Dockerfile.trtllm

# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0

# This section contains build arguments that are common and shared with
# the plain Dockerfile, so they should NOT have a default. The source of truth is from build.sh.
ARG BASE_IMAGE
ARG BASE_IMAGE_TAG

ARG PYTHON_VERSION
ARG ENABLE_KVBM

ARG PYTORCH_BASE_IMAGE="nvcr.io/nvidia/pytorch"
ARG PYTORCH_BASE_IMAGE_TAG="25.10-py3"
ARG RUNTIME_IMAGE="nvcr.io/nvidia/cuda-dl-base"
ARG RUNTIME_IMAGE_TAG="25.10-cuda13.0-runtime-ubuntu24.04"

# TensorRT-LLM specific configuration
ARG HAS_TRTLLM_CONTEXT=0
ARG TENSORRTLLM_PIP_WHEEL="tensorrt-llm"
ARG TENSORRTLLM_INDEX_URL="https://pypi.nvidia.com/"
ARG GITHUB_TRTLLM_COMMIT

# 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

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

# Copy artifacts from NGC PyTorch image
FROM ${PYTORCH_BASE_IMAGE}:${PYTORCH_BASE_IMAGE_TAG} AS pytorch_base

##################################################
########## Framework Builder Stage ##############
##################################################
#
# PURPOSE: Build TensorRT-LLM with root privileges
#
# This stage handles TensorRT-LLM installation which requires:
# - Root access for apt operations (CUDA repos, TensorRT installation)
# - System-level modifications in install_tensorrt.sh
# - Virtual environment population with PyTorch and TensorRT-LLM
#
# The completed venv is then copied to runtime stage with dynamo ownership

FROM ${BASE_IMAGE}:${BASE_IMAGE_TAG} AS framework

ARG ARCH_ALT
ARG PYTHON_VERSION
ARG ENABLE_KVBM
ENV NIXL_PREFIX=/opt/nvidia/nvda_nixl
ENV NIXL_LIB_DIR=$NIXL_PREFIX/lib/${ARCH_ALT}-linux-gnu
ENV NIXL_PLUGIN_DIR=$NIXL_LIB_DIR/plugins
ENV VIRTUAL_ENV=/opt/dynamo/venv
ENV PATH="${VIRTUAL_ENV}/bin:${PATH}"

# Install minimal dependencies needed for TensorRT-LLM installation
RUN apt-get update && \
    DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
        python${PYTHON_VERSION}-dev \
        python3-pip \
        curl \
        git \
        git-lfs \
        ca-certificates && \
    apt-get clean && \
    rm -rf /var/lib/apt/lists/*

# Copy uv
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

# Copy pytorch installation from NGC PyTorch
ARG TORCH_VER=2.9.0a0+145a3a7bda.nv25.10
ARG TORCH_TENSORRT_VER=2.9.0a0
ARG TORCHVISION_VER=0.24.0a0+094e7af5
ARG JINJA2_VER=3.1.6
ARG SYMPY_VER=1.14.0
ARG FLASH_ATTN_VER=2.7.4.post1+25.10

