Udemy – Strategies for Parallelizing LLMs Masterclass 2025-3

Udemy – Strategies for Parallelizing LLMs Masterclass 2025-3 Downloadly IRSpace

Aug 16, 2025 - 07:25 Updated: Aug 17, 2025 - 20:02

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Udemy – Strategies for Parallelizing LLMs Masterclass 2025-3

Strategies for Parallelizing LLMs Masterclass, 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. Foundational 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).

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

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.

Specificatoin of Strategies for Parallelizing LLMs Masterclass

Publisher : Udemy
Teacher : Paulo Dichone | Software Engineer, AWS Cloud Practitioner & Instructor
Language : English
Level : All Levels
Number of Course : 99
Duration : 8 hours and 41 minutes

Content of Strategies for Parallelizing LLMs Masterclass

Strategies for Parallelizing LLMs Masterclass

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!

Udemy – Strategies for Parallelizing LLMs Masterclass 2025-3