251 lines
7.6 KiB
YAML
251 lines
7.6 KiB
YAML
# Use the latest 2.1 version of CircleCI pipeline process engine.
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# See: https://circleci.com/docs/2.0/configuration-reference
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version: 2.1
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# the default pipeline parameters, which will be updated according to
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# the results of the path-filtering orb
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parameters:
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lint_only:
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type: boolean
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default: true
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# Define a job to be invoked later in a workflow.
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# See: https://circleci.com/docs/2.0/configuration-reference/#jobs
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jobs:
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lint:
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docker:
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- image: cimg/python:3.7.4
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# Add steps to the job
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# See: https://circleci.com/docs/2.0/configuration-reference/#steps
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steps:
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- checkout
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- run:
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name: Install pre-commit hook
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command: |
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pip install pre-commit
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pre-commit install
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- run:
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name: Linting
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command: pre-commit run --all-files
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- run:
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name: Check docstring coverage
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command: |
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pip install interrogate
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interrogate -v --ignore-init-method --ignore-module --ignore-nested-functions --ignore-magic --ignore-regex "__repr__" --fail-under 60 mmpretrain
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build_cpu_with_3rdparty:
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parameters:
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# The python version must match available image tags in
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# https://circleci.com/developer/images/image/cimg/python
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python:
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type: string
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torch:
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type: string
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torchvision:
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type: string
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docker:
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- image: cimg/python:<< parameters.python >>
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resource_class: large
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steps:
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- checkout
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- run:
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name: Install Libraries
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command: |
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sudo apt-get update
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sudo apt-get install -y libjpeg8-dev zlib1g-dev
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- run:
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name: Configure Python & pip
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command: |
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pip install --upgrade pip
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pip install wheel
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- run:
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name: Install PyTorch
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command: |
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python -V
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pip install torch==<< parameters.torch >>+cpu torchvision==<< parameters.torchvision >>+cpu -f https://download.pytorch.org/whl/torch_stable.html
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- run:
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name: Install mmpretrain dependencies
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command: |
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pip install git+https://github.com/open-mmlab/mmengine.git@main
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pip install -U openmim
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mim install 'mmcv >= 2.0.0rc4'
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pip install timm
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pip install transformers
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pip install -r requirements.txt
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python -c 'import mmcv; print(mmcv.__version__)'
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- run:
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name: Build and install
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command: |
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pip install -e .
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- run:
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name: Run unittests
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command: |
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coverage run --branch --source mmpretrain -m pytest tests/
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coverage xml
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coverage report -m
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build_cpu:
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parameters:
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# The python version must match available image tags in
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# https://circleci.com/developer/images/image/cimg/python
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python:
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type: string
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torch:
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type: string
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torchvision:
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type: string
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docker:
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- image: cimg/python:<< parameters.python >>
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resource_class: large
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steps:
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- checkout
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- run:
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name: Install Libraries
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command: |
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sudo apt-get update
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sudo apt-get install -y libjpeg8-dev zlib1g-dev
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- run:
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name: Configure Python & pip
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command: |
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pip install --upgrade pip
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pip install wheel
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- run:
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name: Install PyTorch
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command: |
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python -V
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pip install torch==<< parameters.torch >>+cpu torchvision==<< parameters.torchvision >>+cpu -f https://download.pytorch.org/whl/torch_stable.html
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- run:
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name: Install mmpretrain dependencies
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command: |
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pip install git+https://github.com/open-mmlab/mmengine.git@main
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pip install -U openmim
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mim install 'mmcv >= 2.0.0rc4'
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pip install -r requirements.txt
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python -c 'import mmcv; print(mmcv.__version__)'
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- run:
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name: Build and install
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command: |
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pip install -e .
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- run:
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name: Run unittests
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command: |
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coverage run --branch --source mmpretrain -m pytest tests/
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coverage xml
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coverage report -m
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build_cuda:
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machine:
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image: ubuntu-2004-cuda-11.4:202110-01
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resource_class: gpu.nvidia.small
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environment:
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MKL_SERVICE_FORCE_INTEL: 1
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parameters:
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torch:
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type: string
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cuda:
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type: enum
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enum: ["11.1", "11.7"]
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cudnn:
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type: integer
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default: 8
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steps:
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- checkout
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- run:
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# Cloning repos in VM since Docker doesn't have access to the private key
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name: Clone Repos
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command: |
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git clone -b main --depth 1 https://github.com/open-mmlab/mmengine.git /home/circleci/mmengine
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- run:
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name: Build Docker image
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command: |
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docker build .circleci/docker -t mmpretrain:gpu --build-arg PYTORCH=<< parameters.torch >> --build-arg CUDA=<< parameters.cuda >> --build-arg CUDNN=<< parameters.cudnn >>
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docker run --gpus all -t -d -v /home/circleci/project:/mmpretrain -v /home/circleci/mmengine:/mmengine -w /mmpretrain --name mmpretrain mmpretrain:gpu
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- run:
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name: Install mmpretrain dependencies
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command: |
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docker exec mmpretrain pip install -e /mmengine
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docker exec mmpretrain pip install -U openmim
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docker exec mmpretrain mim install 'mmcv >= 2.0.0rc4'
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docker exec mmpretrain pip install -r requirements.txt
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docker exec mmpretrain python -c 'import mmcv; print(mmcv.__version__)'
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- run:
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name: Build and install
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command: |
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docker exec mmpretrain pip install -e .
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- run:
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name: Run unittests
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command: |
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docker exec mmpretrain python -m pytest tests/
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# Invoke jobs via workflows
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# See: https://circleci.com/docs/2.0/configuration-reference/#workflows
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workflows:
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pr_stage_lint:
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when: << pipeline.parameters.lint_only >>
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jobs:
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- lint:
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name: lint
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filters:
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branches:
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ignore:
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- dev
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- main
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pr_stage_test:
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when:
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not:
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<< pipeline.parameters.lint_only >>
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jobs:
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- lint:
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name: lint
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filters:
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branches:
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ignore:
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- dev
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- build_cpu:
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name: minimum_version_cpu
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torch: 1.8.0
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torchvision: 0.9.0
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python: 3.7.16
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requires:
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- lint
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- build_cpu_with_3rdparty:
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name: maximum_version_cpu
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torch: 2.0.0
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torchvision: 0.15.1
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python: 3.10.0
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requires:
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- minimum_version_cpu
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- hold:
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type: approval
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requires:
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- maximum_version_cpu
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- build_cuda:
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name: mainstream_version_gpu
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torch: 1.8.1
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# Use double quotation mark to explicitly specify its type
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# as string instead of number
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cuda: "11.1"
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requires:
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- hold
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- build_cuda:
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name: maximum_version_gpu
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torch: 2.0.0
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cuda: "11.7"
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cudnn: 8
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requires:
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- hold
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merge_stage_test:
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when:
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not:
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<< pipeline.parameters.lint_only >>
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jobs:
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- build_cuda:
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name: minimum_version_gpu
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torch: 1.8.0
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# Use double quotation mark to explicitly specify its type
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# as string instead of number
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cuda: "11.1"
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filters:
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branches:
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only:
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- pretrain
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