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self-nightly-scheduled.yml
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name: Self-hosted runner; Nightly (scheduled)
on:
push:
branches:
- nightly_ci*
repository_dispatch:
schedule:
- cron: "0 0 */3 * *"
env:
HF_HOME: /mnt/cache
TRANSFORMERS_IS_CI: yes
RUN_SLOW: yes
OMP_NUM_THREADS: 16
MKL_NUM_THREADS: 16
PYTEST_TIMEOUT: 600
SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
jobs:
run_all_tests_torch_gpu:
runs-on: [self-hosted, docker-gpu, single-gpu]
container:
image: pytorch/pytorch:1.9.0-cuda11.1-cudnn8-runtime
options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
steps:
- name: Launcher docker
uses: actions/checkout@v2
- name: NVIDIA-SMI
run: |
nvidia-smi
- name: Install dependencies
run: |
apt -y update && apt install -y libsndfile1-dev git
pip install --upgrade pip
pip install .[integrations,sklearn,testing,onnxruntime,sentencepiece,torch-speech,vision,timm]
pip install --pre torch torchvision torchaudio -f https://download.pytorch.org/whl/nightly/cu111/torch_nightly.html -U
- name: Are GPUs recognized by our DL frameworks
run: |
python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
python -c "import torch; print('Cuda version:', torch.version.cuda)"
python -c "import torch; print('CuDNN version:', torch.backends.cudnn.version())"
python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
- name: Run all tests on GPU
run: |
python -m pytest -n 1 -v --dist=loadfile --make-reports=tests_torch_gpu tests
- name: Failure short reports
if: ${{ always() }}
run: cat reports/tests_torch_gpu_failures_short.txt
- name: Run examples tests on GPU
if: ${{ always() }}
env:
OMP_NUM_THREADS: 16
MKL_NUM_THREADS: 16
RUN_SLOW: yes
HF_HOME: /mnt/cache
TRANSFORMERS_IS_CI: yes
run: |
pip install -r examples/pytorch/_tests_requirements.txt
python -m pytest -n 1 -v --dist=loadfile --make-reports=examples_torch_gpu examples
- name: Failure short reports
if: ${{ always() }}
run: cat reports/examples_torch_gpu_failures_short.txt
- name: Run all pipeline tests on GPU
if: ${{ always() }}
env:
RUN_PIPELINE_TESTS: yes
run: |
python -m pytest -n 1 -v --dist=loadfile -m is_pipeline_test --make-reports=tests_torch_pipeline_gpu tests
- name: Failure short reports
if: ${{ always() }}
run: cat reports/tests_torch_pipeline_gpu_failures_short.txt
- name: Test suite reports artifacts
if: ${{ always() }}
uses: actions/upload-artifact@v2
with:
name: run_all_tests_torch_gpu_test_reports
path: reports
run_all_tests_torch_multi_gpu:
runs-on: [self-hosted, docker-gpu, multi-gpu]
container:
image: pytorch/pytorch:1.9.0-cuda11.1-cudnn8-runtime
options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
steps:
- name: Launcher docker
uses: actions/checkout@v2
- name: NVIDIA-SMI
continue-on-error: true
run: |
nvidia-smi
- name: Install dependencies
run: |
apt -y update && apt install -y libsndfile1-dev git
pip install --upgrade pip
pip install .[integrations,sklearn,testing,onnxruntime,sentencepiece,torch-speech,vision,timm]
pip install --pre torch torchvision torchaudio -f https://download.pytorch.org/whl/nightly/cu111/torch_nightly.html -U
- name: Are GPUs recognized by our DL frameworks
run: |
python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
python -c "import torch; print('Cuda version:', torch.version.cuda)"
python -c "import torch; print('CuDNN version:', torch.backends.cudnn.version())"
python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
- name: Run all tests on GPU
env:
MKL_SERVICE_FORCE_INTEL: 1
run: |
python -m pytest -n 1 -v --dist=loadfile --make-reports=tests_torch_multi_gpu tests
- name: Failure short reports
if: ${{ always() }}
run: cat reports/tests_torch_multi_gpu_failures_short.txt
- name: Run all pipeline tests on GPU
if: ${{ always() }}
env:
RUN_PIPELINE_TESTS: yes
run: |
python -m pytest -n 1 -v --dist=loadfile -m is_pipeline_test --make-reports=tests_torch_pipeline_multi_gpu tests
