Computational Performance

:label: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.

  1. :maxdepth: 2
  3. hybridize
  4. async-computation
  5. auto-parallelism
  6. hardware
  7. multiple-gpus
  8. multiple-gpus-concise
  9. parameterserver