Machine Learning Compilation: Episode 1 / Overview

An Introduction to Machine Learning Compilation (MLC). As the first course of its kind in the world for ML compilation, in this lecture CMU professor Tianqi Chen introduces why AI training and inference workloads need ML compilation to transform and optimize ML models from their development state in frameworks like PyTorch and TensorFlow to their deployment form on CPUs and GPUs. MLC helps solve the problem of combinatorial explosion of ML models and deployment hardware platforms.

This course is targeted not just for for undergraduate and graduate students but also people putting ML to use - data scientists, ML engineers and hardware providers. It covers ML programming abstractions, learning-driven search, compilation, and optimized library runtimes. These themes form a new field of ML systems – machine learning compilation.

In this course, we offer the first comprehensive treatment of its kind to study key elements of this emerging field systematically. We will learn the key abstractions to represent machine learning programs, automatic optimization techniques, and approaches to optimize dependency, memory, and performance in end-to-end machine learning deployment. By completing this course, you will learn how to apply the latest developments in ML compilation to build models that can be optimized for emerging hardware stacks. This let you deploy your models efficiently - minimizing memory usage, reducing inference latency and scaling to multiple heterogeneous hardware nodes.

The course requires a minimum set of prerequisites in data science and machine learning:
Python, familiarity with numpy
Some background in one deep learning framework (e.g. PyTorch, TensorFlow, JAX)
Experiences in system programming (e.g. C/CUDA) would be beneficial but not required

Episode 1 Slides: https://mlc.ai/summer22/slides/1-Intr...
Episode 1 Notes: https://mlc.ai/chapter_introduction/

Full course schedule: https://mlc.ai/summer22/schedule

Instructors:
Tianqi Chen with Hongyi Jin (TA), Siyuan Feng (TA) and Ruihang Lai (TA)