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### Description Serve as example to build and run onnxruntime-gpu with latest software stack. To build docker image: ``` git clone https://github.com/microsoft/onnxruntime cd onnxruntime/dockerfiles docker build -t onnxruntime-cuda -f Dockerfile.cuda .. ``` To launch the docker image built from previous step (and mount the code directory to run a unit test below): ``` cd .. docker run --rm -it --gpus all -v $PWD:/code onnxruntime-cuda /bin/bash ``` Then run the following in docker image to verify that the cuda provider is good: ``` python /code/onnxruntime/test/python/onnxruntime_test_python_cudagraph.py ``` ### Motivation and Context https://github.com/microsoft/onnxruntime/issues/22335
88 lines
3.6 KiB
Text
88 lines
3.6 KiB
Text
# --------------------------------------------------------------
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# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License.
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# --------------------------------------------------------------
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# Build onnxruntime-gpu python package with CUDA 12.6 & CUDNN 9.4 for python 3.12 in Ubuntu 24.04 for Nvidia GPU.
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# If memory is less than 64GB, you may change "--parallel" to "--parallel 4" to avoid out-of-memory error.
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FROM nvcr.io/nvidia/cuda:12.6.1-devel-ubuntu24.04
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# Target CUDA device with compute capability >= 6.1
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ARG CMAKE_CUDA_ARCHITECTURES="61;70;75;80;86;90"
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ENV DEBIAN_FRONTEND=noninteractive
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MAINTAINER Changming Sun "chasun@microsoft.com"
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# Add source code to /code
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ADD . /code
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ENV PATH=/usr/local/cuda/bin:${PATH}
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# Install required packages
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RUN apt-get update && apt-get install -y --no-install-recommends \
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ca-certificates \
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g++ \
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gcc \
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make \
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git \
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cmake \
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wget \
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ninja-build \
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python3-pip \
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python3.12-dev \
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python3.12-venv \
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&& rm -rf /var/lib/apt/lists/*
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# Install CUDNN 9.4.0.58 for building ONNX Runtime with CUDA.
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RUN wget https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-x86_64/cudnn-linux-x86_64-9.4.0.58_cuda12-archive.tar.xz \
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&& mkdir -p /code/build/cudnn \
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&& tar -Jxvf cudnn-linux-x86_64-9.4.0.58_cuda12-archive.tar.xz -C /code/build/cudnn --strip=1
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# Create a virtual environment and install dependencies, then build ONNX Runtime with CUDA support.
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RUN cd /code \
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&& python3 -m venv /code/env \
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&& . /code/env/bin/activate \
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&& pip install --upgrade psutil setuptools wheel packaging \
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&& pip install -r tools/ci_build/github/linux/docker/inference/x86_64/python/cpu/scripts/requirements.txt \
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&& python /code/tools/ci_build/build.py --build_dir /code/build/Linux \
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--allow_running_as_root --skip_submodule_sync \
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--use_cuda --cuda_home /usr/local/cuda \
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--cudnn_home /code/build/cudnn \
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--build_shared_lib --skip_tests \
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--config Release --build_wheel --update --build --parallel \
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--cmake_generator Ninja \
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--enable_cuda_nhwc_ops \
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--cmake_extra_defines ONNXRUNTIME_VERSION=$(cat ./VERSION_NUMBER) "CMAKE_CUDA_ARCHITECTURES=${CMAKE_CUDA_ARCHITECTURES}" onnxruntime_BUILD_UNIT_TESTS=OFF
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# Start second stage to copy the build artifacts
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FROM nvcr.io/nvidia/cuda:12.6.1-runtime-ubuntu24.04
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ENV DEBIAN_FRONTEND=noninteractive
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# Copy built wheel and license
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COPY --from=0 /code/build/Linux/Release/dist /ort
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COPY --from=0 /code/dockerfiles/LICENSE-IMAGE.txt /code/LICENSE-IMAGE.txt
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# Set LD_LIBRARY_PATH so that runtime can load CUDA and CUDNN DLLs.
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# CUDNN will be installed by nvidia-cudnn-cu12 python package later.
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# Its location is in the site-packages directory, which can be retrieved like the following:
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# python -c "import sysconfig; print(sysconfig.get_path('purelib'))"
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ENV LD_LIBRARY_PATH="/ort/env/lib/python3.12/site-packages/nvidia/cudnn/lib:/usr/local/cuda/lib64"
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# Install runtime dependencies, and run a simple test to verify the installation.
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RUN apt-get update && apt-get install -y --no-install-recommends \
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libstdc++6 \
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ca-certificates \
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python3-pip \
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python3.12-venv \
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unattended-upgrades \
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&& unattended-upgrade \
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&& python3 -m venv /ort/env \
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&& . /ort/env/bin/activate \
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&& pip install /ort/*.whl \
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&& pip install nvidia-cudnn-cu12==9.4.0.58 \
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&& python -c 'import onnxruntime; print(onnxruntime.get_available_providers())' \
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&& rm -rf /ort/*.whl \
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&& rm -rf /var/lib/apt/lists/*
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# Ensure the virtual environment is always activated when running commands in the container.
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RUN echo ". /ort/env/bin/activate" >> ~/.bashrc
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