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Leaner, Greener and Faster PyTorch Inference with Quantization

Offered By: MLOps World: Machine Learning in Production via YouTube

Tags

PyTorch Courses Machine Learning Courses Deep Learning Courses Neural Networks Courses Quantization Courses Inference Courses Model Optimization Courses Model Compression Courses

Course Description

Overview

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Discover the power of quantization in PyTorch for optimizing neural networks in this comprehensive conference talk. Learn how to transform FP32 parameters into integers without sacrificing accuracy, resulting in leaner, greener, and faster models. Explore the fundamentals of quantization, its implementation in PyTorch, and various approaches available. Gain insights into the benefits and potential pitfalls of each method, enabling informed decision-making for specific use cases. Follow along as the speaker demonstrates the application of quantization techniques on a large non-academic model, showcasing real-world effectiveness. Presented by Suraj Subramanian, a developer advocate and ML engineer at Meta AI, this talk offers valuable knowledge for enhancing PyTorch inference performance.

Syllabus

Leaner, Greener and Faster Pytorch Inference with Quantization


Taught by

MLOps World: Machine Learning in Production

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