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‎.github/workflows/runtime_nightly_cuda.yaml‎

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name: Multipy runtime nightly release with cuda
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on:
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schedule:
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- cron: '0 2 * * *' # run at 2 AM UTC
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pull_request:
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push:
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branches:
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- main
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jobs:
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unittest:
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strategy:
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matrix:
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python-version: [3.8]
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platform: [linux.4xlarge.nvidia.gpu]
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abi: [0,1]
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fail-fast: false
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runs-on: ${{ matrix.platform }}
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steps:
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- name: Update pip
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run: |
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sudo yum update -y
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sudo yum -y install git python3-pip
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sudo pip3 install --upgrade pip
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- name: Setup conda
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run: |
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wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh
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bash ~/miniconda.sh -b -p $HOME/miniconda
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- name: setup Path
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run: |
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echo /usr/local/cuda-11.3/bin >> $GITHUB_PATH
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echo "/home/ec2-user/miniconda/bin" >> $GITHUB_PATH
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echo "CONDA=/home/ec2-user/miniconda" >> $GITHUB_PATH
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- name: Checkout MultiPy
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uses: actions/checkout@v2
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with:
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submodules: true
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- name: Setup SSH (Click me for login details)
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uses: ./.github/actions/setup-ssh
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with:
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github-secret: ${{ secrets.GITHUB_TOKEN }}
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- name: Install C++ toolchain
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run: |
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sudo yum -y install clang llvm
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export CC=clang
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export CXX=clang++
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sudo yum -y install xz-devel bzip2-devel libnsl2-devel readline-devel expat-devel gdbm-devel glibc-devel gmp-devel libffi-devel libGL-devel libX11-devel ncurses-devel openssl-devel sqlite-devel tcl-devel tix-devel tk-devel
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sudo yum -y install lzma
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sudo yum -y install uuid
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sudo yum -y install openmpi-devel
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sudo yum -y install zlib-devel
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- name: create conda env
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run: |
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conda create --name multipy_runtime_env python=${{ matrix.python-version }}
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conda info
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- name: Install python/pytorch dependencies
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shell: bash -l {0}
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env:
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PYTHON_VERSION: ${{ matrix.python-version }}
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run: |
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conda run -n multipy_runtime_env python -m pip install astunparse numpy ninja pyyaml mkl mkl-include setuptools cmake cffi typing_extensions future six requests dataclasses pytest
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- name: gen examples
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shell: bash -l {0}
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env:
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PYTHON_VERSION: ${{ matrix.python-version }}
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run: |
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# A minor hack to get the CI working as conda doesn't have torch,
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# fortunately we can remove this once we have a dynamically linked torch
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cd multipy/runtime/example
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conda create --name example_env python=${{ matrix.python-version }}
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conda run -n example_env python -m pip install torch torchvision torchaudio pathlib
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conda run -n example_env python generate_examples.py
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- name: Build pytorch with ABI=${{ matrix.abi }}
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shell: bash -l {0}
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env:
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PYTHON_VERSION: ${{ matrix.python-version }}
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run: |
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export GLIBCXX_USE_CXX11_ABI=${{ matrix.abi }}
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export CXXFLAGS="-D_GLIBCXX_USE_CXX11_ABI=${{ matrix.abi }}"
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export TORCH_CXX_FLAGS="-D_GLIBCXX_USE_CXX11_ABI=${{ matrix.abi }}"
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cd multipy/runtime/third-party/pytorch
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export USE_DEPLOY=1
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export USE_CUDA=1
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conda run -n multipy_runtime_env python setup.py develop
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- name: Build multipy runtime with ABI=${{ matrix.abi }}
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shell: bash -l {0}
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env:
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PYTHON_VERSION: ${{ matrix.python-version }}
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run: |
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cd multipy/runtime
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mkdir build
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cd build
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conda run -n multipy_runtime_env cmake -DABI_EQUALS_1=${{ matrix.abi }} ..
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conda run -n multipy_runtime_env cmake --build . --config Release
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- name: install files
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shell: bash -l {0}
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env:
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PYTHON_VERSION: ${{ matrix.python-version }}
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run: |
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cd multipy/runtime/build
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conda run -n multipy_runtime_env cmake --install . --prefix "."
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- name: Run unit tests with ABI=${{ matrix.abi }}
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shell: bash -l {0}
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env:
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PYTHON_VERSION: ${{ matrix.python-version }}
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run: |
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cd multipy/runtime/build
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./test_deploy
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- name: Run unit tests with ABI=${{ matrix.abi }} with gpu
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shell: bash -l {0}
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env:
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PYTHON_VERSION: ${{ matrix.python-version }}
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run: |
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cd multipy/runtime/build
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./test_deploy_gpu
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- name: create tarball [click me to get a list of files for the nightly release]
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shell: bash -l {0}
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env:
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PYTHON_VERSION: ${{ matrix.python-version }}
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run: |
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cd multipy/runtime/build/dist
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tar -czvf multipy_runtime.tar.gz multipy/
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- name: Update nightly release
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uses: pyTooling/Actions/releaser@main
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with:
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tag: nightly-runtime-cuda-abi-${{ matrix.abi }}
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rm: true
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token: ${{ secrets.GITHUB_TOKEN }}
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files: |
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multipy/runtime/build/dist/multipy_runtime.tar.gz

