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config.yml.in
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config.yml.in
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version: 2.1
# How to test the Linux jobs:
# - Install CircleCI local CLI: https://circleci.com/docs/2.0/local-cli/
# - circleci config process .circleci/config.yml > gen.yml && circleci local execute -c gen.yml --job binary_linux_wheel_py3.7
# - Replace binary_linux_wheel_py3.7 with the name of the job you want to test.
# Job names are 'name:' key.
executors:
windows-cpu:
machine:
resource_class: windows.xlarge
image: windows-server-2019-vs2019:stable
shell: bash.exe
windows-gpu:
machine:
resource_class: windows.gpu.nvidia.medium
image: windows-server-2019-nvidia:stable
shell: bash.exe
commands:
checkout_merge:
description: "checkout merge branch"
steps:
- checkout
# - run:
# name: Checkout merge branch
# command: |
# set -ex
# BRANCH=$(git rev-parse --abbrev-ref HEAD)
# if [[ "$BRANCH" != "main" ]]; then
# git fetch --force origin ${CIRCLE_BRANCH}/merge:merged/${CIRCLE_BRANCH}
# git checkout "merged/$CIRCLE_BRANCH"
# fi
designate_upload_channel:
description: "inserts the correct upload channel into ${BASH_ENV}"
steps:
- run:
name: adding UPLOAD_CHANNEL to BASH_ENV
command: |
our_upload_channel=nightly
# On tags upload to test instead
if [[ -n "${CIRCLE_TAG}" ]]; then
our_upload_channel=test
fi
echo "export UPLOAD_CHANNEL=${our_upload_channel}" >> ${BASH_ENV}
brew_update:
description: "Update Homebrew and install base formulae"
steps:
- run:
name: Update Homebrew
no_output_timeout: "10m"
command: |
set -ex
# Update repositories manually.
# Running `brew update` produces a comparison between the
# current checkout and the updated checkout, which takes a
# very long time because the existing checkout is 2y old.
for path in $(find /usr/local/Homebrew -type d -name .git)
do
cd $path/..
git fetch --depth=1 origin
git reset --hard origin/master
done
export HOMEBREW_NO_AUTO_UPDATE=1
# Install expect and moreutils so that we can call `unbuffer` and `ts`.
# moreutils installs a `parallel` executable by default, which conflicts
# with the executable from the GNU `parallel`, so we must unlink GNU
# `parallel` first, and relink it afterwards.
brew install coreutils
brew unlink parallel
brew install moreutils
brew link parallel --overwrite
brew install expect
brew_install:
description: "Install Homebrew formulae"
parameters:
formulae:
type: string
default: ""
steps:
- run:
name: Install << parameters.formulae >>
no_output_timeout: "10m"
command: |
set -ex
export HOMEBREW_NO_AUTO_UPDATE=1
brew install << parameters.formulae >>
run_brew_for_ios_build:
steps:
- brew_update
- brew_install:
formulae: libtool
apt_install:
parameters:
args:
type: string
descr:
type: string
default: ""
update:
type: boolean
default: true
steps:
- run:
name: >
<<^ parameters.descr >> apt install << parameters.args >> <</ parameters.descr >>
<<# parameters.descr >> << parameters.descr >> <</ parameters.descr >>
command: |
<<# parameters.update >> sudo apt update -qy <</ parameters.update >>
sudo apt install << parameters.args >>
pip_install:
parameters:
args:
type: string
descr:
type: string
default: ""
user:
type: boolean
default: true
steps:
- run:
name: >
<<^ parameters.descr >> pip install << parameters.args >> <</ parameters.descr >>
<<# parameters.descr >> << parameters.descr >> <</ parameters.descr >>
command: >
pip install
<<# parameters.user >> --user <</ parameters.user >>
--progress-bar=off
<< parameters.args >>
install_torchvision:
parameters:
editable:
type: boolean
default: true
steps:
- pip_install:
args: --pre torch --extra-index-url https://download.pytorch.org/whl/nightly/cpu
descr: Install PyTorch from nightly releases
- pip_install:
args: --no-build-isolation <<# parameters.editable >> --editable <</ parameters.editable >> .
