Tag: distributed training

Large language model (LLM) training has surged in popularity over the last year with the release of several popular models such as Llama 2, Falcon, and Mistral. Customers are now pre-training and fine-tuning LLMs ranging from 1 billion to over 175 billion parameters to optimize model performance for applications across industries, from healthcare to finance […]

Large language models (LLMs) have captured the imagination and attention of developers, scientists, technologists, entrepreneurs, and executives across several industries. These models can be used for question answering, summarization, translation, and more in applications such as conversational agents for customer support, content creation for marketing, and coding assistants. Recently, Meta released Llama 2 for both […]

Modern model pre-training often calls for larger cluster deployment to reduce time and cost. At the server level, such training workloads demand faster compute and increased memory allocation. As models grow to hundreds of billions of parameters, they require a distributed training mechanism that spans multiple nodes (instances). In October 2022, we launched Amazon EC2 […]

Today, a lot of customers are using TensorFlow to train deep learning models for their clickthrough rate in advertising and personalization recommendations in ecommerce. As the behavior of their clients change, they can accumulate large amounts of new data every day. Model iteration is one of a data scientist’s daily jobs, but they face the […]