COPY --from=pytorch_base /usr/local/lib/python${PYTHON_VERSION}/dist-packages/torch ${VIRTUAL_ENV}/lib/python${PYTHON_VERSION}/site-packages/torch
COPY --from=pytorch_base /usr/local/lib/python${PYTHON_VERSION}/dist-packages/torch-${TORCH_VER}.dist-info ${VIRTUAL_ENV}/lib/python${PYTHON_VERSION}/site-packages/torch-${TORCH_VER}.dist-info
COPY --from=pytorch_base /usr/local/lib/python${PYTHON_VERSION}/dist-packages/torchgen ${VIRTUAL_ENV}/lib/python${PYTHON_VERSION}/site-packages/torchgen
COPY --from=pytorch_base /usr/local/lib/python${PYTHON_VERSION}/dist-packages/torchvision ${VIRTUAL_ENV}/lib/python${PYTHON_VERSION}/site-packages/torchvision
COPY --from=pytorch_base /usr/local/lib/python${PYTHON_VERSION}/dist-packages/torchvision-${TORCHVISION_VER}.dist-info ${VIRTUAL_ENV}/lib/python${PYTHON_VERSION}/site-packages/torchvision-${TORCHVISION_VER}.dist-info
COPY --from=pytorch_base /usr/local/lib/python${PYTHON_VERSION}/dist-packages/torchvision.libs ${VIRTUAL_ENV}/lib/python${PYTHON_VERSION}/site-packages/torchvision.libs
COPY --from=pytorch_base /usr/local/lib/python${PYTHON_VERSION}/dist-packages/functorch ${VIRTUAL_ENV}/lib/python${PYTHON_VERSION}/site-packages/functorch
COPY --from=pytorch_base /usr/local/lib/python${PYTHON_VERSION}/dist-packages/jinja2 ${VIRTUAL_ENV}/lib/python${PYTHON_VERSION}/site-packages/jinja2
COPY --from=pytorch_base /usr/local/lib/python${PYTHON_VERSION}/dist-packages/jinja2-${JINJA2_VER}.dist-info ${VIRTUAL_ENV}/lib/python${PYTHON_VERSION}/site-packages/jinja2-${JINJA2_VER}.dist-info
COPY --from=pytorch_base /usr/local/lib/python${PYTHON_VERSION}/dist-packages/sympy ${VIRTUAL_ENV}/lib/python${PYTHON_VERSION}/site-packages/sympy
COPY --from=pytorch_base /usr/local/lib/python${PYTHON_VERSION}/dist-packages/sympy-${SYMPY_VER}.dist-info ${VIRTUAL_ENV}/lib/python${PYTHON_VERSION}/site-packages/sympy-${SYMPY_VER}.dist-info
COPY --from=pytorch_base /usr/local/lib/python${PYTHON_VERSION}/dist-packages/flash_attn ${VIRTUAL_ENV}/lib/python${PYTHON_VERSION}/site-packages/flash_attn
COPY --from=pytorch_base /usr/local/lib/python${PYTHON_VERSION}/dist-packages/flash_attn-${FLASH_ATTN_VER}.dist-info ${VIRTUAL_ENV}/lib/python${PYTHON_VERSION}/site-packages/flash_attn-${FLASH_ATTN_VER}.dist-info
COPY --from=pytorch_base /usr/local/lib/python${PYTHON_VERSION}/dist-packages/flash_attn_2_cuda.cpython-*-*-linux-gnu.so ${VIRTUAL_ENV}/lib/python${PYTHON_VERSION}/site-packages/
COPY --from=pytorch_base /usr/local/lib/python${PYTHON_VERSION}/dist-packages/torch_tensorrt ${VIRTUAL_ENV}/lib/python${PYTHON_VERSION}/site-packages/torch_tensorrt
COPY --from=pytorch_base /usr/local/lib/python${PYTHON_VERSION}/dist-packages/torch_tensorrt-${TORCH_TENSORRT_VER}.dist-info ${VIRTUAL_ENV}/lib/python${PYTHON_VERSION}/site-packages/torch_tensorrt-${TORCH_TENSORRT_VER}.dist-info

# Install TensorRT-LLM and related dependencies
ARG HAS_TRTLLM_CONTEXT
ARG TENSORRTLLM_PIP_WHEEL
ARG TENSORRTLLM_INDEX_URL
ARG GITHUB_TRTLLM_COMMIT

# Copy only wheel files and commit info from trtllm_wheel stage from build_context
COPY --from=trtllm_wheel /*.whl /trtllm_wheel/
COPY --from=trtllm_wheel /*.txt /trtllm_wheel/

RUN uv pip install --no-cache "cuda-python==13.0.2"

# Note: TensorRT needs to be uninstalled before installing the TRTLLM wheel
# because there might be mismatched versions of TensorRT between the NGC PyTorch
# and the TRTLLM wheel.
RUN [ -f /etc/pip/constraint.txt ] && : > /etc/pip/constraint.txt || true && \
    # Clean up any existing conflicting CUDA repository configurations and GPG keys
    rm -f /etc/apt/sources.list.d/cuda*.list && \
    rm -f /usr/share/keyrings/cuda-archive-keyring.gpg && \
    rm -f /etc/apt/trusted.gpg.d/cuda*.gpg

