Summary

In this chapter, we learned about the key features of Amazon SageMaker for large-scale distributed training. We looked at how to optimize your script, from importing packages to parsing arguments, writing code, invoking your script with mpi, writing to CloudWatch logs, checkpointing, working with the SM estimator, and so on. We covered key usability features to make SageMaker more fun and friendly to work with, such as warm pools for rapid experimentation, SSM and SSH in training instances, and tracking jobs. Finally, we learned about backend optimizations for distributed training, such as SMDDP collectives, using it both standalone and in combination with the model parallel package.

In the next chapter, we’ll explore even more advanced topics in distributed training!