descr: Install torchvision <<# parameters.editable >> in editable mode <</ parameters.editable >>
install_prototype_dependencies:
steps:
- pip_install:
args: iopath
descr: Install third-party dependencies
- pip_install:
args: --pre torchdata --extra-index-url https://download.pytorch.org/whl/nightly/cpu
descr: Install torchdata from nightly releases
# Most of the test suite is handled by the `unittest` jobs, with completely different workflow and setup.
# This command can be used if only a selection of tests need to be run, for ad-hoc files.
run_tests_selective:
parameters:
file_or_dir:
type: string
steps:
- run:
name: Install test utilities
command: pip install --progress-bar=off pytest pytest-mock
- run:
name: Run tests
command: pytest --junitxml=test-results/junit.xml -v --durations 20 <<parameters.file_or_dir>>
- store_test_results:
path: test-results
download_model_weights:
parameters:
extract_roots:
type: string
default: "torchvision/models"
background:
type: boolean
default: true
steps:
- apt_install:
args: parallel wget
descr: Install download utilitites
- run:
name: Download model weights
background: << parameters.background >>
command: |
mkdir -p ~/.cache/torch/hub/checkpoints
python scripts/collect_model_urls.py << parameters.extract_roots >> \
| parallel -j0 'wget --no-verbose -O ~/.cache/torch/hub/checkpoints/`basename {}` {}\?source=ci'
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
default: "cpu"
unicode_abi:
description: "Python 2.7 wheel only: whether or not we are cp27mu (default: no)"
type: string
default: ""
wheel_docker_image:
description: "Wheel only: what docker image to use"
type: string
default: ""
conda_docker_image:
description: "Conda only: what docker image to use"
type: string
default: "pytorch/conda-builder:cpu"
environment:
PYTHON_VERSION: << parameters.python_version >>
PYTORCH_VERSION: << parameters.pytorch_version >>
UNICODE_ABI: << parameters.unicode_abi >>
CU_VERSION: << parameters.cu_version >>
MACOSX_DEPLOYMENT_TARGET: 10.9
torchvision_ios_params: &torchvision_ios_params
parameters:
build_environment:
type: string
default: ""
ios_arch:
type: string
default: ""
ios_platform:
type: string
default: ""
environment:
BUILD_ENVIRONMENT: << parameters.build_environment >>
IOS_ARCH: << parameters.ios_arch >>
IOS_PLATFORM: << parameters.ios_platform >>
torchvision_android_params: &torchvision_android_params
parameters:
build_environment:
type: string
default: ""
environment:
BUILD_ENVIRONMENT: << parameters.build_environment >>
smoke_test_common: &smoke_test_common
<<: *binary_common
docker:
- image: torchvision/smoke_test:latest
jobs:
circleci_consistency:
docker:
- image: cimg/python:3.7
steps:
- checkout
- pip_install:
args: jinja2 pyyaml
- run:
name: Check CircleCI config consistency
command: |
python .circleci/regenerate.py
git diff --exit-code || (echo ".circleci/config.yml not in sync with config.yml.in! Run .circleci/regenerate.py to update config"; exit 1)
lint_python_and_config:
docker:
- image: cimg/python:3.7
steps:
- checkout
- pip_install:
args: pre-commit
descr: Install lint utilities
- run:
name: Install pre-commit hooks
command: pre-commit install-hooks
- run:
name: Lint Python code and config files
command: pre-commit run --all-files
- run:
name: Required lint modifications
when: on_fail
command: git --no-pager diff
lint_c:
docker:
- image: cimg/python:3.7
steps:
- apt_install:
args: libtinfo5
descr: Install additional system libraries
- checkout
- run:
name: Install lint utilities
command: |
curl https://oss-clang-format.s3.us-east-2.amazonaws.com/linux64/clang-format-linux64 -o clang-format
chmod +x clang-format
sudo mv clang-format /opt/clang-format
- run:
name: Lint C code
command: ./.circleci/unittest/linux/scripts/run-clang-format.py -r torchvision/csrc --clang-format-executable /opt/clang-format
- run:
name: Required lint modifications
when: on_fail
command: git --no-pager diff
type_check_python:
docker:
- image: cimg/python:3.7
steps:
- checkout
- install_torchvision:
editable: true
- install_prototype_dependencies
- pip_install:
args: mypy
descr: Install Python type check utilities
- run:
name: Check Python types statically
command: mypy --install-types --non-interactive --config-file mypy.ini
unittest_torchhub:
docker:
- image: cimg/python:3.7
steps:
- checkout
- install_torchvision
- run_tests_selective:
file_or_dir: test/test_hub.py
unittest_onnx:
