Ai+ Training

There is an increasing need to bring machine learning to a diverse set of hardware devices. Current approaches typically rely on vendor-specific operator libraries and frameworks and require significant engineering effort. In this talk we will present an overview of the Apache TVM open source stack, which exposes graph- and operator-level optimizations to provide performance portability for machine learning workloads across diverse hardware back-ends. TVM solves compiler optimization challenges by employing a learning-based approach for rapid exploration of optimizations, saving months of engineering time, and offering state-of-the-art performance in both edge and server use cases. We will discuss how TVM offers broad model coverage and makes effective use of hardware resources.  We will end the talk with a sneak peek at OctoML's Octomizer, a SaaS platform for continuous model optimization, benchmarking, and packaging.