Modern Data Platform Excellence Series

PyTorch Model Training Performance Tuning: A Comprehensive Guide

Top tips to boost your training speed by 5-10x with lower cost, including code snippets and real-world use cases

You may think PyTorch performance tuning is a complex and daunting topic. This eBook breaks it down into easily consumable tips and tricks with concrete examples.Discover the tuning tips that deliver optimal training speeds at lower costs. Reduce end-to-end latency by 5-10x, improve the accuracy of your model, and boost GPU utilization up to 90%.In this book, you will dive deep into the training aspect of the machine learning pipeline. You will learn a set of optimizations and best practices that can accelerate model training in PyTorch. Presented techniques can be implemented by changing only a few lines of code and can be applied to a wide range of deep learning models across all domains.

In this comprehensive guide, you'll learn:

  • How PyTorch runs under the hood
  • What can impact the performance of model training in the ML pipeline
  • The process of optimizing PyTorch model training step-by-step
  • 13 tuning tips including data loading, data operations, GPU processing, and CPU processing, with lines of code
  • Real-world use cases using Alluxio as the data access layer for speed and efficiency

If you are managing analytics/SQL workloads, check out the optimization handbooks of Trino and Presto in the eBook series.

Case Study Blog

Building High-performance Data Access Layer

On-Demand Video
On-Demand Video

Maximize GPU Ultilization for Model Training

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Event Recap Blog

Data Caching Strategies for Data Analytics and AI