SageMaker HyperPod can speed up the training process by up to 40%, and users can choose to train on Amazon's custom Trainium chips or Nvidia-based GPU instances. The company has already used SageMaker to build LLMs, such as the Falcon 180B model, which was trained using a cluster of thousands of A100 GPUs. AWS used its experience from this and previous SageMaker scaling to develop HyperPod.
Key takeaways:
- Amazon's AWS cloud arm has announced the launch of SageMaker HyperPod, a service designed for training and fine-tuning large language models (LLMs).
- SageMaker HyperPod allows users to create a distributed cluster with accelerated instances, distribute models and data across the cluster, and save checkpoints frequently, speeding up the training process.
- The service can help train foundation models up to 40% faster and includes fail-safes to prevent the entire training process from failing if a GPU goes down.
- Users can choose to train on Amazon's custom Trainium chips or Nvidia-based GPU instances, and the company promises that HyperPod can speed up the training process by up to 40%.