‎.github/workflows/runtime_tests.yaml‎

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conda run -n multipy_runtime_env cmake --install . --prefix "."
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- name: Run unit tests with ABI=${{ matrix.abi }}
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if: ${{ matrix.abi }} == 1
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shell: bash -l {0}
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env:
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PYTHON_VERSION: ${{ matrix.python-version }}

‎.github/workflows/runtime_tests_cuda.yaml‎

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name: Multipy runtime tests with cuda
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on:
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pull_request:
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push:
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branches:
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- main
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jobs:
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unittest:
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strategy:
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matrix:
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python-version: [3.7, 3.8, 3.9]
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platform: [linux.4xlarge.nvidia.gpu]
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abi: [0,1]
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fail-fast: false
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runs-on: ${{ matrix.platform }}
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steps:
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- name: Update pip
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run: |
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sudo yum update -y
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sudo yum -y install git python3-pip
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sudo pip3 install --upgrade pip
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- name: Setup conda
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run: |
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wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh
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bash ~/miniconda.sh -b -p $HOME/miniconda
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- name: Install CUDA 11.3
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shell: bash
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run: |
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sudo yum install -y https://dl.fedoraproject.org/pub/epel/epel-release-latest-7.noarch.rpm
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sudo yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/cuda-rhel7.repo
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sudo yum clean expire-cache
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sudo yum install -y nvidia-driver-latest-dkms
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sudo yum install -y cuda-11-3
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sudo yum install -y cuda-drivers
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sudo yum install -y libcudnn8-devel
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- name: setup Path
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run: |
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echo /usr/local/cuda-11.3/bin >> $GITHUB_PATH
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echo "/home/ec2-user/miniconda/bin" >> $GITHUB_PATH
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echo "CONDA=/home/ec2-user/miniconda" >> $GITHUB_PATH
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- name: Checkout MultiPy
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uses: actions/checkout@v2
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with:
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submodules: true
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- name: Setup SSH (Click me for login details)
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uses: ./.github/actions/setup-ssh
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with:
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github-secret: ${{ secrets.GITHUB_TOKEN }}
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- name: Install C++ toolchain
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run: |
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sudo yum -y install clang llvm
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export CC=clang
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export CXX=clang++
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sudo yum -y install xz-devel bzip2-devel libnsl2-devel readline-devel expat-devel gdbm-devel glibc-devel gmp-devel libffi-devel libGL-devel libX11-devel ncurses-devel openssl-devel sqlite-devel tcl-devel tix-devel tk-devel
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sudo yum -y install lzma
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sudo yum -y install uuid
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sudo yum -y install openmpi-devel
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sudo yum -y install zlib-devel
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- name: create conda env
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run: |
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conda create --name multipy_runtime_env python=${{ matrix.python-version }}
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conda info
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- name: Install python/pytorch dependencies
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shell: bash -l {0}
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env:
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PYTHON_VERSION: ${{ matrix.python-version }}
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run: |
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conda run -n multipy_runtime_env python -m pip install astunparse numpy ninja pyyaml mkl mkl-include setuptools cmake cffi typing_extensions future six requests dataclasses pytest
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- name: gen examples
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shell: bash -l {0}
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env:
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PYTHON_VERSION: ${{ matrix.python-version }}
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run: |
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# A minor hack to get the CI working as conda doesn't have torch,
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# fortunately we can remove this once we have a dynamically linked torch
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cd multipy/runtime/example
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conda create --name example_env python=${{ matrix.python-version }}
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conda run -n example_env python -m pip install torch torchvision torchaudio pathlib
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conda run -n example_env python generate_examples.py
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- name: Build pytorch with ABI=${{ matrix.abi }}
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shell: bash -l {0}
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env:
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PYTHON_VERSION: ${{ matrix.python-version }}
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run: |
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export GLIBCXX_USE_CXX11_ABI=${{ matrix.abi }}
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export CXXFLAGS="-D_GLIBCXX_USE_CXX11_ABI=${{ matrix.abi }}"
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export TORCH_CXX_FLAGS="-D_GLIBCXX_USE_CXX11_ABI=${{ matrix.abi }}"
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cd multipy/runtime/third-party/pytorch
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export USE_DEPLOY=1
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export USE_CUDA=1
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conda run -n multipy_runtime_env python setup.py develop
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- name: Build multipy runtime with ABI=${{ matrix.abi }}
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shell: bash -l {0}
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env:
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PYTHON_VERSION: ${{ matrix.python-version }}
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run: |
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cd multipy/runtime
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mkdir build
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cd build
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conda run -n multipy_runtime_env cmake -DABI_EQUALS_1=${{ matrix.abi }} ..
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conda run -n multipy_runtime_env cmake --build . --config Release
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- name: install files
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shell: bash -l {0}
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env:
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PYTHON_VERSION: ${{ matrix.python-version }}
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run: |
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cd multipy/runtime/build
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conda run -n multipy_runtime_env cmake --install . --prefix "."
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- name: Run unit tests with ABI=${{ matrix.abi }}
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shell: bash -l {0}
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env:
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PYTHON_VERSION: ${{ matrix.python-version }}
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run: |
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cd multipy/runtime/build
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./test_deploy
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- name: Run unit tests with ABI=${{ matrix.abi }} with gpu
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shell: bash -l {0}
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env:
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PYTHON_VERSION: ${{ matrix.python-version }}
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run: |
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cd multipy/runtime/build
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./test_deploy_gpu

