Boosting PyTorch Inference on CPU: From Post-Training Quantization to Multithreading
<p>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.</p>
<p>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.</p>
<h1>Problem Statement: Deep Learning Inference under Limited Time and Computation Constraints</h1>
<p>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.</p>
<p><a href="https://medium.com/towards-data-science/boosting-pytorch-inference-on-cpu-from-post-training-quantization-to-multithreading-6820ac7349bb"><strong>Read More</strong></a></p>