Train Large Language Models Faster - Parallelism Deep Dive

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Paid Certificate Available
Duration & workload

13 hours 44 minutes
Sessions

On-Demand
Subtitles

English

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Overview

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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.

Syllabus

Introduction

In this module, we will introduce the course, explain the key objectives, and provide a roadmap of how parallelism techniques will accelerate large language model training. You will gain an overview of what to expect and get familiar with the course structure.

Strategies for Parallelizing LLMS - Deep Dive

In this module, we will explore the different parallelism strategies for LLM training, including single GPU vs. parallel strategies. You'll understand how parallelism improves efficiency and learn its key advantages in real-world applications.

IT Fundamental Concepts

In this module, we will establish a foundational understanding of IT concepts crucial for training LLMs. Topics like cloud computing, storage solutions, and computer architecture will provide the context for optimizing LLM workflows.

GPU Architecture for LLM Training Deep Dive

In this module, we will explore GPU architecture and its role in LLM training. You'll learn how GPUs are designed to handle the massive computations required by large models, ensuring faster and more efficient training.

Deep and Machine Learning - Deep Dive

In this module, we will cover the fundamentals of machine learning and deep learning. We’ll explore neural networks, training processes, and key differences between ML and DL to lay the groundwork for LLM training.

Large Language Models - Fundamentals of AI and LLMs

In this module, we will dive into the fundamentals of LLMs, starting with the Transformer architecture. You'll learn about key components such as self-attention and how the Transformer library powers modern AI applications.

Parallel Computing Fundamentals & Parallelism in LLM Training

In this module, we will introduce parallel computing concepts and their relevance to LLM training. You’ll gain a deeper understanding of how parallelism reduces bottlenecks and accelerates model development.

Types of Parallelism in LLM Training - Data, Model, and Hybrid Parallelism

In this module, we will explore data, model, and hybrid parallelism in detail. You’ll learn how each strategy optimizes training workflows and where to apply them for maximum efficiency in LLM training.

Types of Parallelism - Pipeline and Tensor Parallelism

In this module, we will delve into pipeline and tensor parallelism, explaining their key concepts and how they work together to enhance training efficiency. You’ll also explore real-world strategies for implementing these techniques.

Tensor Parallelism - Deep Dive

In this module, we will dive deep into tensor parallelism, focusing on partitioning strategies, communication patterns, and device synchronization. You'll gain a clear understanding of how this technique accelerates LLM training.

HANDS-ON: Strategies for Parallelism - Data Parallelism Deep Dive

In this module, we will shift to hands-on learning, applying data parallelism techniques in PyTorch. You'll train a small model on the MNIST dataset, testing different parallelism strategies and observing their effects on performance.

HANDS-ON: Data Parallelism w/ WikiText Dataset & DeepSpeed Mem. Optimization

In this module, we will apply data parallelism to the WikiText-2 dataset and use DeepSpeed to optimize memory usage. You'll gain hands-on experience with advanced techniques to improve LLM training efficiency.

Running TRUE Parallelism on Multiple GPU Systems - Runpod.io

In this module, we will guide you through setting up Runpod.io for multi-GPU parallelism. You’ll gain practical experience running parallelism experiments on a distributed environment and working with large-scale models.

Fault Tolerance and Scalability & Advanced Checkpointing Strategies - Deep Dive

In this module, we will dive into fault tolerance and checkpointing strategies. You'll learn how to ensure scalable, resilient LLM training workflows that can recover from failures and continue without interruptions.

Advanced Topics and Emerging Trends

In this module, we will explore cutting-edge advancements in parallel computing and LLM training. You'll gain insight into the latest trends and technologies that are revolutionizing AI and the future of machine learning.

Wrap up and Next Steps

In this module, we will wrap up the course by summarizing everything you've learned about parallelism and LLM training. You'll also receive guidance on how to proceed with your AI journey and apply these skills in future projects.