transformers/.github/workflows/self-scheduled.yml
Stas Bekman 805a202e1a
[CIs] report slow tests add --durations=0 to some pytest jobs (#7884)
* add --durations=50 to some pytest runs

* report all tests
2020-10-19 08:23:14 -04:00

134 lines
3.9 KiB
YAML

name: Self-hosted runner (scheduled)
on:
push:
branches:
- ci_*
repository_dispatch:
schedule:
- cron: "0 0 * * *"
jobs:
run_all_tests_torch_and_tf_gpu:
runs-on: [self-hosted, single-gpu]
steps:
- uses: actions/checkout@v2
- name: Loading cache.
uses: actions/cache@v2
id: cache
with:
path: .env
key: v0-slow_tests_tf_torch_gpu-${{ hashFiles('setup.py') }}
- name: Python version
run: |
which python
python --version
pip --version
- name: Current dir
run: pwd
- run: nvidia-smi
- name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
if: steps.cache.outputs.cache-hit != 'true'
run: |
python -m venv .env
source .env/bin/activate
which python
python --version
pip --version
- name: Install dependencies
run: |
source .env/bin/activate
pip install --upgrade pip
pip install torch!=1.6.0
pip install .[sklearn,testing,onnxruntime]
pip install git+https://github.com/huggingface/datasets
- name: Are GPUs recognized by our DL frameworks
run: |
source .env/bin/activate
python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
- name: Run all tests on GPU
env:
TF_FORCE_GPU_ALLOW_GROWTH: "true"
OMP_NUM_THREADS: 1
RUN_SLOW: yes
run: |
source .env/bin/activate
python -m pytest -n 1 --dist=loadfile -s ./tests/ --durations=0
- name: Run examples tests on GPU
env:
TF_FORCE_GPU_ALLOW_GROWTH: "true"
OMP_NUM_THREADS: 1
RUN_SLOW: yes
run: |
source .env/bin/activate
pip install -r examples/requirements.txt
python -m pytest -n 1 --dist=loadfile -s examples --durations=0
run_all_tests_torch_and_tf_multiple_gpu:
runs-on: [self-hosted, multi-gpu]
steps:
- uses: actions/checkout@v2
- name: Loading cache.
uses: actions/cache@v2
id: cache
with:
path: .env
key: v0-slow_tests_tf_torch_multi_gpu-${{ hashFiles('setup.py') }}
- name: Python version
run: |
which python
python --version
pip --version
- name: Current dir
run: pwd
- run: nvidia-smi
- name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
if: steps.cache.outputs.cache-hit != 'true'
run: |
python -m venv .env
source .env/bin/activate
which python
python --version
pip --version
- name: Install dependencies
run: |
source .env/bin/activate
pip install --upgrade pip
pip install torch!=1.6.0
pip install .[sklearn,testing,onnxruntime]
pip install git+https://github.com/huggingface/datasets
- name: Are GPUs recognized by our DL frameworks
run: |
source .env/bin/activate
python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
- name: Run all tests on GPU
env:
TF_FORCE_GPU_ALLOW_GROWTH: "true"
OMP_NUM_THREADS: 1
RUN_SLOW: yes
run: |
source .env/bin/activate
python -m pytest -n 1 --dist=loadfile -s ./tests/ --durations=0
- name: Run examples tests on GPU
env:
TF_FORCE_GPU_ALLOW_GROWTH: "true"
OMP_NUM_THREADS: 1
RUN_SLOW: yes
run: |
source .env/bin/activate
pip install -r examples/requirements.txt
python -m pytest -n 1 --dist=loadfile -s examples --durations=0