docker:
- image: cimg/python:3.7
steps:
- checkout
- install_torchvision
- pip_install:
args: onnx onnxruntime
descr: Install ONNX
- run_tests_selective:
file_or_dir: test/test_onnx.py
unittest_extended:
docker:
- image: cimg/python:3.7
resource_class: xlarge
steps:
- checkout
- download_model_weights
- install_torchvision
- run:
name: Enable extended tests
command: echo 'export PYTORCH_TEST_WITH_EXTENDED=1' >> $BASH_ENV
- run_tests_selective:
file_or_dir: test/test_extended_*.py
binary_linux_wheel:
<<: *binary_common
docker:
- image: << parameters.wheel_docker_image >>
resource_class: 2xlarge+
steps:
- checkout_merge
- designate_upload_channel
- run:
name: Build conda packages
no_output_timeout: 30m
command: |
set -ex
packaging/build_wheel.sh
- store_artifacts:
path: dist
- persist_to_workspace:
root: dist
paths:
- "*"
binary_linux_conda:
<<: *binary_common
docker:
- image: "<< parameters.conda_docker_image >>"
resource_class: 2xlarge+
steps:
- checkout_merge
- designate_upload_channel
- run:
name: Build conda packages
no_output_timeout: 30m
command: |
set -ex
packaging/build_conda.sh
- store_artifacts:
path: /opt/conda/conda-bld/linux-64
- persist_to_workspace:
root: /opt/conda/conda-bld/linux-64
paths:
- "*"
- store_test_results:
path: build_results/
binary_win_conda:
<<: *binary_common
executor: windows-cpu
steps:
- checkout_merge
- designate_upload_channel
- run:
name: Build conda packages
no_output_timeout: 30m
command: |
set -ex
source packaging/windows/internal/vc_install_helper.sh
packaging/windows/internal/cuda_install.bat
eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')"
conda activate base
conda install -yq conda-build "conda-package-handling!=1.5.0"
packaging/build_conda.sh
rm /C/tools/miniconda3/conda-bld/win-64/vs${VC_YEAR}*.tar.bz2
- store_artifacts:
path: C:/tools/miniconda3/conda-bld/win-64
- persist_to_workspace:
root: C:/tools/miniconda3/conda-bld/win-64
paths:
- "*"
- store_test_results:
path: build_results/
binary_win_wheel:
<<: *binary_common
executor: windows-cpu
steps:
- checkout_merge
- designate_upload_channel
- run:
name: Build wheel packages
no_output_timeout: 30m
command: |
set -ex
source packaging/windows/internal/vc_install_helper.sh
packaging/windows/internal/cuda_install.bat
packaging/build_wheel.sh
- store_artifacts:
path: dist
- persist_to_workspace:
root: dist
paths:
- "*"
- store_test_results:
path: build_results/
binary_macos_wheel:
<<: *binary_common
macos:
xcode: "14.0"
steps:
- checkout_merge
- designate_upload_channel
- 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 $HOME/miniconda3/bin/activate
packaging/build_wheel.sh
- store_artifacts:
path: dist
- persist_to_workspace:
root: dist
paths:
- "*"
binary_ios_build:
<<: *torchvision_ios_params
macos:
xcode: "14.0"
steps:
- attach_workspace:
at: ~/workspace
- checkout
- run_brew_for_ios_build
- run:
name: Build
no_output_timeout: "1h"
command: |
script="/Users/distiller/project/.circleci/unittest/ios/scripts/binary_ios_build.sh"
cat "$script"
source "$script"
- persist_to_workspace:
root: /Users/distiller/workspace/
paths: ios
binary_ios_upload:
<<: *torchvision_ios_params
macos:
xcode: "14.0"
steps:
- attach_workspace:
at: ~/workspace
- checkout
- run_brew_for_ios_build
- run:
name: Upload
no_output_timeout: "1h"
command: |
script="/Users/distiller/project/.circleci/unittest/ios/scripts/binary_ios_upload.sh"
cat "$script"
source "$script"
binary_android_build:
<<: *torchvision_android_params
docker:
- image: cimg/android:2021.08-ndk
resource_class: xlarge
steps:
- attach_workspace:
at: ~/workspace
- checkout
- run:
name: Build
no_output_timeout: "1h"
command: |
script="/home/circleci/project/.circleci/unittest/android/scripts/binary_android_build.sh"
cat "$script"
source "$script"
- store_artifacts:
path: ~/workspace/artifacts
binary_android_upload:
<<: *torchvision_android_params
docker:
- image: cimg/android:2021.08-ndk
resource_class: xlarge
steps:
- attach_workspace:
at: ~/workspace
- checkout
- run:
name: Upload
no_output_timeout: "1h"
command: |
script="/home/circleci/project/.circleci/unittest/android/scripts/binary_android_upload.sh"
cat "$script"
source "$script"
binary_macos_conda:
<<: *binary_common
macos:
xcode: "14.0"
steps:
- checkout_merge
- designate_upload_channel
- run:
command: |
curl -o conda.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh
sh conda.sh -b
source $HOME/miniconda3/bin/activate
