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c827e94da0
* remove libsndfile1-dev and libgl1 from workflows and ensure that re present in the respective dockerfiles.
* change to self-hosted runner; let's see 🤞
* add libsndfile1-dev libgl1 for now
* use self-hosted runners for building and push too.
53 lines
1.6 KiB
YAML
53 lines
1.6 KiB
YAML
name: Benchmarking tests
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on:
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workflow_dispatch:
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schedule:
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- cron: "30 1 1,15 * *" # every 2 weeks on the 1st and the 15th of every month at 1:30 AM
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env:
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DIFFUSERS_IS_CI: yes
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HF_HOME: /mnt/cache
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OMP_NUM_THREADS: 8
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MKL_NUM_THREADS: 8
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jobs:
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torch_pipelines_cuda_benchmark_tests:
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name: Torch Core Pipelines CUDA Benchmarking Tests
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strategy:
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fail-fast: false
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max-parallel: 1
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runs-on: [single-gpu, nvidia-gpu, a10, ci]
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container:
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image: diffusers/diffusers-pytorch-cuda
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options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/ --gpus 0
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steps:
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- name: Checkout diffusers
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uses: actions/checkout@v3
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with:
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fetch-depth: 2
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- name: NVIDIA-SMI
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run: |
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nvidia-smi
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- name: Install dependencies
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run: |
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python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
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python -m uv pip install -e [quality,test]
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python -m uv pip install pandas peft
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- name: Environment
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run: |
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python utils/print_env.py
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- name: Diffusers Benchmarking
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env:
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HUGGING_FACE_HUB_TOKEN: ${{ secrets.DIFFUSERS_BOT_TOKEN }}
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BASE_PATH: benchmark_outputs
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run: |
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export TOTAL_GPU_MEMORY=$(python -c "import torch; print(torch.cuda.get_device_properties(0).total_memory / (1024**3))")
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cd benchmarks && mkdir ${BASE_PATH} && python run_all.py && python push_results.py
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- name: Test suite reports artifacts
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if: ${{ always() }}
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uses: actions/upload-artifact@v2
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with:
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name: benchmark_test_reports
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path: benchmarks/benchmark_outputs |