Language models are statistical methods predicting the succession of tokens in sequences, using natural text. Large language models (LLMs) are neural network-based language models with hundreds of millions (BERT) to over a trillion parameters (MiCS), and whose size makes single-GPU training impractical. LLMs’ generative abilities make them popular for text synthesis, summarization, machine translation, and more.
The size of an LLM and its training data is a double-edged sword: it brings modeling quality, but entails infrastructure challenges. The model itself is often too big to fit in memory of a single GPU device or on the multiple devices of a multi-GPU instance. These factors require training an LLM over large clusters of accelerated machine learning (ML) instances. In the past few years, numerous customers have been using the AWS Cloud for LLM training.
In this post, we dive into tips and best practices for successful LLM training on Amazon SageMaker

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