Accelerate Deep Learning Workloads with Amazon SageMaker

3

Managing SageMaker Development Environment

In previous chapters, we learned about the fundamental components and capabilities of Amazon SageMaker. By now, you know how to build and deploy your first simple models on SageMaker. In many more complex cases, however, you will need to write, profile, and test your DL code before deploying it to SageMaker-managed training or hosting clusters. Being able to perform this action locally while mocking SageMaker runtime will shorten development cycles and will avoid any unnecessary costs associated with provisioning SageMaker resources for development.

In this chapter, we will explore how to organize your development environment to effectively develop and test your DL models for SageMaker. This chapter ...