Training Models at Facebook Scale with PyTorch
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.
Dwarak Rajagopal is a Senior Engineering Manager and Technical Lead in AI Infrastructure at Facebook. He currently leads the core development of PyTorch 1.0, an open source deep learning platform and the center of Facebook's effort to scale Research to Production in deep learning. Prior to Facebook, as the head of Core Platforms in Uber ATG, he led the Onboard Infra, ML and Data Platforms for the self driving software stack and built out the engineering team in SF.