Strategies For Parallelizing Llms Masterclass

Mastering LLM Parallelism: Scale Large Language Models with DeepSpeed & Multi-GPU Systems

What you'll learn

Understand and Apply Parallelism Strategies for LLMs

Implement Distributed Training with DeepSpeed

Deploy and Manage LLMs on Multi-GPU Systems

Enhance Fault Tolerance and Scalability in LLM Training

Requirements

Basic knowledge of Python programming and deep learning concepts.

Familiarity with PyTorch or similar frameworks is helpful but not required.

Access to a GPU-enabled environment (e.g., colab) for hands-on sections—don’t worry, we’ll guide you through setup!

Description

Mastering LLM Parallelism: Scale Large Language Models with DeepSpeed & Multi-GPU SystemsAre you ready to unlock the full potential of large language models (LLMs) and train them at scale? In this comprehensive course, you’ll dive deep into the world of parallelism strategies, learning how to efficiently train massive LLMs using cutting-edge techniques like data, model, pipeline, and tensor parallelism. Whether you’re a machine learning engineer, data scientist, or AI enthusiast, this course will equip you with the skills to harness multi-GPU systems and optimize LLM training with DeepSpeed.What You’ll LearnFoundational Knowledge: Start with the essentials of IT concepts, GPU architecture, deep learning, and LLMs (Sections 3-7). Understand the fundamentals of parallel computing and why parallelism is critical for training large-scale models (Section 8).Types of Parallelism: Explore the core parallelism strategies for LLMs—data, model, pipeline, and tensor parallelism (Sections 9-11). Learn the theory and practical applications of each method to scale your models effectively.Hands-On Implementation: Get hands-on with DeepSpeed, a leading framework for distributed training. Implement data parallelism on the WikiText dataset and master pipeline parallelism strategies (Sections 12-13). Deploy your models on RunPod, a multi-GPU cloud platform, and see parallelism in action (Section 14).Fault Tolerance & Scalability: Discover strategies to ensure fault tolerance and scalability in distributed LLM training, including advanced checkpointing techniques (Section 15).Advanced Topics & Trends: Stay ahead of the curve with emerging trends and advanced topics in LLM parallelism, preparing you for the future of AI (Section 16).Why Take This Course?Practical, Hands-On Focus: Build real-world skills by implementing parallelism strategies with DeepSpeed and deploying on Run Pod’s multi-GPU systems.Comprehensive Deep Dives: Each section includes in-depth explanations and practical examples, ensuring you understand both the "why" and the "how" of LLM parallelism.Scalable Solutions: Learn techniques to train LLMs efficiently, whether you’re working with a single GPU or a distributed cluster.Who This Course Is ForMachine learning engineers and data scientists looking to scale LLM training.AI researchers interested in distributed computing and parallelism strategies.Developers and engineers working with multi-GPU systems who want to optimize LLM performance.Anyone with a basic understanding of deep learning and Python who wants to master advanced LLM training techniques.PrerequisitesBasic knowledge of Python programming and deep learning concepts.Familiarity with PyTorch or similar frameworks is helpful but not required.Access to a GPU-enabled environment (e.g., run pod) for hands-on sections—don’t worry, we’ll guide you through setup!

Overview

Section 1: Introduction

Lecture 1 Introduction & What Is This Course About

Lecture 2 Course Structure

Lecture 3 DEMO - What You'll Build in This Course

Section 2: Course Source Code and Resources

Lecture 4 Get Source Code

Lecture 5 Get Course Slides

Section 3: Strategies for Parallelizing LLMS - Deep Dive

Lecture 6 What is Parallelism and Why it Matters

Lecture 7 Understanding the Single GPU Strategy

Lecture 8 Understanding the Parallel Strategy and Advantages

Lecture 9 Parallelism vs Single GPU - Summary

Section 4: IT Fundamental Concepts

Lecture 10 IT Fundamentals - Introduction

Lecture 11 What is a Computer - CPU and RAM Overview

Lecture 12 Data Storage and File Systems

Lecture 13 OS File System Structure

Lecture 14 LAN Introduction

Lecture 15 What is the Internet

Lecture 16 Internet Communication Deep Dive

Lecture 17 Understanding Servers and Clients

Lecture 18 GPUs - Overview

Section 5: GPU Architecture for LLM Training Deep Dive

Lecture 19 GPU Architecture for LLM Training

Lecture 20 Why this Architecture Excels

Section 6: Deep and Machine Learning - Deep Dive

Lecture 21 Machine and Deep Learning Introduction

Lecture 22 Deep and Machine Learning - Overview and Breakdown

Lecture 23 Deep Learning Key Aspects

Lecture 24 Deep Neural Networks - Deep Dive

Lecture 25 The Single Neuron Computation - Deep Dive

Lecture 26 Weights

Lecture 27 Activation Functions - Deep Dive

Lecture 28 Deep Learning - Summary

Lecture 29 Machine Learning Introduction - ML vs DL

Lecture 30 Learning Types and Full ML & DL Analogy Example

Lecture 31 DL and ML Comparative Capabilities - Summary

Section 7: Large Language Models - Fundamentals of AI and LLMs

Lecture 32 Introduction

Lecture 33 The Transformer Architecture Fundamentals

Lecture 34 The Self-Attention Mechanism - Analogy

Lecture 35 The Transformer Architecture Animation

Lecture 36 The Transformer Library - Deep dive

Section 8: Parallel Computing Fundamentals & Parallelism in LLM Training

Lecture 37 Parallel Computing Introduction - Key Concepts

Lecture 38 Parallel Computing Fundamentals and Scaling Laws - Deep Dive

Section 9: Types of Parallelism in LLM Training - Data - Model and Hybrid Parallelism

