pytorch3d/.circleci/config.in.yml

version: 2.1

#examples:
#https://github.com/facebookresearch/ParlAI/blob/master/.circleci/config.yml
#https://github.com/facebookresearch/hydra/blob/master/.circleci/config.yml
#https://github.com/facebookresearch/habitat-api/blob/master/.circleci/config.yml

#drive tests with nox or tox or pytest?

# -------------------------------------------------------------------------------------
# environments where we run our jobs
# -------------------------------------------------------------------------------------


setupcuda: &setupcuda
  run:
    name: Setup CUDA
    working_directory: ~/
    command: |
      # download and install nvidia drivers, cuda, etc
      wget --no-verbose --no-clobber -P ~/nvidia-downloads 'https://s3.amazonaws.com/ossci-linux/nvidia_driver/NVIDIA-Linux-x86_64-430.40.run'
      wget --no-verbose --no-clobber -P ~/nvidia-downloads http://developer.download.nvidia.com/compute/cuda/10.2/Prod/local_installers/cuda_10.2.89_440.33.01_linux.run
      sudo /bin/bash ~/nvidia-downloads/NVIDIA-Linux-x86_64-430.40.run --no-drm -q --ui=none
      sudo sh ~/nvidia-downloads/cuda_10.2.89_440.33.01_linux.run --silent
      echo "Done installing CUDA."
      pyenv versions
      nvidia-smi
      pyenv global 3.7.0

gpu: &gpu
  environment:
    CUDA_VERSION: "10.2"
  machine:
    image: default
  resource_class: gpu.medium # tesla m60

binary_common: &binary_common
  parameters:
    # Edit these defaults to do a release`
    build_version:
      description: "version number of release binary; by default, build a nightly"
      type: string
      default: ""
    pytorch_version:
      description: "PyTorch version to build against; by default, use a nightly"
      type: string
      default: ""
    # Don't edit these
    python_version:
      description: "Python version to build against (e.g., 3.7)"
      type: string
    cu_version:
      description: "CUDA version to build against, in CU format (e.g., cpu or cu100)"
      type: string
    wheel_docker_image:
      description: "Wheel only: what docker image to use"
      type: string
      default: "pytorch/manylinux-cuda102"
  environment:
    PYTHON_VERSION: << parameters.python_version >>
    BUILD_VERSION: << parameters.build_version >>
    PYTORCH_VERSION: << parameters.pytorch_version >>
    CU_VERSION: << parameters.cu_version >>

jobs:
  main:
    <<: *gpu
    machine:
      image: ubuntu-1604:201903-01
    steps:
      - checkout
      - <<: *setupcuda
      - run: pip3 install --progress-bar off wheel matplotlib 'pillow<7'
      - run: pip3 install --progress-bar off torch torchvision
      # - run: conda create -p ~/conda_env python=3.7 numpy
      # - run: conda activate ~/conda_env
      # - run: conda install -c pytorch pytorch torchvision

      - run: pip3 install --progress-bar off 'git+https://github.com/facebookresearch/fvcore'
      - run: LD_LIBRARY_PATH=$LD_LIBARY_PATH:/usr/local/cuda-10.2/lib64 python3 setup.py build_ext --inplace
      - run: LD_LIBRARY_PATH=$LD_LIBARY_PATH:/usr/local/cuda-10.2/lib64 python -m unittest discover -v -s tests
      - run: python3 setup.py bdist_wheel

  binary_linux_wheel:
    <<: *binary_common
    docker:
      - image: << parameters.wheel_docker_image >>
    resource_class: 2xlarge+
    steps:
      - checkout
      - run: packaging/build_wheel.sh
      - store_artifacts:
          path: dist
      - persist_to_workspace:
          root: dist
          paths:
            - "*"

  binary_linux_conda:
    <<: *binary_common
    docker:
      - image: "pytorch/conda-cuda"
    resource_class: 2xlarge+
    steps:
      - checkout
      # This is building with cuda but no gpu present,
      # so we aren't running the tests.
      - run: TEST_FLAG=--no-test packaging/build_conda.sh
      - store_artifacts:
          path: /opt/conda/conda-bld/linux-64
      - persist_to_workspace:
          root: /opt/conda/conda-bld/linux-64
          paths:
            - "*"

