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Karthik Prasadfacebook-github-bot
Karthik Prasad
authored and
facebook-github-bot
committed
Move CI to Github Actions (#620)
Summary: Moves the continuous integration (CI) processy from CircleCI to Github Actions. The changes include creating a new ci.yml file to define the CI workflow that largely mimics the existing circleCi config. Differential Revision: D53107145
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‎.github/workflows/ci.yml

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name: CI
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on:
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push:
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branches:
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- main
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pull_request:
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branches:
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- main
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schedule:
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- cron: '4 4 * * *' # This schedule runs the nightly job every night at 4:04AM
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jobs:
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lint_py39_torch_release:
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runs-on: ubuntu-latest
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steps:
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- name: Checkout
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uses: actions/checkout@v2
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- name: Set up Python
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uses: actions/setup-python@v2
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with:
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python-version: 3.9
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install flake8 black isort
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./scripts/install_via_pip.sh
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- name: Lint with flake8
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run: flake8 --config ./.github/workflows/flake8_config.ini
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- name: Lint with black
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run: black --check --diff --color .
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- name: Check import order with isort
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run: isort -v -l 88 -o opacus --lines-after-imports 2 -m 3 --trailing-comma --check-only .
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unittest_py38_torch_release:
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runs-on: ubuntu-latest
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steps:
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- name: Checkout
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uses: actions/checkout@v2
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- name: Set up Python
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uses: actions/setup-python@v2
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with:
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python-version: 3.8
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install pytest coverage coveralls
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./scripts/install_via_pip.sh
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- name: Run unit tests
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run: |
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mkdir unittest-py38-release-reports
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coverage run -m pytest --doctest-modules -p conftest --junitxml=unittest-py38-release-reports/junit.xml opacus
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coverage report -i -m
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- name: Store test results
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uses: actions/upload-artifact@v2
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with:
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name: unittest-py38-release-reports
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path: unittest-py38-release-reports
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unittest_py39_torch_release:
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runs-on: ubuntu-latest
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steps:
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- name: Checkout
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uses: actions/checkout@v2
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- name: Set up Python
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uses: actions/setup-python@v2
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with:
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python-version: 3.9
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install pytest coverage coveralls
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./scripts/install_via_pip.sh
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- name: Run unit tests
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run: |
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mkdir unittest-py39-release-reports
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coverage run -m pytest --doctest-modules -p conftest --junitxml=unittest-py39-release-reports/junit.xml opacus
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coverage report -i -m
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- name: Store test results
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uses: actions/upload-artifact@v2
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with:
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name: unittest-py39-release-reports
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path: unittest-py39-release-reports
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unittest_py39_torch_nightly:
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runs-on: ubuntu-latest
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if: ${{ github.event_name == 'schedule' }}
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steps:
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- name: Checkout
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uses: actions/checkout@v2
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- name: Set up Python
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uses: actions/setup-python@v2
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with:
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python-version: 3.9
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install pytest coverage coveralls
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./scripts/install_via_pip.sh -n
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- name: Run unit tests
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run: |
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mkdir unittest-py39-nightly-reports
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coverage run -m pytest --doctest-modules -p conftest --junitxml=unittest-py39-nightly-reports/junit.xml opacus
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coverage report -i -m
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- name: Store test results
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uses: actions/upload-artifact@v2
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with:
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name: unittest-py39-nightly-reports
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path: unittest-py39-nightly-reports
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prv_accountant_values:
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runs-on: ubuntu-latest
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steps:
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- name: Checkout
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uses: actions/checkout@v2
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- name: Set up Python
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uses: actions/setup-python@v2
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with:
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python-version: 3.9
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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./scripts/install_via_pip.sh
