.. _chap_performance:

Computational Performance
=========================


In deep learning, datasets are usually large and model computation is
complex. Therefore, we are always very concerned about computing
performance. This chapter will focus on the important factors that
affect computing performance: imperative programming, symbolic
programming, asynchronous programing, automatic parallel computation,
and multi-GPU computation. By studying this chapter, you should be able
to further improve the computing performance of the models that have
been implemented in the previous chapters, for example, by reducing the
model training time without affecting the accuracy of the model.

.. toctree::
    :maxdepth: 2

    auto-parallelism
    hardware
    multiple-gpus-concise
    parameterserver