RUN if [ "$HAS_TRTLLM_CONTEXT" = "1" ]; then \
        # Download and run install_tensorrt.sh from TensorRT-LLM GitHub before installing the wheel
        curl -fsSL --retry 5 --retry-delay 10 --max-time 1800 -o /tmp/install_tensorrt.sh "https://github.com/NVIDIA/TensorRT-LLM/raw/${GITHUB_TRTLLM_COMMIT}/docker/common/install_tensorrt.sh" && \
        # Modify the script to use virtual environment pip instead of system pip3
        sed -i 's/pip3 install/uv pip install/g' /tmp/install_tensorrt.sh && \
        bash /tmp/install_tensorrt.sh && \
        # Install from local wheel directory in build context
        WHEEL_FILE="$(find /trtllm_wheel -name "*.whl" | head -n 1)"; \
        if [ -n "$WHEEL_FILE" ]; then \
            uv pip install --no-cache "$WHEEL_FILE" triton==3.5.0; \
        else \
            echo "No wheel file found in /trtllm_wheel directory."; \
            exit 1; \
        fi; \
    else \
        # Download and run install_tensorrt.sh from TensorRT-LLM GitHub before installing the wheel
        TRTLLM_VERSION=$(echo "${TENSORRTLLM_PIP_WHEEL}" | sed -n 's/.*==\([0-9a-zA-Z\.\-]*\).*/\1/p') && \
        (curl -fsSL --retry 5 --retry-delay 10 --max-time 1800 -o /tmp/install_tensorrt.sh "https://github.com/NVIDIA/TensorRT-LLM/raw/v${TRTLLM_VERSION}/docker/common/install_tensorrt.sh" || \
         curl -fsSL --retry 5 --retry-delay 10 --max-time 1800 -o /tmp/install_tensorrt.sh "https://github.com/NVIDIA/TensorRT-LLM/raw/${GITHUB_TRTLLM_COMMIT}/docker/common/install_tensorrt.sh") && \
        # Modify the script to use virtual environment pip instead of system pip3
        sed -i 's/pip3 install/uv pip install/g' /tmp/install_tensorrt.sh && \
        bash /tmp/install_tensorrt.sh && \
        # Install TensorRT-LLM wheel from the provided index URL, allow dependencies from PyPI
        # TRTLLM 1.2.0rc2 has issues installing from pypi with uv, installing from direct wheel link works best
        # explicitly installing triton 3.5.0 as trtllm only lists triton as dependency on x64_64 for some reason
        export TENSORRTLLM_PIP_WHEEL="https://pypi.nvidia.com/tensorrt-llm/tensorrt_llm-1.2.0rc2-cp312-cp312-linux_${ARCH_ALT}.whl"; \
        uv pip install --no-cache --index-strategy=unsafe-best-match --extra-index-url "${TENSORRTLLM_INDEX_URL}" "${TENSORRTLLM_PIP_WHEEL}" triton==3.5.0; \
    fi

##################################################
########## Runtime Image ########################
##################################################
#
# PURPOSE: Production runtime environment
#
# This stage creates a lightweight production-ready image containing:
# - Pre-compiled TensorRT-LLM 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 TensorRT-LLM
# - 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

ARG ARCH_ALT
ARG ENABLE_KVBM
ARG PYTHON_VERSION

WORKDIR /workspace

ENV ENV=${ENV:-/etc/shinit_v2}
ENV VIRTUAL_ENV=/opt/dynamo/venv
ENV NIXL_PREFIX=/opt/nvidia/nvda_nixl
ENV NIXL_LIB_DIR=$NIXL_PREFIX/lib/${ARCH_ALT}-linux-gnu
ENV NIXL_PLUGIN_DIR=$NIXL_LIB_DIR/plugins
# workaround for pickle lib issue
ENV OMPI_MCA_coll_ucc_enable=0
# Use UCX KVCACHE by default
ENV TRTLLM_USE_UCX_KVCACHE=1

