In this module, we will explore the different parallelism strategies for LLM training, including single GPU vs. parallel strategies. You'll understand how parallelism improves efficiency and learn its key advantages in real-world applications.

In this module, we will establish a foundational understanding of IT concepts crucial for training LLMs. Topics like cloud computing, storage solutions, and computer architecture will provide the context for optimizing LLM workflows.

In this module, we will explore GPU architecture and its role in LLM training. You'll learn how GPUs are designed to handle the massive computations required by large models, ensuring faster and more efficient training.

In this module, we will cover the fundamentals of machine learning and deep learning. We’ll explore neural networks, training processes, and key differences between ML and DL to lay the groundwork for LLM training.

In this module, we will dive into the fundamentals of LLMs, starting with the Transformer architecture. You'll learn about key components such as self-attention and how the Transformer library powers modern AI applications.

In this module, we will introduce parallel computing concepts and their relevance to LLM training. You’ll gain a deeper understanding of how parallelism reduces bottlenecks and accelerates model development.

In this module, we will explore data, model, and hybrid parallelism in detail. You’ll learn how each strategy optimizes training workflows and where to apply them for maximum efficiency in LLM training.

In this module, we will delve into pipeline and tensor parallelism, explaining their key concepts and how they work together to enhance training efficiency. You’ll also explore real-world strategies for implementing these techniques.

In this module, we will dive deep into tensor parallelism, focusing on partitioning strategies, communication patterns, and device synchronization. You'll gain a clear understanding of how this technique accelerates LLM training.

In this module, we will shift to hands-on learning, applying data parallelism techniques in PyTorch. You'll train a small model on the MNIST dataset, testing different parallelism strategies and observing their effects on performance.

In this module, we will apply data parallelism to the WikiText-2 dataset and use DeepSpeed to optimize memory usage. You'll gain hands-on experience with advanced techniques to improve LLM training efficiency.

In this module, we will guide you through setting up Runpod.io for multi-GPU parallelism. You’ll gain practical experience running parallelism experiments on a distributed environment and working with large-scale models.

In this module, we will dive into fault tolerance and checkpointing strategies. You'll learn how to ensure scalable, resilient LLM training workflows that can recover from failures and continue without interruptions.

In this module, we will explore cutting-edge advancements in parallel computing and LLM training. You'll gain insight into the latest trends and technologies that are revolutionizing AI and the future of machine learning.

In this module, we will wrap up the course by summarizing everything you've learned about parallelism and LLM training. You'll also receive guidance on how to proceed with your AI journey and apply these skills in future projects.