Performance

Performance is often a significant issue when training a machine learning
model. This section explains various ways to optimize performance. Start
your investigation with the following guide:

• @{$performance_guide$Performance}, which contains a collection of best
  practices for optimizing your TensorFlow code.

XLA (Accelerated Linear Algebra) is an experimental compiler for linear
algebra that optimizes TensorFlow computations. The following guides explore
XLA:

• @{$xla$XLA Overview}, which introduces XLA.
• @{$broadcasting$Broadcasting Semantics}, which describes XLA’s
  broadcasting semantics.
• @{$developing_new_backend$Developing a new back end for XLA}, which
  explains how to re-target TensorFlow in order to optimize the performance
  of the computational graph for particular hardware.
• @{$jit$Using JIT Compilation}, which describes the XLA JIT compiler that
  compiles and runs parts of TensorFlow graphs via XLA in order to optimize
  performance.
• @{$operation_semantics$Operation Semantics}, which is a reference manual
  describing the semantics of operations in the ComputationBuilder
  interface.
• @{$shapes$Shapes and Layout}, which details the Shape protocol buffer.
• @{$tfcompile$Using AOT compilation}, which explains tfcompile, a
  standalone tool that compiles TensorFlow graphs into executable code in
  order to optimize performance.

And finally, we offer the following guide:

• @{$quantization$How to Quantize Neural Networks with TensorFlow}, which
  can explains how to use quantization to reduce model size, both in storage
  and at runtime. Quantization can improve performance, especially on
  mobile hardware.