Building Distributed Deep Learning Infrastructure

This chapter explores the complexities of creating an infrastructure for distributed deep learning, addressing the challenges of managing large datasets and GPU utilization. It emphasizes the role of technological advancements and DevOps practices, alongside the collaboration between scientists and engineers to enhance research efficiency. The discussion also highlights the importance of flexibility and standardization in deep learning frameworks, particularly in relation to recent advancements in semantic segmentation and self-supervised learning.