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‎.circleci/check.sh

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#!/bin/bash -e
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# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
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# Run this script before committing config.yml to verify it is valid yaml.
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python -c 'import yaml; yaml.safe_load(open("config.yml"))' && echo OK

‎.circleci/config.in.yml

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version: 2.1
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#examples:
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#https://github.com/facebookresearch/ParlAI/blob/master/.circleci/config.yml
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#https://github.com/facebookresearch/hydra/blob/master/.circleci/config.yml
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#https://github.com/facebookresearch/habitat-api/blob/master/.circleci/config.yml
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#drive tests with nox or tox or pytest?
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# -------------------------------------------------------------------------------------
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# environments where we run our jobs
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# -------------------------------------------------------------------------------------
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setupcuda: &setupcuda
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run:
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name: Setup CUDA
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working_directory: ~/
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command: |
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# download and install nvidia drivers, cuda, etc
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wget --no-verbose --no-clobber -P ~/nvidia-downloads 'https://s3.amazonaws.com/ossci-linux/nvidia_driver/NVIDIA-Linux-x86_64-430.40.run'
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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
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sudo /bin/bash ~/nvidia-downloads/NVIDIA-Linux-x86_64-430.40.run --no-drm -q --ui=none
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sudo sh ~/nvidia-downloads/cuda_10.2.89_440.33.01_linux.run --silent
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echo "Done installing CUDA."
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pyenv versions
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nvidia-smi
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pyenv global 3.7.0
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gpu: &gpu
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environment:
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CUDA_VERSION: "10.2"
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machine:
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image: default
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resource_class: gpu.medium # tesla m60
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binary_common: &binary_common
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parameters:
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# Edit these defaults to do a release`
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build_version:
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description: "version number of release binary; by default, build a nightly"
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type: string
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default: ""
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pytorch_version:
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description: "PyTorch version to build against; by default, use a nightly"
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type: string
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default: ""
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# Don't edit these
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python_version:
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description: "Python version to build against (e.g., 3.7)"
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type: string
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cu_version:
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description: "CUDA version to build against, in CU format (e.g., cpu or cu100)"
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type: string
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wheel_docker_image:
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description: "Wheel only: what docker image to use"
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type: string
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default: "pytorch/manylinux-cuda101"
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environment:
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PYTHON_VERSION: << parameters.python_version >>
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BUILD_VERSION: << parameters.build_version >>
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PYTORCH_VERSION: << parameters.pytorch_version >>
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CU_VERSION: << parameters.cu_version >>
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jobs:
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main:
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<<: *gpu
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machine:
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image: ubuntu-1604:201903-01
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steps:
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- checkout
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- <<: *setupcuda
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- run: pip3 install --progress-bar off wheel matplotlib 'pillow<7'
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- run: pip3 install --progress-bar off torch torchvision
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# - run: conda create -p ~/conda_env python=3.7 numpy
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# - run: conda activate ~/conda_env
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# - run: conda install -c pytorch pytorch torchvision
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- run: pip3 install --progress-bar off 'git+https://github.com/facebookresearch/fvcore'
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- run: LD_LIBRARY_PATH=$LD_LIBARY_PATH:/usr/local/cuda-10.2/lib64 python3 setup.py build_ext --inplace
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- run: LD_LIBRARY_PATH=$LD_LIBARY_PATH:/usr/local/cuda-10.2/lib64 python -m unittest discover -v -s tests
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- run: python3 setup.py bdist_wheel
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binary_linux_wheel:
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<<: *binary_common
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docker:
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- image: << parameters.wheel_docker_image >>
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resource_class: 2xlarge+
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steps:
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- checkout
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- run: packaging/build_wheel.sh
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- store_artifacts:
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path: dist
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- persist_to_workspace:
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root: dist
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paths:
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- "*"
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binary_linux_conda:
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<<: *binary_common
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docker:
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- image: "pytorch/conda-cuda"
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resource_class: 2xlarge+
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steps:
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- checkout
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# This is building with cuda but no gpu present,
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# so we aren't running the tests.
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- run: TEST_FLAG=--no-test packaging/build_conda.sh
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- store_artifacts:
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path: /opt/conda/conda-bld/linux-64
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- persist_to_workspace:
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root: /opt/conda/conda-bld/linux-64
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paths:
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- "*"
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binary_linux_conda_cuda:
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<<: *binary_common
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machine:
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image: ubuntu-1604:201903-01
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resource_class: gpu.medium
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steps:
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- checkout
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- run:
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name: Setup environment
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command: |
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set -e
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curl -L https://packagecloud.io/circleci/trusty/gpgkey | sudo apt-key add -
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curl -L https://dl.google.com/linux/linux_signing_key.pub | sudo apt-key add -
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sudo apt-get update
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sudo apt-get install \
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apt-transport-https \
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ca-certificates \
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curl \
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gnupg-agent \
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software-properties-common
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curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
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sudo add-apt-repository \
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"deb [arch=amd64] https://download.docker.com/linux/ubuntu \
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$(lsb_release -cs) \
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stable"
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sudo apt-get update
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export DOCKER_VERSION="5:19.03.2~3-0~ubuntu-xenial"
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sudo apt-get install docker-ce=${DOCKER_VERSION} docker-ce-cli=${DOCKER_VERSION} containerd.io=1.2.6-3
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# Add the package repositories
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distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
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curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
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curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
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export NVIDIA_CONTAINER_VERSION="1.0.3-1"
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sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit=${NVIDIA_CONTAINER_VERSION}
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sudo systemctl restart docker
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DRIVER_FN="NVIDIA-Linux-x86_64-410.104.run"
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wget "https://s3.amazonaws.com/ossci-linux/nvidia_driver/$DRIVER_FN"
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sudo /bin/bash "$DRIVER_FN" -s --no-drm || (sudo cat /var/log/nvidia-installer.log && false)
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nvidia-smi
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- run:
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name: Pull docker image
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command: |
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set -e
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export DOCKER_IMAGE=pytorch/conda-cuda
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echo Pulling docker image $DOCKER_IMAGE
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docker pull $DOCKER_IMAGE >/dev/null
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- run:
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name: Build and run tests
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command: |
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set -e
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cd ${HOME}/project/
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export DOCKER_IMAGE=pytorch/conda-cuda
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export VARS_TO_PASS="-e PYTHON_VERSION -e BUILD_VERSION -e PYTORCH_VERSION -e UNICODE_ABI -e CU_VERSION"
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docker run --gpus all --ipc=host -v $(pwd):/remote -w /remote ${VARS_TO_PASS} ${DOCKER_IMAGE} ./packaging/build_conda.sh
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workflows:
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version: 2
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build_and_test:
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jobs:
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- main
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{{workflows()}}
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- binary_linux_conda:
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cu_version: cu101
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name: binary_linux_conda_py3.7_cu101
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python_version: '3.7'
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- binary_linux_conda_cuda:
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name: testrun_conda_cuda_py3.7_cu100
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python_version: "3.7"
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pytorch_version: "1.4"
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cu_version: "cu100"

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