🏷️chap_performance

In deep learning, datasets and models are usually large, which involves heavy computation. Therefore, computational performance matters a lot. This chapter will focus on the major factors that affect computational performance: imperative programming, symbolic programming, asynchronous computing, automatic parallellism, and multi-GPU computation. By studying this chapter, you may further improve computational performance of those models implemented in the previous chapters, for example, by reducing training time without affecting accuracy.

:maxdepth: 2

hybridize
async-computation
auto-parallelism
hardware
multiple-gpus
multiple-gpus-concise
parameterserver