  binary_linux_conda_cuda:
    <<: *binary_common
    machine:
      image: ubuntu-1604:201903-01
    resource_class: gpu.medium
    steps:
    - checkout
    - run:
        name: Setup environment
        command: |
          set -e

          curl -L https://packagecloud.io/circleci/trusty/gpgkey | sudo apt-key add -
          curl -L https://dl.google.com/linux/linux_signing_key.pub | sudo apt-key add -

          sudo apt-get update

          sudo apt-get install \
              apt-transport-https \
              ca-certificates \
              curl \
              gnupg-agent \
              software-properties-common

          curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -

          sudo add-apt-repository \
             "deb [arch=amd64] https://download.docker.com/linux/ubuntu \
             $(lsb_release -cs) \
             stable"

          sudo apt-get update
          export DOCKER_VERSION="5:19.03.2~3-0~ubuntu-xenial"
          sudo apt-get install docker-ce=${DOCKER_VERSION} docker-ce-cli=${DOCKER_VERSION} containerd.io=1.2.6-3

          # Add the package repositories
          distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
          curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
          curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list

          export NVIDIA_CONTAINER_VERSION="1.0.3-1"
          sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit=${NVIDIA_CONTAINER_VERSION}
          sudo systemctl restart docker

          DRIVER_FN="NVIDIA-Linux-x86_64-440.59.run"
          wget "https://s3.amazonaws.com/ossci-linux/nvidia_driver/$DRIVER_FN"
          sudo /bin/bash "$DRIVER_FN" -s --no-drm || (sudo cat /var/log/nvidia-installer.log && false)
          nvidia-smi

    - run:
        name: Pull docker image
        command: |
          set -e
          export DOCKER_IMAGE=pytorch/conda-cuda
          echo Pulling docker image $DOCKER_IMAGE
          docker pull $DOCKER_IMAGE >/dev/null

    - run:
        name: Build and run tests
        command: |
          set -e

          cd ${HOME}/project/

          export DOCKER_IMAGE=pytorch/conda-cuda
          export VARS_TO_PASS="-e PYTHON_VERSION -e BUILD_VERSION -e PYTORCH_VERSION -e UNICODE_ABI -e CU_VERSION"

          docker run --gpus all  --ipc=host -v $(pwd):/remote -w /remote ${VARS_TO_PASS} ${DOCKER_IMAGE} ./packaging/build_conda.sh

  binary_macos_wheel:
    <<: *binary_common
    macos:
      xcode: "9.0"
    steps:
      - checkout
      - run:
          # Cannot easily deduplicate this as source'ing activate
          # will set environment variables which we need to propagate
          # to build_wheel.sh
          command: |
            curl -o conda.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh
            sh conda.sh -b
            source $HOME/miniconda3/bin/activate
            packaging/build_wheel.sh
      - store_artifacts:
          path: dist

workflows:
  version: 2
  build_and_test:
    jobs:
      - main
      {{workflows()}}
      - binary_linux_conda_cuda:
          name: testrun_conda_cuda_py37_cu100_pyt14
          python_version: "3.7"
          pytorch_version: "1.4"
          cu_version: "cu100"
      - binary_linux_conda_cuda:
          name: testrun_conda_cuda_py37_cu102_pyt160
          python_version: "3.7"
          pytorch_version: '1.6.0'
          cu_version: "cu102"
      - binary_macos_wheel:
          cu_version: cpu
          name: macos_wheel_py36_cpu
          python_version: '3.6'
          pytorch_version: '1.6.0'
      - binary_macos_wheel:
          cu_version: cpu
          name: macos_wheel_py37_cpu
          python_version: '3.7'
          pytorch_version: '1.6.0'
      - binary_macos_wheel:
          cu_version: cpu
          name: macos_wheel_py38_cpu
          python_version: '3.8'
          pytorch_version: '1.6.0'