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所在平台: Udemy |
课程主页: https://www.udemy.com/course/llms-parallelism/
课程评论：没有评论
Coursera LLM模型并行化策略大师班（Strategies for Parallelizing LLMs Masterclass）课程总结 本课程旨在帮助学员掌握大型语言模型（LLMs）的并行化训练技术，以应对大规模模型的训练挑战。 **核心学习内容：** * **基础知识：** 课程从IT概念、GPU架构、深度学习和LLM基础讲起，深入讲解并行计算的概念及其在大型模型训练中的重要性。 * **并行化策略：** 系统性地学习LLM训练中的核心并行化技术，包括数据并行、模型并行、流水线并行和张量并行，理解其理论基础和实际应用。 * **实践操作：** 通过深度实践，利用DeepSpeed这一领先的分布式训练框架，实现WikiText数据集上的数据并行，并掌握流水线并行策略。学员还将学习如何在RunPod这一多GPU云平台上部署模型，直观感受并行化训练的效果。 * **容错与扩展性：** 学习如何保障分布式LLM训练的容错性和可扩展性，包括先进的检查点（checkpointing）技术。 * **前沿趋势：** 探讨LLM并行化领域的最新趋势和高级主题，为学员在AI领域的未来发展做好准备。 **课程亮点：** * **实操性强：** 通过与DeepSpeed和RunPod的结合，学员将获得宝贵的实际操作经验。 * **深入浅出：** 每个环节都提供深入的讲解和实践案例，帮助学员理解并行化背后的原理和实施方法。 * **可扩展解决方案：** 无论是在单GPU还是分布式集群上，学员都能学到高效训练LLM的通用技术。 **目标学员：** * 希望扩展LLM训练能力机器学习工程师和数据科学家。 * 对分布式计算和并行化策略感兴趣的AI研究人员。 * 寻求优化LLM性能的多GPU系统开发者和工程师。 * 具备深度学习基础和Python知识，希望掌握高级LLM训练技术的任何人。 **先修要求：** * 具备Python编程和深度学习基础知识。 * 熟悉PyTorch或其他类似框架会更有帮助（非必需）。 * 需要一个支持GPU的环境（如RunPod）进行实践操作。
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!
