Deep Learning for Network Engineers by Toni Pasanen instant download

Deep Learning for Network Engineers by Toni Pasanen instant download

Modern Deep Learning models can be extremely large, often exceeding the memory capacity of a single GPU or CPU.

In these cases, training must be distributed across multiple processors. This introduces the need for high-speed communication between GPUs—both within a single server and across multiple servers.

Intra-node GPU communication typically relies on high-speed interconnects like NVLink, with Direct Memory Access operations enabling efficient data transfers between GPUs. Inter-node communication, however, depends on the backend network, either  InfiniBand or Ethernet-based. Synchronization of model parameters across GPUs places strict requirements on the network: high throughput, ultra-low latency, and zero packet loss. Achieving this in an Ethernet fabric is challenging but possible.  

This is where datacenter networking meets Deep Learning. Understanding how GPUs communicate and what the network must deliver is essential for designing effective AI data center infrastructures.

Deep Learning for Network Engineers bridges the gap between AI theory and modern data center network infrastructure. This book offers a technical foundation for network professionals who want to understand how Deep Neural Networks (DNNs) operate—and how GPU clusters communicate at scale.