End-to-end LLM training on instance clusters with over 100 nodes using AWS Trainium

This article discusses end-to-end training of a large language model (LLM), Llama 2-7B, on AWS Trainium clusters with over 100 nodes. It covers the challenges involved in distributed training at this scale and provides best practices for addressing them.

Specifically, the article covers:

• Setting up the infrastructure with 128 trn1.32xlarge instances and data preparation
• Optimizing distributed training efficiency and scalability using techniques like model/data parallelism, precision formats like BF16, and compiler optimizations
• Efficient hardware and system recovery using checkpointing and automatic fault recovery
• Improving training stability and convergence through techniques like scaled initialization, gradient synchronization, and persistent cache management
• Evaluation of the trained model's quality on various tasks, showing comparable performance to the open-source version
• Demonstration of good training throughput scalability on Trainium clusters