Optimizing machine learning models can often feel like trying to navigate a dense forest without a map. Fortunately, with the right guidelines, you can traverse through the complexities of model optimization. In this article, we’ll walk you through the setup and application of the OpenVINO NNCF with the vuiseng9/bert-base-squadv1-pruneofa-90pc-bt model.
Step-by-Step Guide to Model Optimization
The process involves several critical steps, powered by OpenVINO’s Neural Network Compression Framework (NNCF) to achieve remarkable performance and efficiency.
- Clone the OpenVINO NNCF Repository:
git clone https://github.com/vuiseng9/nncf cd nncf git checkout tld-poc git reset --hard 5647610d5ee2bf9f1324604e6579bca1c391e260 python setup.py develop pip install -r examples/torch/requirements.txt - Clone the Hugging Face NNCF Pruning Repository:
git clone https://github.com/vuiseng9/nn_pruning cd nn_pruning git checkout reproduce-evaluation git reset --hard 2d4e196d694c465e43e5fbce6c3836d0a60e1446 pip install -e .[dev] - Clone the Hugging Face Transformers Repository:
git clone https://github.com/vuiseng9/transformers cd transformers git checkout tld-poc git reset --hard 5dd7402e9a316041dea4ff67508c01047323616e pip install -e . - Install Additional Dependencies:
pip install onnx - Train the Model:
wget https://huggingface.co/vuiseng9/bert-base-squadv1-pruneofa-90pc-bt-qat-ltraw/main/nncf_bert_squad_sparsity.json NNCF_CFG=path/to/downloaded_nncf_cfg_above # to revise OUTROOT=path/to/train_output_root # to revise WORKDIR=transformers/examples/pytorch/question-answering # to revise RUNID=bert-base-squadv1-pruneofa-90pc-bt-qat-lt cd $WORKDIR OUTDIR=$OUTROOT/$RUNID mkdir -p $OUTDIR export CUDA_VISIBLE_DEVICES=0 NEPOCH=5 python run_qa.py --model_name_or_path vuiseng9/bert-base-squadv1-pruneofa-90pc-bt --pruneofa_qat --dataset_name squad --do_eval --do_train --evaluation_strategy steps --eval_steps 250 --learning_rate 3e-5 --lr_scheduler_type cosine_with_restarts --warmup_ratio 0.25 --cosine_cycles 1 --teacher bert-large-uncased-whole-word-masking-finetuned-squad --teacher_ratio 0.9 --num_train_epochs $NEPOCH --per_device_eval_batch_size 128 --per_device_train_batch_size 16 --max_seq_length 384 --doc_stride 128 --save_steps 250 --nncf_config $NNCF_CFG --logging_steps 1 --overwrite_output_dir --run_name $RUNID --output_dir $OUTDIR
Understanding the Code: An Analogy
Think of model optimization like preparing a dish in a bustling kitchen. Each step corresponds to an essential process that ensures the meal is not only delicious but also visually appealing.
- The cloning of repositories is like gathering your ingredients.
- Setting up dependencies is akin to preheating your oven – critical for getting things started.
- The training process represents cooking – you mix and heat your ingredients (data), following the recipe (code) to achieve a harmonious dish (optimized model).
- Finally, evaluation mirrors plating and presenting the dish — you assess its taste (performance) and presentation (results).
Troubleshooting Tips
If you encounter issues during the optimization process, consider these troubleshooting ideas:
- Double-check the paths of dependencies; incorrect paths can lead to errors.
- Ensure that your CUDA environment is properly configured for GPU acceleration, as this will significantly speed up training.
- Check the compatibility of the cloned repositories with the current version of libraries like PyTorch and ONNX.
- If strange behaviors occur during training, it may be beneficial to reduce learning rates or adjust the batch sizes.
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Final Thoughts
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.
