PyTorch 2.1 - New Developments

Pytorch is a deep learning framework used to build AI models that accelerates the path from research prototyping to production deployment. The most recent version of PyTorch, 2.1, boasts some new features around compile, distributed, inference, export and edge. PyTorch also supports inference optimization techniques like memory efficient attention, quantization and pruning which are expected to make the popular generative AI models run efficiently (use less memory and run faster) during inference. By benchmarking popular generative AI models using the latest techniques in PyTorch, we see upto ~8.5x speedup for segment anything and ~5.6x for llama2. In this session we will deep dive into all the new developments and techniques in PyTorch and provide recommendations on how you can accelerate your models using native PyTorch code.