Nvidia - Fundamentals of Deep Learning for Multi-GPUs

Duration

Duration:

Just 2 Days

Method

Method:

Classroom / Online / Hybrid

Next date

Next date:

7/11/2022 (Monday)

Overview

On this accelerated Nvidia Fundamentals of Deep Learning for Multi-GPUs course, you’ll learn the techniques for training deep neural networks on multi-GPU technology to shorten the training time required for data-intensive applications.

In just 2 days, you’ll work with deep learning tools, frameworks, and workflows to perform neural network training, and learn concepts for implementing Horovod multi-GPUs to reduce the complexity of writing efficient distributed software and to maintain accuracy when training a model across many GPUs. You’ll also build knowledge on:

  • Stochastic gradient descent (SGD), a crucial tool in parallelised training
  • Batch size and its effect on training time and accuracy
  • Transforming a single-GPU implementation to a Horovod multi-GPU implementation

At the end of this accelerated course, you'll earn a NIVIDIA DLI certificate highlighting your competency in the elements learned during the course. Firebrand’s Lecture | Lab | Review methodology will allow you to train faster than traditional training in a distraction-free environment, covered by a Certification Guarantee.

Four reasons why you should sit your course with Firebrand Training

  1. You'll be trained and certified faster. Learn more on this 2-day accelerated course. You'll get at least 12 hours a day of quality learning time in a distraction-free environment
  2. Your course is all-inclusive. One simple price covers all course materials, exams, accommodation and meals – so you can focus on learning
  3. Pass first time or train again for free. Your expert instructor will deliver our unique accelerated learning methods, allowing you to learn faster and be in the best possible position to pass first time. In the unlikely event that you don't, it's covered by your Certification Guarantee
  4. Study with an award-winning training provider. We've won the Learning and Performance Institute's "Training Company of the Year" three times. Firebrand is your fastest way to learn, with 125.505 students saving more than one million hours since 2001

Curriculum

Section 1: Stochastic Gradient Descent and the Effects of Batch Size

  • Understand the issues with sequential single-thread data processing and the theory behind speeding up applications with parallel processing
  • Explore loss function, gradient descent, and SGD
  • Learn the effect of batch size on accuracy and training time

Section 2: Training on Multiple GPUs with Horovod

  • Discover the benefits of training on multiple GPUs with Horovod
  • Learn to transform single-GPU training on the Fashion-MNIST dataset to Horovod multi-GPU implementation

Section 3: Maintaining Model Accuracy when Scaling to Multiple GPUs

  • Understand why accuracy can decrease when parallelising training on multiple GPUs
  • Explore tools for maintaining accuracy when scaling training to multiple GPUs

Exam Track

At the end of your accelerated course, you’ll receive the following certificate:

NVIDIA DLI Certificate

  • Format: Code-based
  • Languages: English, Korean, Simplified Chinese, Traditional Chinese

What's Included

Your accelerated course includes:

  • Accommodation *
  • Meals, unlimited snacks, beverages, tea and coffee *
  • On-site exams **
  • Exam vouchers **
  • Practice tests **
  • Certification Guarantee ***
  • Courseware
  • Up-to 12 hours of instructor-led training each day
  • 24-hour lab access
  • Digital courseware **
  • * For residential training only. Doesn't apply for online courses
  • ** Some exceptions apply. Please refer to the Exam Track or speak with our experts
  • *** Pass first time or train again free (just pay for accommodation, exams and incidental costs)

Prerequisites

Before attending this accelerated course, you should have experience with gradient descent model training.

Are you ready to get certified in record time?

We interview all applicants for the course on their technical background, degrees and certifications held, and general suitability. If you get through this screening process, it means you stand a great chance of passing.

Firebrand Training is an immersive training environment. You must be committed to the course. The above prerequisites are guidelines, but many students with less experience have other background or traits that have enabled their success in accelerated training through Firebrand Training.

If you have any doubts as to whether you meet the pre-requisites please call 78 79 16 53 and speak to one of our enrolment consultants, who can help you with a training plan.

Reviews

We've currently trained 125.505 students in 12 years. We asked them all to review our Accelerated Learning. Currently,
96,57% have said Firebrand exceeded their expectations:

"This was my first time using Firebrand to attend an online instructor-led course and the entire experience has been extremely positive. Would certainly recommend Firebrand for future training!"
Anonymous (22/8/2022 (Monday) to 25/8/2022 (Thursday))

"I was not sure how this course was going to help me considering I had read three different books but the course content is pitched at the right level to ensure that you have enough to pass the course. The instructor was on hand for most of the day and evening and always happy to help with any questions. We took our course in a hotel who ensured that we had everything we needed including all the food we could eat. Firebrand is an excellent company and I would have no hesitation coming back on future courses and in recommending them to others."
Dawn Horrocks. (5/9/2022 (Monday) to 10/9/2022 (Saturday))

"This was well run course which provided a good overview of the course material."
Anonymous. (6/9/2022 (Tuesday) to 9/9/2022 (Friday))

"Excellent tutor, first rate knowledge and expertise. Brought the course to life."
Marcus Edwards, Butterfield Group. (6/9/2022 (Tuesday) to 9/9/2022 (Friday))

"Good training, friendly staff"
Anonymous. (5/9/2022 (Monday) to 11/9/2022 (Sunday))

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