31_aarch64 --only-binary=:all: --no-deps -d /wheels
pip install -r requirements/build.txt && \
CCACHE_NOHASHDIR="true" pip build --wheel --no-build-isolation -o /wheels
# Build infinistore after vllm to avoid cache invalidation
FROM build-base AS build-infinistore
@@ -166,9 +153,9 @@ RUN git clone -b v1.12.0 https://github.com/google/flatbuffers.git && \
# Build InfiniStore from source as a Python package
RUN git clone https://github.com/bytedance/InfiniStore && \
cd InfiniStore && \
uv pip install meson && \
uv pip install --no-deps --no-build-isolation -e . && \
uv pip uninstall infinistore && \
pip install meson && \
pip install --no-deps --no-build-isolation -e . && \
pip uninstall infinistore && \
python -m build --wheel --no-isolation && \
cp dist/*.whl /wheels/
@@ -181,25 +168,25 @@ COPY --from=build-lmcache /wheels/* wheels/
COPY --from=build-infinistore /wheels/* wheels/
# Install wheels (infinistore is now built as a wheel)
RUN uv pip install wheels/*
RUN pip install wheels/*
RUN rm -r wheels
# Install pynvml
RUN uv pip install pynvml pandas
RUN pip install pynvml pandas
# Add additional packages for vLLM OpenAI
# Bleeding edge: latest transformers
RUN uv pip install accelerate hf_transfer modelscope bitsandbytes timm boto3 runai-model-streamer runai-model-streamer[s3] tensorizer transformers --upgrade
RUN pip install accelerate hf_transfer modelscope bitsandbytes timm boto3 runai-model-streamer runai-model-streamer[s3] tensorizer transformers --upgrade
# Clean uv cache
RUN uv clean
# Clean pip cache
RUN pip cache purge || true
# Install build tools and dependencies
RUN uv pip install -U build cmake ninja pybind11 setuptools==79.0.1 wheel
RUN pip install -U build cmake ninja pybind11 setuptools==79.0.1 wheel
# Enable hf-transfer
ENV HF_HUB_ENABLE_HF_TRANSFER=1
RUN uv pip install datasets aiohttp
RUN pip install datasets aiohttp
# Install nsys for profiling
ARG NSYS_URL=https://developer.nvidia.com/downloads/assets/tools/secure/nsight-systems/2025_5/
@@ -209,7 +196,7 @@ RUN wget ${NSYS_URL}${NSYS_PKG} && dpkg -i $NSYS_PKG && rm $NSYS_PKG
RUN apt install -y --no-install-recommends tmux cmake
# Deprecated cleanup
RUN uv pip uninstall pynvml && uv pip install nvidia-ml-py
RUN pip uninstall pynvml && pip install nvidia-ml-py
# API server entrypoint
# ENTRYPOINT ["vllm", "serve"]