conda install -yq conda-build
packaging/build_conda.sh
- store_artifacts:
path: /Users/distiller/miniconda3/conda-bld/osx-64
- persist_to_workspace:
root: /Users/distiller/miniconda3/conda-bld/osx-64
paths:
- "*"
- store_test_results:
path: build_results/
# Requires org-member context
binary_conda_upload:
docker:
- image: continuumio/miniconda
steps:
- attach_workspace:
at: ~/workspace
- designate_upload_channel
- run:
command: |
# Prevent credential from leaking
conda install -yq anaconda-client
set -x
anaconda -t "${CONDA_PYTORCHBOT_TOKEN}" upload ~/workspace/*.tar.bz2 -u "pytorch-${UPLOAD_CHANNEL}" --label main --no-progress --force
# Requires org-member context
binary_wheel_upload:
parameters:
subfolder:
description: "What whl subfolder to upload to, e.g., blank or cu100/ (trailing slash is important)"
type: string
docker:
- image: cimg/python:3.7
steps:
- attach_workspace:
at: ~/workspace
- designate_upload_channel
- checkout
- pip_install:
args: awscli
- run:
command: |
export PATH="$HOME/.local/bin:$PATH"
# Prevent credential from leaking
set +x
export AWS_ACCESS_KEY_ID="${PYTORCH_BINARY_AWS_ACCESS_KEY_ID}"
export AWS_SECRET_ACCESS_KEY="${PYTORCH_BINARY_AWS_SECRET_ACCESS_KEY}"
set -x
for pkg in ~/workspace/*.whl; do
aws s3 cp "$pkg" "s3://pytorch/whl/${UPLOAD_CHANNEL}/<< parameters.subfolder >>" --acl public-read
done
smoke_test_linux_conda:
<<: *smoke_test_common
steps:
- attach_workspace:
at: ~/workspace
- designate_upload_channel
- run:
name: install binaries
command: |
set -x
source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION}
conda install -v -y -c pytorch-nightly pytorch
conda install -v -y $(ls ~/workspace/torchvision*.tar.bz2)
- run:
name: smoke test
command: |
source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION}
python -c "import torchvision"
smoke_test_linux_pip:
<<: *smoke_test_common
steps:
- attach_workspace:
at: ~/workspace
- designate_upload_channel
- run:
name: install binaries
command: |
set -x
source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION}
- pip_install:
args: $(ls ~/workspace/torchvision*.whl) --pre -f https://download.pytorch.org/whl/nightly/torch_nightly.html
- run:
name: smoke test
command: |
source /usr/local/etc/profile.d/conda.sh && conda activate python${PYTHON_VERSION}
python -c "import torchvision"
smoke_test_docker_image_build:
machine:
image: ubuntu-2004:202104-01
resource_class: large
environment:
image_name: torchvision/smoke_test
steps:
- checkout
- designate_upload_channel
- run:
name: Build and push Docker image
no_output_timeout: "1h"
command: |
set +x
echo "${DOCKER_HUB_TOKEN}" | docker login --username "${DOCKER_HUB_USERNAME}" --password-stdin
set -x
cd .circleci/smoke_test/docker && docker build . -t ${image_name}:${CIRCLE_WORKFLOW_ID}
docker tag ${image_name}:${CIRCLE_WORKFLOW_ID} ${image_name}:latest
docker push ${image_name}:${CIRCLE_WORKFLOW_ID}
docker push ${image_name}:latest
smoke_test_win_conda:
<<: *binary_common
executor:
name: windows-cpu
steps:
- attach_workspace:
at: ~/workspace
- designate_upload_channel
- run:
name: install binaries
command: |
set -x
eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')"
conda env remove -n python${PYTHON_VERSION} || true
conda create -yn python${PYTHON_VERSION} python=${PYTHON_VERSION}
conda activate python${PYTHON_VERSION}
conda install -v -y -c pytorch-nightly pytorch
conda install -v -y $(ls ~/workspace/torchvision*.tar.bz2)
- run:
name: smoke test
command: |
eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')"
conda activate python${PYTHON_VERSION}
python -c "import torchvision"
smoke_test_win_pip:
<<: *binary_common
executor:
name: windows-cpu
steps:
- attach_workspace:
at: ~/workspace
- designate_upload_channel
- run:
name: install binaries
command: |
set -x
eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')"
conda create -yn python${PYTHON_VERSION} python=${PYTHON_VERSION}
conda activate python${PYTHON_VERSION}
- pip_install:
args: $(ls ~/workspace/torchvision*.whl) --pre -f https://download.pytorch.org/whl/nightly/torch_nightly.html
- run:
name: smoke test
command: |
eval "$('/C/tools/miniconda3/Scripts/conda.exe' 'shell.bash' 'hook')"
conda activate python${PYTHON_VERSION}
python -c "import torchvision"
unittest_linux_cpu:
<<: *binary_common
docker:
- image: "pytorch/manylinux-cpu"
resource_class: 2xlarge+
steps:
- checkout
- designate_upload_channel
- run:
name: Generate cache key
# This will refresh cache on Sundays, nightly build should generate new cache.
command: echo "$(date +"%Y-%U")" > .circleci-weekly
- restore_cache:
{% raw %}
keys:
- env-v2-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum ".circleci-weekly" }}
{% endraw %}
- run:
name: Setup
command: .circleci/unittest/linux/scripts/setup_env.sh
- save_cache:
{% raw %}
key: env-v2-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum ".circleci-weekly" }}
{% endraw %}
paths:
- conda
- env
- run:
name: Install torchvision
command: .circleci/unittest/linux/scripts/install.sh
- run:
name: Run tests
command: .circleci/unittest/linux/scripts/run_test.sh
- run:
name: Post process
command: .circleci/unittest/linux/scripts/post_process.sh
- store_test_results:
path: test-results
unittest_linux_gpu:
<<: *binary_common
machine:
image: ubuntu-2004-cuda-11.4:202110-01
resource_class: gpu.nvidia.medium
environment:
image_name: "pytorch/manylinux-cuda116"
CU_VERSION: << parameters.cu_version >>
PYTHON_VERSION: << parameters.python_version >>
steps:
- checkout
- designate_upload_channel
- run:
name: Generate cache key
# This will refresh cache on Sundays, nightly build should generate new cache.
command: echo "$(date +"%Y-%U")" > .circleci-weekly
- restore_cache:
{% raw %}
keys:
- env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum ".circleci-weekly" }}
{% endraw %}
- run:
name: Setup
command: docker run -e PYTHON_VERSION -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/setup_env.sh
- save_cache:
{% raw %}
key: env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum ".circleci-weekly" }}
{% endraw %}
paths:
- conda
- env
- run:
# Here we create an envlist file that contains some env variables that we want the docker container to be aware of.
# Normally, the CIRCLECI variable is set and available on all CI workflows: https://circleci.com/docs/2.0/env-vars/#built-in-environment-variables.
# They're avaiable in all the other workflows (OSX and Windows).
# But here, we're running the unittest_linux_gpu workflows in a docker container, where those variables aren't accessible.
# So instead we dump the variables we need in env.list and we pass that file when invoking "docker run".
name: export CIRCLECI env var
command: echo "CIRCLECI=true" >> ./env.list
- run:
name: Install torchvision
command: docker run -t --gpus all -v $PWD:$PWD -w $PWD -e UPLOAD_CHANNEL -e CU_VERSION "${image_name}" .circleci/unittest/linux/scripts/install.sh
- run:
name: Run tests
command: docker run --env-file ./env.list -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/run_test.sh
- run:
name: Post Process
command: docker run -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux/scripts/post_process.sh
- store_test_results:
path: test-results
unittest_windows_cpu:
<<: *binary_common
executor:
name: windows-cpu
steps:
- checkout
- designate_upload_channel
- run:
