Boosting PyTorch Inference on CPU: From Post-Training Quantization to Multithreading

Welcome to another edition of “<a href="https://towardsdatascience.com/the-kaggle-blueprints-unlocking-winning-approaches-to-data-science-competitions-24d7416ef5fd" rel="noopener" target="_blank">The Kaggle Blueprints</a>”, where we will analyze <a href="https://www.kaggle.com/" rel="noopener ugc nofollow" target="_blank">Kaggle</a> competitions’ winning solutions for lessons we can apply to our own data science projects. This edition will review the techniques and approaches from the <a href="https://www.kaggle.com/competitions/birdclef-2023/" rel="noopener ugc nofollow" target="_blank">“BirdCLEF 2023</a>” competition, which ended in May 2023. <h1>Problem Statement: Deep Learning Inference under Limited Time and Computation Constraints</h1> The BirdCLEF competitions are a series of annually recurring competitions on Kaggle. The main objective of a BirdCLEF competition is usually to identify a specific bird species by sound. The competitors are given short audio files of single bird calls and then must predict whether a specific bird was present in a longer recording. In an earlier edition of The Kaggle Blueprints, we have already reviewed the winning approaches to audio classification with Deep Learning from last year’s “BirdCLEF 2022” competition. One aspect that was novel in the “BirdCLEF 2023” competition was the limited time and computational constraints: Competitors were asked to predict roughly 200 10-minute-long recordings on a CPU Notebook within 2 hours. <a href="https://towardsdatascience.com/boosting-pytorch-inference-on-cpu-from-post-training-quantization-to-multithreading-6820ac7349bb">Click Here</a>