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- name : OpenVINO - Notebooks and Examples Test
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+ name : OpenVINO - Examples Test
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on :
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workflow_dispatch :
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schedule :
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- cron : ' 14 3 * * 1' # run weekly: every Monday at 3:14
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push :
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paths :
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- - ' .github/workflows/test_openvino_notebooks.yml'
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- ' .github/workflows/test_openvino_examples.yml'
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- - ' notebooks/openvino/*'
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- ' examples/openvino/*'
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pull_request :
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paths :
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- - ' .github/workflows/test_openvino_notebooks.yml'
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- ' .github/workflows/test_openvino_examples.yml'
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- - ' notebooks/openvino/*'
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- ' examples/openvino/*'
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run : |
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# Install PyTorch CPU to prevent unnecessary downloading/installing of CUDA packages
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# ffmpeg, torchaudio and pillow are required for image classification and audio classification pipelines
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- sudo apt-get install ffmpeg
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- # pip install torch torchaudio --extra-index-url https://download.pytorch.org/whl/cpu
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- pip install ".[tests, openvino]" nbval
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- pip install optimum[openvino] nncf torchaudio datasets==2.4.0
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- pip install -r notebooks/openvino/requirements.txt
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+ pip install optimum[openvino] nncf
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+ pip install -r examples/openvino/audio-classification/requirements.txt
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pip install -r examples/openvino/image-classification/requirements.txt
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pip install -r examples/openvino/question-answering/requirements.txt
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pip install -r examples/openvino/text-classification/requirements.txt
@@ -55,13 +48,10 @@ jobs:
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- run : lscpu
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- run : pip freeze
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- - name : Test with Pytest
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- run : |
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- python -m pytest --nbval-lax notebooks/openvino/optimum_openvino_inference.ipynb notebooks/openvino/question_answering_quantization.ipynb
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-
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- name : Test examples
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run : |
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- # 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 10 --max_eval_samples 2 --output_dir /tmp/qat-wav2vec2-base-ft-keyword-spotting --overwrite_output_dir --remove_unused_columns False --do_train --learning_rate 3e-5 --max_length_seconds 1 --attention_mask False --warmup_ratio 0.1 --num_train_epochs 1 --gradient_accumulation_steps 1 --dataloader_num_workers 1 --logging_strategy steps --logging_steps 1 --evaluation_strategy epoch --save_strategy epoch --load_best_model_at_end False --seed 42
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+ 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 10 --max_eval_samples 2 --output_dir /tmp/qat-wav2vec2-base-ft-keyword-spotting --overwrite_output_dir --remove_unused_columns False --do_train --learning_rate 3e-5 --max_length_seconds 1 --attention_mask False --warmup_ratio 0.1 --num_train_epochs 1 --gradient_accumulation_steps 1 --dataloader_num_workers 1 --logging_strategy steps --logging_steps 1 --evaluation_strategy epoch --save_strategy epoch --load_best_model_at_end False --seed 42
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TASK_NAME=sst2 && python examples/openvino/text-classification/run_glue.py --model_name_or_path sshleifer/tiny-distilbert-base-cased-distilled-squad --task_name $TASK_NAME --max_train_samples 10 --max_eval_samples 2 --output_dir /tmp/qat-bert-base-ft-$TASK_NAME --overwrite_output_dir --do_train --do_eval --max_seq_length 128 --learning_rate 1e-5 --optim adamw_torch --num_train_epochs 1 --logging_steps 10 --evaluation_strategy steps --eval_steps 5 --save_strategy epoch --seed 42
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python examples/openvino/question-answering/run_qa.py --model_name_or_path sshleifer/tiny-distilbert-base-cased-distilled-squad --dataset_name squad --do_train --do_eval --max_train_samples 10 --max_eval_samples 2 --learning_rate 3e-5 --num_train_epochs 1 --max_seq_length 384 --doc_stride 128 --output_dir /tmp/outputs_squad/ --overwrite_output_dir
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python examples/openvino/image-classification/run_image_classification.py --model_name_or_path nateraw/vit-base-beans --dataset_name beans --max_train_samples 10 --max_eval_samples 2 --remove_unused_columns False --do_train --learning_rate 2e-5 --num_train_epochs 1 --logging_strategy steps --logging_steps 1 --evaluation_strategy epoch --save_strategy epoch --save_total_limit 1 --seed 1337 --output_dir /tmp/beans_outputs/
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+ pip install -r examples/openvino/stable-diffusion/requirements.txt && 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 10 --center_crop --random_flip --dataloader_num_workers=2 --dataset_name="lambdalabs/pokemon-blip-captions" --max_train_steps=1 --output_dir=sd-quantized-pokemon
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