kingers Posted May 4 Report Share Posted May 4 Strategies For Parallelizing Llms Masterclass Published 3/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 3.89 GB | Duration: 8h 41mMastering 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.AusFilehttps://ausfile.com/2u3g58te6c2c/yxusj..-.Strategies.for.Parallelizing.LLMs.Masterclass.-.Paulo.Dichone.Mar.2025.part1.rarhttps://ausfile.com/raz3114vj1bl/yxusj..-.Strategies.for.Parallelizing.LLMs.Masterclass.-.Paulo.Dichone.Mar.2025.part2.rarhttps://ausfile.com/6kmnbg72t91c/yxusj..-.Strategies.for.Parallelizing.LLMs.Masterclass.-.Paulo.Dichone.Mar.2025.part3.rarhttps://ausfile.com/arm2r2yqujl7/yxusj..-.Strategies.for.Parallelizing.LLMs.Masterclass.-.Paulo.Dichone.Mar.2025.part4.rarhttps://ausfile.com/d3dy2btovzj5/yxusj..-.Strategies.for.Parallelizing.LLMs.Masterclass.-.Paulo.Dichone.Mar.2025.part5.rarhttps://ausfile.com/uuw9i4iaaoab/yxusj..-.Strategies.for.Parallelizing.LLMs.Masterclass.-.Paulo.Dichone.Mar.2025.part6.rarDDownloadhttps://ddownload.com/mgr1sox8jfnf/yxusj..-.Strategies.for.Parallelizing.LLMs.Masterclass.-.Paulo.Dichone.Mar.2025.part1.rarhttps://ddownload.com/kuvczom2dh49/yxusj..-.Strategies.for.Parallelizing.LLMs.Masterclass.-.Paulo.Dichone.Mar.2025.part2.rarhttps://ddownload.com/eyir9ubc19wp/yxusj..-.Strategies.for.Parallelizing.LLMs.Masterclass.-.Paulo.Dichone.Mar.2025.part3.rarhttps://ddownload.com/dm8byzwn69o6/yxusj..-.Strategies.for.Parallelizing.LLMs.Masterclass.-.Paulo.Dichone.Mar.2025.part4.rarhttps://ddownload.com/bdx80wroa78j/yxusj..-.Strategies.for.Parallelizing.LLMs.Masterclass.-.Paulo.Dichone.Mar.2025.part5.rarhttps://ddownload.com/d7gcmkmn20ri/yxusj..-.Strategies.for.Parallelizing.LLMs.Masterclass.-.Paulo.Dichone.Mar.2025.part6.rarRapidGatorhttps://rapidgator.net/file/3632db67c3e0c20a40009aaf5620b009/yxusj..-.Strategies.for.Parallelizing.LLMs.Masterclass.-.Paulo.Dichone.Mar.2025.part1.rarhttps://rapidgator.net/file/7fcc13b27f88138daf62c843add12f39/yxusj..-.Strategies.for.Parallelizing.LLMs.Masterclass.-.Paulo.Dichone.Mar.2025.part2.rarhttps://rapidgator.net/file/b5722d124161f96e041b40570e6f8bbd/yxusj..-.Strategies.for.Parallelizing.LLMs.Masterclass.-.Paulo.Dichone.Mar.2025.part3.rarhttps://rapidgator.net/file/1459e134a19e7797a0383897b360adb2/yxusj..-.Strategies.for.Parallelizing.LLMs.Masterclass.-.Paulo.Dichone.Mar.2025.part4.rarhttps://rapidgator.net/file/8505a460cddc9b0f8e13031a593efd3f/yxusj..-.Strategies.for.Parallelizing.LLMs.Masterclass.-.Paulo.Dichone.Mar.2025.part5.rarhttps://rapidgator.net/file/a3d01eb0c8653306b36e2cc2457e6bee/yxusj..-.Strategies.for.Parallelizing.LLMs.Masterclass.-.Paulo.Dichone.Mar.2025.part6.rar Link to comment Share on other sites More sharing options...
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