ARG DYNAMO_COMMIT_SHA
ENV DYNAMO_COMMIT_SHA=$DYNAMO_COMMIT_SHA

# Install Python, build-essential and python3-dev as apt dependencies
RUN if [ ${ARCH_ALT} = "x86_64" ]; then \
        ARCH_FOR_GPG=${ARCH_ALT}; \
    else \
        ARCH_FOR_GPG="sbsa"; \
    fi && \
    curl -fsSL \
        https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2404/${ARCH_FOR_GPG}/cuda-archive-keyring.gpg \
        -o /usr/share/keyrings/cuda-archive-keyring.gpg &&\
    echo "deb [signed-by=/usr/share/keyrings/cuda-archive-keyring.gpg] \
        https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2404/${ARCH_FOR_GPG} /" \
        | tee /etc/apt/sources.list.d/cuda.repo.list > /dev/null &&\
    apt-get update && \
    DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
        # Build tools
        build-essential \
        g++ \
        ninja-build \
        git \
        git-lfs \
        # Python runtime - CRITICAL for virtual environment to work
        python${PYTHON_VERSION}-dev \
        python3-pip \
        # jq for polling various endpoints and health checks
        jq \
        # CUDA/ML libraries
        libcudnn9-cuda-13 \
        libnvshmem3-cuda-13 \
        # Network and communication libraries
        libzmq3-dev \
        # RDMA/UCX libraries required to find RDMA devices
        ibverbs-providers \
        ibverbs-utils \
        libibumad3 \
        libibverbs1 \
        libnuma1 \
        librdmacm1 \
        rdma-core \
        # OpenMPI dependencies
        openssh-client \
        openssh-server \
        # System utilities and dependencies
        curl && \
    apt-get clean && \
    rm -rf /var/lib/apt/lists/*

ENV LD_LIBRARY_PATH="/usr/lib/${ARCH_ALT}-linux-gnu/nvshmem/13/:${LD_LIBRARY_PATH}"

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

ENV CUDA_HOME=/usr/local/cuda \
    TRITON_CUPTI_PATH=/usr/local/cuda/include \
    TRITON_CUDACRT_PATH=/usr/local/cuda/include \
    TRITON_CUOBJDUMP_PATH=/usr/local/cuda/bin/cuobjdump \
    TRITON_NVDISASM_PATH=/usr/local/cuda/bin/nvdisasm \
    TRITON_PTXAS_PATH=/usr/local/cuda/bin/ptxas \
    TRITON_CUDART_PATH=/usr/local/cuda/include

# Copy nats and etcd from dynamo_base 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 OpenMPI from PyTorch base image
COPY --from=pytorch_base /opt/hpcx/ompi /opt/hpcx/ompi
# Copy NUMA library from PyTorch base image
COPY --from=pytorch_base /usr/lib/${ARCH_ALT}-linux-gnu/libnuma.so* /usr/lib/${ARCH_ALT}-linux-gnu/

# Copy UCX libraries, libucc.so is needed by pytorch. May not need to copy whole hpcx dir but only /opt/hpcx/ucc/
COPY --from=pytorch_base /opt/hpcx /opt/hpcx
# This is needed to make libucc.so visible so pytorch can use it.
ENV LD_LIBRARY_PATH="/opt/hpcx/ucc/lib:${LD_LIBRARY_PATH}"
# Might not need to copy cusparseLt in the future once it's included in DLFW cuda container
COPY --from=pytorch_base /usr/local/cuda/lib64/libcusparseLt* /usr/local/cuda/lib64/

# Copy uv to system /bin
COPY --from=framework /bin/uv /bin/uvx /bin/

# Copy libgomp.so from framework image
COPY --from=framework /usr/local/tensorrt /usr/local/tensorrt
COPY --from=framework /usr/lib/${ARCH_ALT}-linux-gnu/libgomp.so* /usr/lib/${ARCH_ALT}-linux-gnu/

# Create dynamo user with group 0 for OpenShift compatibility
RUN userdel -r ubuntu > /dev/null 2>&1 || true \
    && useradd -m -s /bin/bash -g 0 dynamo \
    && [ `id -u dynamo` -eq 1000 ] \
    && mkdir -p /home/dynamo/.cache /opt/dynamo \
    && chown -R dynamo: /workspace /home/dynamo /opt/dynamo \
    && chmod -R g+w /workspace /home/dynamo/.cache /opt/dynamo


