This course teaches you techniques to dramatically speed up model training using the latest features in PyTorch 2.X. Mastering these optimization strategies is essential for professionals building scalable, high-performance AI systems.
Accelerate Model Training with PyTorch 2.X
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Beginner level
Recommended experience
8 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Optimize model training using PyTorch and performance tuning techniques.
Leverage specialized libraries to enhance CPU-based training.
Build efficient data pipelines to improve GPU utilization.
Skills you'll gain
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Recently updated!
January 2026
Assessments
11 assignments
Taught in English
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There are 11 modules in this course
In this section, we explore the training process of neural networks, analyze factors contributing to computational burden, and evaluate elements influencing training time.
What's included
2 videos3 readings1 assignment
In this section, we explore techniques to accelerate model training by modifying the software stack and scaling resources. Key concepts include vertical and horizontal scaling, application and environment layer optimizations, and practical strategies for improving efficiency.
What's included
1 video3 readings1 assignment
In this section, we explore the PyTorch 2.0 Compile API to accelerate deep learning model training, focusing on graph mode benefits, API usage, and workflow components for performance optimization.
What's included
1 video3 readings1 assignment
In this section, we explore using OpenMP for multithreading and IPEX to optimize PyTorch on Intel CPUs, enhancing performance through specialized libraries.
What's included
1 video3 readings1 assignment
In this section, we explore building efficient data pipelines to prevent training bottlenecks. Key concepts include configuring workers, optimizing GPU memory transfer, and ensuring continuous data flow for ML model training.
What's included
1 video2 readings1 assignment
In this section, we explore model simplification through pruning and compression techniques to improve efficiency without sacrificing performance, using the Microsoft NNI toolkit for practical implementation.
What's included
1 video3 readings1 assignment
In this section, we explore mixed precision strategies to optimize model training efficiency by reducing computational and memory demands without sacrificing accuracy, focusing on PyTorch implementation and hardware utilization.
What's included
1 video3 readings1 assignment
In this section, we explore distributed training principles, parallel strategies, and PyTorch implementation to enhance model training efficiency through resource distribution.
What's included
1 video4 readings1 assignment
In this section, we explore distributed training on multiple CPUs, focusing on benefits, implementation, and using Intel oneCCL for efficient communication in resource-constrained environments.
What's included
1 video3 readings1 assignment
In this section, we explore multi-GPU training strategies, analyze interconnection topologies, and configure NCCL for efficient distributed deep learning operations.
What's included
1 video4 readings1 assignment
In this section, we explore distributed training on computing clusters, focusing on Open MPI and NCCL for efficient communication and resource management across multiple machines.
What's included
1 video4 readings1 assignment
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Frequently asked questions
Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.
If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.
Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.
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