Data Parallelism: How to Train Deep Learning Models on Multiple GPUs Training
Course Authors
Data Parallelism: How to Train Deep Learning Models on Multiple GPUs Training Hands-on (Online, Onsite, and Classroom Live)
| Commitment | 1 Day, 7-8 hours a day. |
| Language | English |
| User Ratings | Average User Rating 4.8 See what learners said |
| Price | REQUEST |
| Delivery Options | Instructor-Led Onsite, Online, and Classroom Live |
COURSE OVERVIEW
This Data Parallelism: How to Train Deep Learning Models on Multiple GPUs Training workshop teaches you techniques for data-parallel deep learning on multiple GPUs to shorten the training time required for data-intensive applications. Working with deep learning tools, frameworks, and workflows to perform neural network training, you’ll learn how to decrease model training time by distributing data to multiple GPUs while retaining training accuracy on a single GPU.
Please note that once a booking has been confirmed, it is non-refundable. This means that after you have confirmed your seat for an event, it cannot be canceled, and no refund will be issued, regardless of attendance.
WHAT'S INCLUDED?
- 1 day of Data Parallelism: How to Train Deep Learning Models on Multiple GPUs Training with an expert instructor
- Data Parallelism: How to Train Deep Learning Models on Multiple GPUs Electronic Course Guide
- Certificate of Completion
- 100% Satisfaction Guarantee
RESOURCES
- Data Parallelism: How to Train Deep Learning Models on Multiple GPUs – https://www.wiley.com/
- Data Parallelism: How to Train Deep Learning Models on Multiple GPUs – https://www.packtpub.com/
- Data Parallelism: How to Train Deep Learning Models on Multiple GPUs – https://store.logicaloperations.com/
- Data Parallelism: How to Train Deep Learning Models on Multiple GPUs – https://us.artechhouse.com/
- Data Parallelism: How to Train Deep Learning Models on Multiple GPUs Training – https://www.amazon.com/
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ADDITIONAL INFORMATION
COURSE OBJECTIVES
Upon completion of this Data Parallelism: How to Train Deep Learning Models on Multiple GPUs Training course, participants can:
- Understand how data parallel deep learning training is performed using multiple GPUs
- Achieve maximum throughput when training, for the best use of multiple GPUs
- Distribute training to multiple GPUs using Pytorch Distributed Data Parallel
- Understand and utilize algorithmic considerations specific to multi-GPU training performance and accuracy
CUSTOMIZE IT
- We can adapt this Data Parallelism: How to Train Deep Learning Models on Multiple GPUs Training course to your group’s background and work requirements at little to no added cost.
- If you are familiar with some aspects of this Data Parallelism: How to Train Deep Learning Models on Multiple GPUs course, we can omit or shorten their discussion.
- We can adjust the emphasis placed on the various topics or build the Data Parallelism: How to Train Deep Learning Models on Multiple GPUs course around the mix of technologies of interest to you (including technologies other than those in this outline).
- If your background is nontechnical, we can exclude the more technical topics, include the topics that may be of special interest to you (e.g., as a manager or policymaker), and present the Data Parallelism: How to Train Deep Learning Models on Multiple GPUs course in a manner understandable to lay audiences.
AUDIENCE/TARGET GROUP
The target audience for this Data Parallelism: How to Train Deep Learning Models on Multiple GPUs course:
- ALL
CLASS PREREQUISITES
The knowledge and skills that a learner must have before attending this Data Parallelism: How to Train Deep Learning Models on Multiple GPUs course are:
- Experience with deep learning training using Python
COURSE SYLLABUS
Introduction
- Meet the instructor.
- Create an account at courses.nvidia.com/join
Stochastic Gradient Descent and the Effects of Batch Size
- Learn the significance of stochastic gradient descent when training on multiple GPUs
- Understand the issues with sequential single-thread data processing and the theory behind speeding up applications with parallel processing.
- Understand loss function, gradient descent, and stochastic gradient descent (SGD).
- Understand the effect of batch size on accuracy and training time with an eye towards its use on multi-GPU systems.
Training on Multiple GPUs with PyTorch Distributed Data Parallel (DDP)
- Learn to convert single GPU training to multiple GPUs using PyTorch Distributed Data Parallel
- Understand how DDP coordinates training among multiple GPUs.
- Refactor single-GPU training programs to run on multiple GPUs with DDP.
Maintaining Model Accuracy when Scaling to Multiple GPUs
- Understand and apply key algorithmic considerations to retain accuracy when training on multiple GPUs
- Understand what might cause accuracy to decrease when parallelizing training on multiple GPUs.
- Learn and understand techniques for maintaining accuracy when scaling training to multiple GPUs.
Workshop Assessment
- Use what you have learned during the workshop: complete the workshop assessment to earn a certificate of competency
Final Review
- Review key learnings and wrap up questions.
- Take the workshop survey.
