Abstract: Large scale distributed training has become an essential element to scaling the productivity for ML engineers. Today, ML models are getting larger and more complex in terms of compute and memory requirements. The amount of data we train on at Facebook is huge. In this talk, we will learn about the Distributed Training Platform to support large scale data and model parallelism. We will touch base on Distributed Training support for PyTorch and how we are offering a flexible training platform for ML engineers to increase their productivity at facebook scale.
Bio: Kiuk Chung is a Software Engineer at Facebook leading PyTorch Elastic Training. Prior to Facebook, he spent six years in various teams within Amazon, building a cloud-native infrastructure for deep learning and high-performance computing. More specifically, he scaled deep learning for product recommendations and search and worked on releasing AWS Batch.