‎README.md‎

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The C++ binaries (`libtorch_interpreter.so`,`libtorch_deploy.a`, `utils.cmake`), and the header files of `multipy::runtime` can be installed from our [nightly release](https://github.com/pytorch/multipy/releases/tag/nightly-runtime-abi-0). The ABI for the nightly release is 0. You can find a version of the release with ABI=1 [here](https://github.com/pytorch/multipy/releases/tag/nightly-runtime-abi-1).
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C++ binaries with cuda (11.3) support can also be found for [ABI=0](https://github.com/pytorch/multipy/releases/tag/nightly-runtime-cuda-abi-0) and [ABI=1](https://github.com/pytorch/multipy/releases/tag/nightly-runtime-cuda-abi-1)
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```
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wget https://github.com/pytorch/multipy/releases/download/nightly-runtime-abi-0/multipy_runtime.tar.gz
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tar -xvzf multipy_runtime.tar.gz

‎multipy/runtime/CMakeLists.txt‎

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)
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target_include_directories(test_deploy PRIVATE ${CMAKE_SOURCE_DIR}/../..)
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# LINK_DIRECTORIES("${PYTORCH_ROOT}/torch/lib")
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# add_executable(test_deploy_gpu ${INTERPRETER_TEST_SOURCES_GPU})
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# target_compile_definitions(test_deploy_gpu PUBLIC TEST_CUSTOM_LIBRARY)
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# target_include_directories(test_deploy_gpu PRIVATE ${PYTORCH_ROOT}/torch)
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# target_include_directories(test_deploy_gpu PRIVATE ${CMAKE_SOURCE_DIR}/../..)
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# target_link_libraries(test_deploy_gpu
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# PUBLIC "-Wl,--no-as-needed -rdynamic" gtest dl torch_deploy_interface c10 torch_cpu
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# )
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LINK_DIRECTORIES("${PYTORCH_ROOT}/torch/lib")
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add_executable(test_deploy_gpu ${INTERPRETER_TEST_SOURCES_GPU})
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target_compile_definitions(test_deploy_gpu PUBLIC TEST_CUSTOM_LIBRARY)
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target_include_directories(test_deploy_gpu PRIVATE ${PYTORCH_ROOT}/torch)
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target_include_directories(test_deploy_gpu PRIVATE ${CMAKE_SOURCE_DIR}/../..)
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target_link_libraries(test_deploy_gpu PUBLIC "-Wl,--no-as-needed -rdynamic" gtest dl torch_deploy_interface c10 torch_cpu)
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LINK_DIRECTORIES("${PYTORCH_ROOT}/torch/lib")
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add_library(test_deploy_lib SHARED test_deploy_lib.cpp)
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)
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install(TARGETS test_deploy DESTINATION tests/bin)
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# install(TARGETS test_deploy_gpu DESTINATION tests/bin)
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install(TARGETS test_deploy_gpu DESTINATION tests/bin)
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install(TARGETS test_deploy DESTINATION tests/bin)
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‎multipy/runtime/test_deploy_gpu.cpp‎

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TEST(TorchDeployGPUTest, UsesDistributed) {
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const auto model_filename = path(
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"USES_DISTRIBUTED",
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"torch/csrc/deploy/example/generated/uses_distributed");
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"USES_DISTRIBUTED", "multipy/runtime/example/generated/uses_distributed");
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torch::deploy::InterpreterManager m(1);
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torch::deploy::Package p = m.loadPackage(model_filename);
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{

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