Data Parallelism: How to Train Deep Learning Models on Multiple GPUs
This workshop teaches you techniques for data-parallel deep learning training 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 the accuracy of training on a single GPU.
- 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
Experienced Python Developers
Experience with deep learning training using Python
Course overview and learning objectives
Understanding SGD and how batch size impacts training
Implementing Distributed Data Parallel for scalability
Techniques to preserve model accuracy when training on multiple GPUs
Hands-on evaluation of concepts learned
Recap of key takeaways and closing discussion