name: Generate cache key
# This will refresh cache on Sundays, nightly build should generate new cache.
command: echo "$(date +"%Y-%U")" > .circleci-weekly
- restore_cache:
{% raw %}
keys:
- env-v2-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/environment.yml" }}-{{ checksum ".circleci-weekly" }}
{% endraw %}
- run:
name: Setup
command: .circleci/unittest/windows/scripts/setup_env.sh
- save_cache:
{% raw %}
key: env-v2-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/environment.yml" }}-{{ checksum ".circleci-weekly" }}
{% endraw %}
paths:
- conda
- env
- run:
name: Install torchvision
command: .circleci/unittest/windows/scripts/install.sh
- run:
name: Run tests
command: .circleci/unittest/windows/scripts/run_test.sh
- run:
name: Post process
command: .circleci/unittest/windows/scripts/post_process.sh
- store_test_results:
path: test-results
unittest_windows_gpu:
<<: *binary_common
executor:
name: windows-gpu
environment:
CUDA_VERSION: "11.6"
PYTHON_VERSION: << parameters.python_version >>
steps:
- checkout
- designate_upload_channel
- run:
name: Generate cache key
# This will refresh cache on Sundays, nightly build should generate new cache.
command: echo "$(date +"%Y-%U")" > .circleci-weekly
- restore_cache:
{% raw %}
keys:
- env-v1-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/environment.yml" }}-{{ checksum ".circleci-weekly" }}
{% endraw %}
- run:
name: Setup
command: .circleci/unittest/windows/scripts/setup_env.sh
- save_cache:
{% raw %}
key: env-v1-windows-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/windows/scripts/environment.yml" }}-{{ checksum ".circleci-weekly" }}
{% endraw %}
paths:
- conda
- env
- run:
name: Install CUDA
command: packaging/windows/internal/cuda_install.bat
- run:
name: Update CUDA driver
command: packaging/windows/internal/driver_update.bat
- run:
name: Install torchvision
command: .circleci/unittest/windows/scripts/install.sh
- run:
name: Run tests
command: .circleci/unittest/windows/scripts/run_test.sh
- run:
name: Post process
command: .circleci/unittest/windows/scripts/post_process.sh
- store_test_results:
path: test-results
unittest_macos_cpu:
<<: *binary_common
macos:
xcode: "14.0"
resource_class: large
steps:
- checkout
- designate_upload_channel
- run:
name: Install wget
command: HOMEBREW_NO_AUTO_UPDATE=1 brew install wget
# Disable brew auto update which is very slow
- run:
name: Generate cache key
# This will refresh cache on Sundays, nightly build should generate new cache.
command: echo "$(date +"%Y-%U")" > .circleci-weekly
- restore_cache:
{% raw %}
keys:
- env-v3-macos-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum ".circleci-weekly" }}
{% endraw %}
- run:
name: Setup
command: .circleci/unittest/linux/scripts/setup_env.sh
- save_cache:
{% raw %}
key: env-v3-macos-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux/scripts/environment.yml" }}-{{ checksum ".circleci-weekly" }}
{% endraw %}
paths:
- conda
- env
- run:
name: Install torchvision
command: .circleci/unittest/linux/scripts/install.sh
- run:
name: Run tests
command: .circleci/unittest/linux/scripts/run_test.sh
- run:
name: Post process
command: .circleci/unittest/linux/scripts/post_process.sh
- store_test_results:
path: test-results
cmake_linux_cpu:
<<: *binary_common
docker:
- image: "pytorch/manylinux-cpu"
resource_class: 2xlarge+
steps:
- checkout_merge
- designate_upload_channel
- run:
name: Setup conda
command: .circleci/unittest/linux/scripts/setup_env.sh
- run: packaging/build_cmake.sh
cmake_linux_gpu:
<<: *binary_common
machine:
image: ubuntu-2004-cuda-11.4:202110-01
resource_class: gpu.nvidia.small
environment:
PYTHON_VERSION: << parameters.python_version >>
PYTORCH_VERSION: << parameters.pytorch_version >>
UNICODE_ABI: << parameters.unicode_abi >>
CU_VERSION: << parameters.cu_version >>
steps:
- checkout_merge
- designate_upload_channel
- run:
name: Setup conda
command: docker run -e CU_VERSION -e PYTHON_VERSION -e UNICODE_ABI -e PYTORCH_VERSION -t --gpus all -v $PWD:$PWD -w $PWD << parameters.wheel_docker_image >> .circleci/unittest/linux/scripts/setup_env.sh
- run:
name: Build torchvision C++ distribution and test
no_output_timeout: 30m
command: docker run -e CU_VERSION -e PYTHON_VERSION -e UNICODE_ABI -e PYTORCH_VERSION -e UPLOAD_CHANNEL -t --gpus all -v $PWD:$PWD -w $PWD << parameters.wheel_docker_image >> packaging/build_cmake.sh
cmake_macos_cpu:
<<: *binary_common
macos:
xcode: "14.0"
steps:
- checkout_merge
- designate_upload_channel
- run:
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
conda install -yq conda-build cmake
packaging/build_cmake.sh