Summary

In this chapter, we introduced accelerators for machine learning, including how they are different from standard CPU processing and why you need them for large-scale deep learning. We covered some techniques for acquiring accelerators and getting them ready for software development and model training. We covered key aspects of Amazon SageMaker, notably Studio, Training, and hosting. You should know that there are key software frameworks that let you run code on GPUs, such as NCCL, CUDA, and more. You should also know about the top features that AWS provides for high-performance GPU conception to train deep learning models, such as EFA, Nitro, and more. We covered finding and building containers with these packages preinstalled, to successfully run your scripts on them. We also covered debugging your code on SageMaker and troubleshooting GPU performance.

Now that we’ve learned about GPUs in some detail, in the next chapter, we’ll explore the fundamentals of...