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 for

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.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!

Who this course is for

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.
Homepage:

https://www.udemy.com/course/llms-parallelism/