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Accelerate Model Training with PyTorch 2.X

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Accelerate Model Training with PyTorch 2.X

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Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

8 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
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.

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Recently updated!

January 2026

Assessments

11 assignments

Taught in English

There are 11 modules in this course

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.

You’ll learn how to refine your training workflow, improve computation efficiency, and achieve faster, more reliable model iterations. Each module translates performance concepts into practical techniques you can immediately apply. The course blends deep technical foundations with real-world optimization workflows, ensuring you understand both why each method works and how to execute it effectively. You’ll practice using compiled models, mixed precision, distributed strategies, and more. This course is ideal for developers, data scientists, and ML engineers with basic PyTorch experience who want to train models faster and scale training across hardware configurations.

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

2 videos•Total 2 minutes
  • Course Overview•1 minute
  • Deconstructing the Training Process - Overview Video•1 minute
3 readings•Total 50 minutes
  • Introduction•10 minutes
  • Loss Calculation•10 minutes
  • Operations•30 minutes
1 assignment•Total 10 minutes
  • Exploring Neural Network Training Fundamentals•10 minutes

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

1 video•Total 1 minute
  • Training Models Faster - Overview Video•1 minute
3 readings•Total 30 minutes
  • Introduction•10 minutes
  • Increasing Computing Resources•10 minutes
  • What if We Change the Batch Size?•10 minutes
1 assignment•Total 10 minutes
  • Optimizing Model Training Efficiency•10 minutes

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

1 video•Total 1 minute
  • Compiling the Model - Overview Video•1 minute
3 readings•Total 30 minutes
  • Introduction•10 minutes
  • Using the Compile API•10 minutes
  • Give Me a Real Fight Training a Heavier Model•10 minutes
1 assignment•Total 10 minutes
  • Execution Modes and Model Compilation in PyTorch•10 minutes

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

1 video•Total 1 minute
  • Using Specialized Libraries - Overview Video•1 minute
3 readings•Total 30 minutes
  • Introduction•10 minutes
  • Using and Configuring OpenMP•10 minutes
  • Using and Configuring Intel OpenMP•10 minutes
1 assignment•Total 10 minutes
  • Exploring Parallelism and Library Optimization•10 minutes

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

1 video•Total 1 minute
  • Building an Efficient Data Pipeline - Overview Video•1 minute
2 readings•Total 20 minutes
  • Introduction•10 minutes
  • Accelerating Data Loading•10 minutes
1 assignment•Total 10 minutes
  • Optimizing Data Pipeline Performance•10 minutes

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

1 video•Total 1 minute
  • Simplifying the Model - Overview Video•1 minute
3 readings•Total 30 minutes
  • Introduction•10 minutes
  • The Pruning Phase•10 minutes
  • Using Microsoft NNI to Simplify a Model•10 minutes
1 assignment•Total 10 minutes
  • Model Optimization Fundamentals•10 minutes

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

1 video•Total 1 minute
  • Adopting Mixed Precision - Overview Video•1 minute
3 readings•Total 30 minutes
  • Introduction•10 minutes
  • TF32•10 minutes
  • How About Tensor Cores•10 minutes
1 assignment•Total 10 minutes
  • Mixed Precision in Deep Learning•10 minutes

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

1 video•Total 1 minute
  • Distributed Training at a Glance - Overview Video•1 minute
4 readings•Total 40 minutes
  • Introduction•10 minutes
  • Learning the Fundamentals of Parallelism Strategies•10 minutes
  • Data Parallelism•10 minutes
  • Distributed Training on PyTorch•10 minutes
1 assignment•Total 10 minutes
  • Distributed Training Fundamentals•10 minutes

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

1 video•Total 1 minute
  • Training with Multiple CPUs - Overview Video•1 minute
3 readings•Total 30 minutes
  • Introduction•10 minutes
  • Implementing Distributed Training on Multiple CPUs•10 minutes
  • Launching Distributed Training on Multiple CPUs•10 minutes
1 assignment•Total 10 minutes
  • Multi-CPU Training in PyTorch•10 minutes

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

1 video•Total 1 minute
  • Training with Multiple GPUs - Overview Video•1 minute
4 readings•Total 40 minutes
  • Introduction•10 minutes
  • NVLink•10 minutes
  • Discovering the Interconnection Topology•10 minutes
  • Implementing Distributed Training on Multiple GPUs•10 minutes
1 assignment•Total 10 minutes
  • Multi-GPU Training Fundamentals•10 minutes

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

1 video•Total 1 minute
  • Training with Multiple Machines - Overview Video•1 minute
4 readings•Total 40 minutes
  • Introduction•10 minutes
  • Meeting the SLURM Workload Manager•10 minutes
  • Implementing Distributed Training on Multiple Machines•10 minutes
  • Coding the Distributed Training for Multiple Machines•10 minutes
1 assignment•Total 10 minutes
  • Distributed Computing Fundamentals•10 minutes

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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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