# Switch to dynamo user
USER dynamo
ENV HOME=/home/dynamo
ENV DYNAMO_HOME=/workspace

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

ENV PATH="/usr/local/ucx/bin:${VIRTUAL_ENV}/bin:/opt/hpcx/ompi/bin:/usr/local/bin/etcd/:/usr/local/cuda/bin:/usr/local/cuda/nvvm/bin:$PATH"
ENV LD_LIBRARY_PATH=\
$NIXL_LIB_DIR:\
$NIXL_PLUGIN_DIR:\
/usr/local/ucx/lib:\
/usr/local/ucx/lib/ucx:\
/opt/hpcx/ompi/lib:\
$LD_LIBRARY_PATH
ENV OPAL_PREFIX=/opt/hpcx/ompi

# Copy pre-built venv with PyTorch and TensorRT-LLM from framework stage
COPY --chown=dynamo: --from=framework ${VIRTUAL_ENV} ${VIRTUAL_ENV}

ENV TENSORRT_LIB_DIR=/usr/local/tensorrt/targets/${ARCH_ALT}-linux-gnu/lib
ENV LD_LIBRARY_PATH=/opt/dynamo/venv/lib/python3.12/site-packages/torch/lib:/opt/dynamo/venv/lib/python3.12/site-packages/torch_tensorrt/lib:${TENSORRT_LIB_DIR}:${LD_LIBRARY_PATH}

# Install dynamo, NIXL, and dynamo-specific dependencies
COPY --chown=dynamo: benchmarks/ /opt/dynamo/benchmarks/
COPY --chown=dynamo: --from=dynamo_base /opt/dynamo/wheelhouse/ /opt/dynamo/wheelhouse/
RUN uv pip install \
      --no-cache \
      /opt/dynamo/wheelhouse/ai_dynamo_runtime*.whl \
      /opt/dynamo/wheelhouse/ai_dynamo*any.whl \
      /opt/dynamo/wheelhouse/nixl/nixl*.whl \
    && if [ "${ENABLE_KVBM}" = "true" ]; then \
        uv pip install --no-cache /opt/dynamo/wheelhouse/kvbm*.whl; \
       fi \
    && cd /opt/dynamo/benchmarks \
    && UV_GIT_LFS=1 uv pip install --no-cache . \
    && cd - \
    && 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_GIT_LFS=1 uv pip install \
        --no-cache \
        --index-strategy unsafe-best-match \
        --extra-index-url https://download.pytorch.org/whl/cu130 \
        --requirement /tmp/requirements.txt \
        --requirement /tmp/requirements.test.txt \
        cupy-cuda13x

# Copy tests, benchmarks, deploy and components for CI with correct ownership
COPY --chown=dynamo: tests /workspace/tests
COPY --chown=dynamo: examples /workspace/examples
COPY --chown=dynamo: benchmarks /workspace/benchmarks
COPY --chown=dynamo: deploy /workspace/deploy
COPY --chown=dynamo: components/ /workspace/components/
COPY --chown=dynamo: recipes/ /workspace/recipes/

# Copy attribution files with correct ownership
COPY --chown=dynamo: ATTRIBUTION* LICENSE /workspace/

# Setup launch banner in common directory accessible to all users
RUN --mount=type=bind,source=./container/launch_message/runtime.txt,target=/opt/dynamo/launch_message.txt \
    sed '/^#\s/d' /opt/dynamo/launch_message.txt > /opt/dynamo/.launch_screen

# Setup environment for all users
USER root
RUN chmod 755 /opt/dynamo/.launch_screen && \
    echo 'source /opt/dynamo/venv/bin/activate' >> /etc/bash.bashrc && \
    echo 'cat /opt/dynamo/.launch_screen' >> /etc/bash.bashrc

USER dynamo
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

# Switch to root for system package installation
USER root

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

# 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

# Install maturin, for maturin develop
RUN uv pip install --no-cache maturin[patchelf]

# Editable install of dynamo
COPY pyproject.toml README.md hatch_build.py /workspace/
RUN uv pip install --no-cache --no-deps -e .

CMD []