Lecture 39 Types of Parallelism in LLM Training

Lecture 40 Data Parallelism - How It Works

Lecture 41 Data Parallelism Advantages for LLM Training

Lecture 42 Real-world Example - Data Parallelism in GPT-3 Training

Lecture 43 Model Parallelism and Tensor Parallelism and Layer Parallelism - Deep Dive

Lecture 44 LLM Relevance and Implementaion

Lecture 45 Model vs Data Parallelism

Lecture 46 Key Differences Highlighted - Data vs Model Parallelism

Lecture 47 Data vs Model Parallelism

Lecture 48 Hybrid Parallelism - Animation

Lecture 49 Hybrid Parallelism - What is It and Motivation

Section 10: Types of Parallelism - Pipeline and Tensor Parallelism

Lecture 50 Pipeline Parallelism Overview

Lecture 51 Pipeline Parallelism Key Concepts and How it Works - Step by Step

Lecture 52 Pipeline Bubbles Key Concepts

Lecture 53 Pipeline Schedules Key Concepts

Lecture 54 Activation Recomputation - Overview and Introduction

Lecture 55 Neural Network and Activation and Backward and Forward Passes - Full Dive

Lecture 56 Understanding Activation Recomputation vs Standard Training - Deep Dive

Lecture 57 Demo - Activation Recomputation Visualization

Lecture 58 Activation Recomputation vs Standard Approach

Lecture 59 Benefits of Activation Recomputation and Implementation Strategies

Lecture 60 Pipeline Parallelism Implementation Frameworks and Key Takeaways

Section 11: Tensor Parallelism - Deep Dive

Lecture 61 What is Tensor Parallelism and Why - Benefits

Lecture 62 Tensor Parallel Pizza Making Analogy

Lecture 63 Tensors and Partitioning Strategies - Deep Dive

Lecture 64 Tensor Communication Patterns - Deep Dive

Lecture 65 Device Mesh Communication Pattern - Deep Dive

Lecture 66 How Components Work Together in Distributed LLM Training

Lecture 67 Understanding Tensor Parallelism with LEGO Bricks Animation Demo

Lecture 68 Putting it All Together - All Strategies in LLM Training

Section 12: HANDS-ON: Strategies for Parallelism - Data Parallelism Deep Dive

Lecture 69 Strategies for Parallelizing LLMs - Hands- on Introduction

Lecture 70 Pytorch - LLM Training Library Overview

Lecture 71 The Transformers Library - Overview

Lecture 72 Numpy Overview

Lecture 73 TorchVision and TorchDistributed Overview

Lecture 74 DeepSpeed and Megatron-LM - Overview

Lecture 75 Datasets and Why this Toolkit

Lecture 76 HANDS-On: Data Parallelism - Training a Small Model - MNIST Dataset

Lecture 77 Testing Pseudo Data Parallelism Trained Model

Lecture 78 HANDS-ON: Data Parallelism - Colab - Full Demo

Lecture 79 Data Parallelism - Simulated Parallelism on GPU Takeaways

Section 13: HANDS-ON: Data Parallelism w/ WikiText Dataset & DeepSpeed Mem. Optimizatization

Lecture 80 Hands-on: Data Parallelism - Wikitext-2 Dataset

Lecture 81 DeepSpeed - Full Dive

Lecture 82 Hands-on: Data Parallelism with DeepSpeed Optimization

Section 14: Running TRUE Parallelism on Multiple GPU Systems - Runpod.io

Lecture 83 Setup Runpod.io Environment Overview

Lecture 84 Runpod SSH Setup

Lecture 85 Setting up Runpod Parallelism in JupyterNotebook

Lecture 86 HANDS-ON - Parallelism with IMDB Dataset - Deep Dive - True Parallelism

Lecture 87 Runpod Cleanup

Section 15: Fault Tolerance and Scalability & Advanced Checkpointing Strategies - Deep Dive

Lecture 88 Fault Tolerance Introduction & Types of Failures in Distributed LLM Training

Lecture 89 Strategies for Fault Tolerance

Lecture 90 Checkpointing in LLM Training - Animation

Lecture 91 Basic Checkpointing in LLM Taining

Lecture 92 Incremental Checkpointing in LLM Training

Lecture 93 Asynchronous Checkpointing in LLM Training

Lecture 94 Multi-level Checkpointing in LLM Training - Animation

Lecture 95 Checkpoint Storage Considerations - Deep Dive

Lecture 96 Implementing a Hybrid Approach - Performance, Failure, Optimizations - Full Dive

Lecture 97 Checkpoint Storage Strategy - Summary

Section 16: Advanced Topics and Emerging Trends

Lecture 98 Advanced Topics and Emerging Trends

Section 17: Wrap up and Next Steps

Lecture 99 Course Summary and Next Steps

Machine learning engineers and data scientists looking to scale LLM training.,AI researchers interested in distributed computing and parallelism strategies.,Developers and engineers working with multi-GPU systems who want to optimize LLM performance.,Anyone with a basic understanding of deep learning and Python who wants to master advanced LLM training techniques.