test_openvino_examples.yml

name: OpenVINO - Examples Test

on:
  workflow_dispatch:
  schedule:
    - cron:  '14 3 * * 1'  # run weekly: every Monday at 3:14
  push:
    paths:
    - '.github/workflows/test_openvino_examples.yml'
    - 'examples/openvino/*'
  pull_request:
    paths:
    - '.github/workflows/test_openvino_examples.yml'
    - 'examples/openvino/*'


concurrency:
  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
  cancel-in-progress: true

jobs:
  build:
    strategy:
      fail-fast: false
      matrix:
        python-version: ["3.8", "3.10"]

    runs-on: ubuntu-20.04

    steps:
    - uses: actions/checkout@v2
    - name: Setup Python ${{ matrix.python-version }}
      uses: actions/setup-python@v2
      with:
        python-version: ${{ matrix.python-version }}

    - name: Install dependencies
      run: |
        # Install PyTorch CPU to prevent unnecessary downloading/installing of CUDA packages
        # ffmpeg, torchaudio and pillow are required for image classification and audio classification pipelines
        #sudo apt-get install ffmpeg
        #pip install torch torchaudio --extra-index-url https://download.pytorch.org/whl/cpu
        pip install optimum[openvino] nncf
        pip install datasets==2.4.0
        #pip install -r examples/openvino/audio-classification/requirements.txt
        pip install -r examples/openvino/image-classification/requirements.txt
        pip install -r examples/openvino/question-answering/requirements.txt
        #pip install -r examples/openvino/stable-diffusion/requirements.txt
        pip install -r examples/openvino/text-classification/requirements.txt

    - run: free -h
    - run: lscpu
    - run: pip freeze

    - name: Test examples
      run: |
        #python examples/openvino/audio-classification/run_audio_classification.py     --model_name_or_path facebook/wav2vec2-base     --nncf_compression_config examples/openvino/audio-classification/configs/wav2vec2-base-qat.json     --dataset_name superb     --dataset_config_name ks     --max_train_samples 50   --max_eval_samples 10  --output_dir /tmp/qat-wav2vec2-base-ft-keyword-spotting     --overwrite_output_dir     --remove_unused_columns False     --do_train     --do_eval     --learning_rate 3e-5     --max_length_seconds 1     --attention_mask False     --warmup_ratio 0.1     --num_train_epochs 5     --gradient_accumulation_steps 4     --dataloader_num_workers 4     --logging_strategy steps     --logging_steps 10     --evaluation_strategy epoch     --save_strategy epoch     --load_best_model_at_end True     --metric_for_best_model accuracy     --save_total_limit 3     --seed 42
        python examples/openvino/image-classification/run_image_classification.py     --model_name_or_path nateraw/vit-base-beans     --dataset_name beans     --max_train_samples 50   --max_eval_samples 10  --remove_unused_columns False     --do_train     --do_eval     --learning_rate 2e-5     --num_train_epochs 1     --logging_strategy steps     --logging_steps 10     --evaluation_strategy epoch     --save_strategy epoch     --save_total_limit 3     --seed 1337     --output_dir /tmp/beans_outputs/
        python examples/openvino/question-answering/run_qa.py   --model_name_or_path distilbert-base-uncased-distilled-squad   --dataset_name squad   --do_train   --do_eval     --max_train_samples 50   --learning_rate 3e-5   --num_train_epochs 1   --max_seq_length 384   --doc_stride 128   --output_dir /tmp/outputs_squad/   --overwrite_output_dir 
        #python examples/openvino/stable-diffusion/train_text_to_image_qat.py   --ema_device="cpu"  --use_kd  --model_id="svjack/Stable-Diffusion-Pokemon-en" --max_train_samples 20  --center_crop   --random_flip   --dataloader_num_workers=2  --dataset_name="lambdalabs/pokemon-blip-captions"   --max_train_steps=1   --output_dir=sd-quantized-pokemon
        TASK_NAME=sst2   &&  python examples/openvino/text-classification/run_glue.py     --model_name_or_path bert-base-uncased     --task_name $TASK_NAME    --max_train_samples 50   --max_eval_samples 10   --output_dir /tmp/qat-bert-base-ft-$TASK_NAME     --overwrite_output_dir     --do_train     --do_eval     --max_seq_length 128     --learning_rate 1e-5     --optim adamw_torch     --num_train_epochs 3     --logging_steps 10     --evaluation_strategy steps     --eval_steps 20     --save_strategy epoch     --seed 42