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

lmcache/Dockerfile

@@ -1,10 +1,12 @@
-ARG CUDA_VERSION=12.9.0
-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
 ARG TORCH_CUDA_ARCH_LIST="9.0a"
 ENV TORCH_CUDA_ARCH_LIST=${TORCH_CUDA_ARCH_LIST}
 ENV DEBIAN_FRONTEND=noninteractive
@@ -26,37 +28,29 @@ ENV CXX=/usr/bin/g++
 # Install uv
 RUN curl -LsSf https://astral.sh/uv/install.sh | env UV_INSTALL_DIR=/usr/local/bin sh
 
-# Set up workspace and virtualenv
+# Set up workspace
 WORKDIR /workspace
-ARG PYTHON_VERSION
-RUN uv venv -p ${PYTHON_VERSION} --seed --python-preference only-managed
 
-# Activate uv venv
+# Environment setup (PyTorch container already has CUDA paths set)
-ENV VIRTUAL_ENV=/workspace/.venv
-ENV PATH=${VIRTUAL_ENV}/bin:${PATH}
 ENV CUDA_HOME=/usr/local/cuda
 ENV LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}
 
-# Install Python deps in venv
-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
-
 FROM base AS build-base
 
 # 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>=77.0.3,<81.0.0" wheel
 
 # Clone the repo, apply the patch, and build
-RUN git clone https://github.com/LMCache/LMCache.git -b v0.3.3 && \
+RUN git clone https://github.com/LMCache/LMCache.git && \
     cd LMCache && \
-    uv pip install setuptools_scm && \
+    git checkout dev && \
-    python -m build --wheel --no-isolation && \
+    echo "\n\n========================================" && \
-    cp dist/*.whl /workspace/
+    echo ">>> BUILDING LMCACHE FROM:" && \
-CMD ["/bin/bash"]">>> COMMIT: $(git rev-parse HEAD)" && \
+    echo ">>> BRANCH: $(git rev-parse --abbrev-ref HEAD)" && \
+    echo ">>> COMMIT: $(git rev-parse HEAD)" && \
     echo ">>> DATE:   $(git log -1 --format=%cd --date=short)" && \
     echo "========================================\n\n" && \
-    uv pip install setuptools_scm && \
+    pip install setuptools_scm && \
     python -m build --wheel --no-isolation && \
     cp dist/*.whl /workspace/
-CMD ["/bin/bash"]
+CMD ["/bin/bash"]

vllm/Dockerfile

@@ -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 update && apt install -y --no-install-recommends \
-RUN apt upgrade -y
-RUN apt install -y --no-install-recommends \
         curl \
         git \
         libibverbs-dev \
         zlib1g-dev \
-        libnuma-dev
+        libnuma-dev \
-
+        wget \
-# Clean apt cache
+    && apt clean \
-RUN apt clean
+    && rm -rf /var/lib/apt/lists/* /var/cache/apt/archives
-RUN rm -rf /var/lib/apt/lists/*
-RUN rm -rf /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
+# Environment setup (PyTorch container already has CUDA paths set)
-ARG PYTHON_VERSION
-RUN uv venv -p ${PYTHON_VERSION} --seed --python-preference only-managed
-ENV VIRTUAL_ENV=/workspace/.venv
-ENV PATH=${VIRTUAL_ENV}/bin:${PATH}
 ENV CUDA_HOME=/usr/local/cuda
 ENV LD_LIBRARY_PATH=${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
+# Use the Python environment from the container
-
+# The NGC container already has a working Python/PyTorch setup
-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
 
 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
+# Build vLLM from source
-# To restore native build on GH200, uncomment the block below and comment out
-# the PyPI download section.
 # ==============================================================================
 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 && \
+    pip install -r requirements/build.txt && \
-    CCACHE_NOHASHDIR="true" uv build --wheel --no-build-isolation -o /wheels
+    CCACHE_NOHASHDIR="true" pip 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
 
 # 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 && \
+    pip install meson && \
-    uv pip install --no-deps --no-build-isolation -e . && \
+    pip install --no-deps --no-build-isolation -e . && \
-    uv pip uninstall infinistore && \
+    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
+# Clean pip cache
-RUN uv clean
+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"]