Train Large Language Models Faster - Parallelism Deep Dive

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This course focuses on accelerating the training of large language models (LLMs) through parallelism strategies. By exploring techniques such as data, model, and hybrid parallelism, you will learn how to optimize training processes for faster results. The course breaks down complex topics in a structured way, starting with an introduction to parallel computing and scaling laws, before diving into hands-on applications using popular libraries like PyTorch and DeepSpeed. You will also gain practical experience running parallelism strategies on multi-GPU systems and exploring fault tolerance techniques to ensure reliable training. The course integrates theoretical concepts with real-world examples to provide a comprehensive understanding of LLM training. Throughout the course, you will explore various types of parallelism—data, model, pipeline, and tensor parallelism—and their applications in LLMs. You’ll work with datasets like MNIST and WikiText, gaining hands-on experience implementing parallel strategies to optimize training speed. The course culminates in an exploration of advanced checkpointing strategies and fault tolerance methods, ensuring you understand how to recover from system failures during training. This course is perfect for learners interested in optimizing machine learning workflows and accelerating AI model development. A background in machine learning or deep learning is recommended, and the course is suitable for intermediate learners seeking to deepen their knowledge of LLM training strategies. By the end of the course, you will be able to implement and compare various parallelism techniques for LLM training, run distributed training on multi-GPU environments, apply fault tolerance strategies, and understand advanced topics in parallel computing.