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- name: Run prv accountant unit tests
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run: |
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python -m unittest opacus.tests.prv_accountant
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integrationtest_py39_torch_release_cpu:
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runs-on: ubuntu-latest
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steps:
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- name: Checkout
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uses: actions/checkout@v2
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- name: Set up Python
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uses: actions/setup-python@v2
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with:
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python-version: 3.9
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install pytest coverage coveralls
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./scripts/install_via_pip.sh
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- name: Run MNIST integration test (CPU)
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run: |
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mkdir -p runs/mnist/data
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mkdir -p runs/mnist/test-reports
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coverage run examples/mnist.py --lr 0.25 --sigma 0.7 -c 1.5 --batch-size 64 --epochs 1 --data-root runs/mnist/data --n-runs 1 --device cpu
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python -c "import torch; accuracy = torch.load('run_results_mnist_0.25_0.7_1.5_64_1.pt'); exit(0) if (accuracy[0]>0.78 and accuracy[0]<0.95) else exit(1)"
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coverage report -i -m
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- name: Store test results
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uses: actions/upload-artifact@v2
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with:
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name: mnist-cpu-reports
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path: runs/mnist/test-reports
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integrationtest_py39_torch_release_cuda:
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runs-on: ubuntu-latest
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needs: [unittest_py39_torch_release]
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container:
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# https://hub.docker.com/r/nvidia/cuda
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image: nvidia/cuda:12.3.1-base-ubuntu22.04
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options: --gpus all
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env:
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TZ: 'UTC'
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steps:
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- name: Checkout
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uses: actions/checkout@v2
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- name: Set up Python
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uses: actions/setup-python@v2
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with:
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python-version: 3.9
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install pytest coverage coveralls
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./scripts/install_via_pip.sh -c
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- name: Install CUDA toolkit and cuDNN
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run: |
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apt-get update
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apt-get install -y --no-install-recommends \
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cuda-toolkit-11-1 \
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libcudnn8=8.1.1.33-1+cuda11.1 \
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libcudnn8-dev=8.1.1.33-1+cuda11.1
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- name: Run MNIST integration test (CUDA)
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run: |
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mkdir -p runs/mnist/data
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mkdir -p runs/mnist/test-reports
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python examples/mnist.py --lr 0.25 --sigma 0.7 -c 1.5 --batch-size 64 --epochs 1 --data-root runs/mnist/data --n-runs 1 --device cuda
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python -c "import torch; accuracy = torch.load('run_results_mnist_0.25_0.7_1.5_64_1.pt'); exit(0) if (accuracy[0]>0.78 and accuracy[0]<0.95) else exit(1)"
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- name: Store MNIST test results
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uses: actions/upload-artifact@v2
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with:
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name: mnist-gpu-reports
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path: runs/mnist/test-reports
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- name: Run CIFAR10 integration test (CUDA)
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run: |
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mkdir -p runs/cifar10/data
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mkdir -p runs/cifar10/logs
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mkdir -p runs/cifar10/test-reports
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pip install tensorboard
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python examples/cifar10.py --lr 0.1 --sigma 1.5 -c 10 --batch-size 2000 --epochs 10 --data-root runs/cifar10/data --log-dir runs/cifar10/logs --device cuda
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python -c "import torch; model = torch.load('model_best.pth.tar'); exit(0) if (model['best_acc1']>0.4 and model['best_acc1']<0.49) else exit(1)"
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python examples/cifar10.py --lr 0.1 --sigma 1.5 -c 10 --batch-size 2000 --epochs 10 --data-root runs/cifar10/data --log-dir runs/cifar10/logs --device cuda --grad_sample_mode no_op
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python -c "import torch; model = torch.load('model_best.pth.tar'); exit(0) if (model['best_acc1']>0.4 and model['best_acc1']<0.49) else exit(1)"
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- name: Store CIFAR10 test results
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uses: actions/upload-artifact@v2
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with:
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name: cifar10-gpu-reports
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path: runs/cifar10/test-reports
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- name: Run IMDb integration test (CUDA)
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run: |
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mkdir -p runs/imdb/data
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mkdir -p runs/imdb/test-reports
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pip install --user datasets transformers
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python examples/imdb.py --lr 0.02 --sigma 1.0 -c 1.0 --batch-size 64 --max-sequence-length 256 --epochs 2 --data-root runs/imdb/data --device cuda
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python -c "import torch; accuracy = torch.load('run_results_imdb_classification.pt'); exit(0) if (accuracy>0.54 and accuracy<0.66) else exit(1)"
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- name: Store IMDb test results
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uses: actions/upload-artifact@v2
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with:
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name: imdb-gpu-reports
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path: runs/imdb/test-reports
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- name: Run charlstm integration test (CUDA)
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run: |
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mkdir -p runs/charlstm/data
