The Increasing Influence of LLMs
In the world of artificial intelligence (AI), large language models (LLMs) are becoming increasingly impactful. These models possess impressive generative capabilities that have led to widespread adoption across diverse sectors and use cases. Their applications range from content generation and sentiment analysis to chatbot development and virtual assistant technology.
One such LLM is Llama2 by Meta, offered by Amazon Web Services (AWS). Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. It’s tailored for both commercial and research applications in English.
Fine-tuning LLMs with AWS
The model comes in a range of parameter sizes—7 billion, 13 billion, and 70 billion—and is available in both pre-trained and fine-tuned variants. You can learn more about Llama 2 on AWS in this resource.
Many practitioners enhance Llama 2’s accuracy by fine-tuning or pre-training these models with their text data. However, high costs can pose challenges when it comes to fine-tuning and training. Hence, as we push to further the capabilities of LLMs, cost-effective training solutions are increasingly in demand.
In this article, we explore how to leverage the Neuron distributed training library to fine-tune, continuously pre-train, and reduce the cost of training LLMs like Llama 2 using AWS Trainium instances on Amazon SageMaker.
Introducing AWS Trainium
Designed for high-performance deep learning training, SageMaker’s ml.trn1 and ml.trn1n instances, powered by Trainium accelerators, offer up to 50% cost-to-train savings over comparable training-optimized Amazon Elastic Compute Cloud (Amazon EC2) instances.
You can find more information about the Trainium Accelerator chips at AWS’s guide. You’ll also find customer testimonials and more specifics about the accelerator features and specifications.
Using SageMaker with Neuron Distributed Library
SageMaker offers a fully managed service that enables developers, data scientists, and practitioners to build, train, and deploy machine learning (ML) models seamlessly at scale. It includes several features designed to simplify the ML training experience like managed infrastructure, images for deep learning, automatic model tuning with hyperparameter optimization, and a pay-for-what-you-use billing structure.
This article offers an overview of SageMaker’s benefits for distributed training with the Neuron Distributed library. This includes cost-to-train benefits, managed infrastructure, and time-to-train associated with SageMaker’s resiliency and recovery features.
Resilience and Recovery Features of SageMaker
In high-performance computing (HPC) clusters used for deep learning model training, hardware resiliency challenges could become potential hurdles. As a cluster grows in size, the likelihood of running into stalled training due to hardware failures could increase. Regular checkpointing can help mitigate wasted compute time. However, engineering teams still need to closely monitor their workloads and prepare for failure remediation promptly to minimize training downtime.
In response, SageMaker Training offers several resiliency features that streamline this monitoring and recovery process. These include cluster health checks; automatic checkpointing; monitoring and tracking training; and built-in retries and cluster repair.
For customers operating large clusters of hundreds of instances, SageMaker Training’s resiliency and recovery features can help reduce the time to convergence of a model by up to 20% through fewer failures and faster recovery.