- name: Failure short reports
if: ${{ always() }}
run: cat reports/tests_torch_pipeline_multi_gpu_failures_short.txt
- name: Test suite reports artifacts
if: ${{ always() }}
uses: actions/upload-artifact@v2
with:
name: run_all_tests_torch_multi_gpu_test_reports
path: reports
run_all_tests_torch_cuda_extensions_gpu:
runs-on: [self-hosted, docker-gpu, single-gpu]
container:
image: nvcr.io/nvidia/pytorch:21.03-py3
options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
steps:
- name: Launcher docker
uses: actions/checkout@v2
- name: NVIDIA-SMI
run: |
nvidia-smi
- name: Install dependencies
run: |
apt -y update && apt install -y libaio-dev
pip install --upgrade pip
pip install --pre torch torchvision torchaudio -f https://download.pytorch.org/whl/nightly/cu111/torch_nightly.html -U
pip install .[testing,deepspeed]
pip install git+https://github.com/microsoft/DeepSpeed
- name: Are GPUs recognized by our DL frameworks
run: |
python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
python -c "import torch; print('Cuda version:', torch.version.cuda)"
python -c "import torch; print('CuDNN version:', torch.backends.cudnn.version())"
python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
- name: Run all tests on GPU
run: |
python -m pytest -n 1 -v --dist=loadfile --make-reports=tests_torch_cuda_extensions_gpu tests/deepspeed tests/extended
- name: Failure short reports
if: ${{ always() }}
run: cat reports/tests_torch_cuda_extensions_gpu_failures_short.txt
- name: Test suite reports artifacts
if: ${{ always() }}
uses: actions/upload-artifact@v2
with:
name: run_tests_torch_cuda_extensions_gpu_test_reports
path: reports
run_all_tests_torch_cuda_extensions_multi_gpu:
runs-on: [self-hosted, docker-gpu, multi-gpu]
container:
image: nvcr.io/nvidia/pytorch:21.03-py3
options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
steps:
- name: Launcher docker
uses: actions/checkout@v2
- name: NVIDIA-SMI
continue-on-error: true
run: |
nvidia-smi
- name: Install dependencies
run: |
apt -y update && apt install -y libaio-dev
pip install --upgrade pip
pip install --pre torch torchvision torchaudio -f https://download.pytorch.org/whl/nightly/cu111/torch_nightly.html -U
rm -rf ~/.cache/torch_extensions/ # shared between conflicting builds
pip install .[testing,fairscale]
pip install git+https://github.com/microsoft/DeepSpeed # testing bleeding edge
- name: Are GPUs recognized by our DL frameworks
run: |
python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
python -c "import torch; print('Cuda version:', torch.version.cuda)"
python -c "import torch; print('CuDNN version:', torch.backends.cudnn.version())"
python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
- name: Run all tests on GPU
run: |
python -m pytest -n 1 -v --dist=loadfile --make-reports=tests_torch_cuda_extensions_multi_gpu tests/deepspeed tests/extended
- name: Failure short reports
if: ${{ always() }}
run: cat reports/tests_torch_cuda_extensions_multi_gpu_failures_short.txt
- name: Test suite reports artifacts
if: ${{ always() }}
uses: actions/upload-artifact@v2
with:
name: run_tests_torch_cuda_extensions_multi_gpu_test_reports
path: reports
send_results:
name: Send results to webhook
runs-on: ubuntu-latest
if: always()
needs: [
run_all_tests_torch_gpu,
run_all_tests_torch_multi_gpu,
run_all_tests_torch_cuda_extensions_gpu,
run_all_tests_torch_cuda_extensions_multi_gpu
]
steps:
- uses: actions/checkout@v2
- uses: actions/download-artifact@v2
- name: Send message to Slack
env:
CI_SLACK_BOT_TOKEN: ${{ secrets.CI_SLACK_BOT_TOKEN }}
CI_SLACK_CHANNEL_ID: ${{ secrets.CI_SLACK_CHANNEL_ID }}
CI_SLACK_CHANNEL_ID_DAILY: ${{ secrets.CI_SLACK_CHANNEL_ID_DAILY }}
CI_SLACK_CHANNEL_ID_PAST_FUTURE: ${{ secrets.CI_SLACK_CHANNEL_ID_PAST_FUTURE }}
run: |
pip install slack_sdk
python utils/notification_service.py scheduled nightly-torch