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wget https://download.pytorch.org/tutorial/data.zip -O runs/charlstm/data/data.zip
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unzip runs/charlstm/data/data.zip -d runs/charlstm/data
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rm runs/charlstm/data/data.zip
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mkdir -p runs/charlstm/test-reports
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pip install scikit-learn
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python examples/char-lstm-classification.py --epochs=20 --learning-rate=2.0 --hidden-size=128 --delta=8e-5 --batch-size 400 --n-layers=1 --sigma=1.0 --max-per-sample-grad-norm=1.5 --data-root="runs/charlstm/data/data/names/" --device cuda --test-every 5
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python -c "import torch; accuracy = torch.load('run_results_chr_lstm_classification.pt'); exit(0) if (accuracy>0.60 and accuracy<0.80) else exit(1)"
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- name: Store test results
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uses: actions/upload-artifact@v2
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with:
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name: charlstm-gpu-reports
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path: runs/charlstm/test-reports
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micro_benchmarks_py39_torch_release_cuda:
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runs-on: ubuntu-latest
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needs: [integrationtest_py39_torch_release_cuda]
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container:
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# https://hub.docker.com/r/nvidia/cuda
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image: nvidia/cuda:12.3.1-base-ubuntu22.04
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options: --gpus all
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env:
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TZ: 'UTC'
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steps:
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- name: Checkout
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uses: actions/checkout@v2
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- name: Set up Python
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uses: actions/setup-python@v2
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with:
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python-version: 3.9
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install pytest coverage coveralls
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./scripts/install_via_pip.sh
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- name: Install CUDA toolkit and cuDNN
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run: |
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apt-get update
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apt-get install -y --no-install-recommends \
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cuda-toolkit-11-1 \
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libcudnn8=8.1.1.33-1+cuda11.1 \
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libcudnn8-dev=8.1.1.33-1+cuda11.1
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- name: Run benchmark integration tests (CUDA)
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run: |
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mkdir -p benchmarks/results/raw
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python benchmarks/run_benchmarks.py --batch_size 16 --layers "groupnorm instancenorm layernorm" --config_file ./benchmarks/config.json --root ./benchmarks/results/raw/ --cont
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IFS=$' ';layers=("groupnorm" "instancenorm" "layernorm"); rm -rf /tmp/report_layers; mkdir -p /tmp/report_layers; IFS=$'\n'; files=`( echo "${layers[*]}" ) | sed 's/.*/.\/benchmarks\/results\/raw\/&*/'`
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cp -v ${files[@]} /tmp/report_layers
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report_id=`IFS=$'-'; echo "${layers[*]}"`
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python benchmarks/generate_report.py --path-to-results /tmp/report_layers --save-path benchmarks/results/report-${report_id}.csv --format csv
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python benchmarks/generate_report.py --path-to-results /tmp/report_layers --save-path benchmarks/results/report-${report_id}.pkl --format pkl
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python benchmarks/check_threshold.py --report-path "./benchmarks/results/report-"$report_id".pkl" --metric runtime --threshold 3.0 --column "hooks/baseline"
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python benchmarks/check_threshold.py --report-path "./benchmarks/results/report-"$report_id".pkl" --metric memory --threshold 1.6 --column "hooks/baseline"
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- name: Store artifacts
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uses: actions/upload-artifact@v2
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with:
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name: benchmarks-reports
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path: benchmarks/results/
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unittest_multi_gpu:
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runs-on: linux.4xlarge.nvidia.gpu
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steps:
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- name: Checkout
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uses: actions/checkout@v2
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- name: Set up Python
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uses: actions/setup-python@v2
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with:
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python-version: 3.x
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install pytest coverage coveralls
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./scripts/install_via_pip.sh -c
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- name: Run multi-GPU unit tests
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run: |
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nvidia-smi
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nvcc --version | grep "cuda_"
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mkdir unittest-multigpu-reports
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coverage run -m unittest opacus.tests.multigpu_gradcheck.GradientComputationTest.test_gradient_correct
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coverage report -i -m
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- name: Store test results
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uses: actions/upload-artifact@v2
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with:
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name: unittest-multigpu-reports
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path: unittest-multigpu-reports
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finish_coveralls_parallel:
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runs-on: ubuntu-latest
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steps:
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- name: Checkout
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uses: actions/checkout@v2
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- name: Finish Coveralls Parallel
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run: |
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python -m pip install --upgrade pip
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pip install coveralls --user
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coveralls --finish
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- name: coveralls upload
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timeout-minutes: 5
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run: |
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python -m pip install --upgrade pip
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pip install coveralls --user
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COVERALLS_PARALLEL=true COVERALLS_FLAG_NAME="${GITHUB_JOB}" coveralls

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