Title

AWS re:Invent 2022 - Choosing the right accelerator for training and inference (CMP207)

Summary

• Samir and Max from AWS discuss the growing complexity and size of machine learning models, which now range from billions to trillions of parameters.
• They emphasize the importance of selecting the right combination of hardware (GPUs, CPUs, accelerators) for training and inference to maximize performance and reduce costs.
• Machine learning workloads are classified into small, intermediate, big, and huge scenarios, with a focus on the latter two.
• AWS offers a range of instances powered by CPUs, GPUs, and custom accelerators to cater to different machine learning needs.
• SageMaker is highlighted as a managed service that simplifies the machine learning pipeline, handling infrastructure management and allowing data scientists to focus on model building and deployment.
• Distributed training is necessary for large models or datasets and can be achieved through data parallelism, pipeline parallelism, or tensor parallelism.
• AWS's EC2 Ultra Cluster is introduced as a supercomputer option for training gigantic models with billions of parameters.
• Customer use cases are presented to illustrate the practical application of AWS's machine learning infrastructure and services.

Insights

• The rapid growth in machine learning model complexity necessitates the use of specialized hardware accelerators to efficiently handle the increased computational demands.
• AWS provides a diverse set of instances tailored for different stages of machine learning development, from experimentation to large-scale production deployment.
• The choice of hardware (CPU vs. GPU vs. custom accelerator) should be based on the specific requirements of the workload, including model size, data size, and cost considerations.
• SageMaker's managed service approach can significantly reduce the operational burden on data scientists, allowing them to focus on model development rather than infrastructure management.
• Distributed training techniques are critical for handling very large models that cannot fit into the memory of a single accelerator or need to be trained on large datasets quickly.
• AWS's EC2 Ultra Cluster represents a significant advancement in cloud-based supercomputing, enabling customers to train models with trillions of parameters.
• Real-world customer stories demonstrate the tangible benefits of using AWS's machine learning infrastructure, including cost savings, performance improvements, and the ability to scale inference workloads.