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 Performance Guide and then go deeper with techniques detailed in High-Performance Models:
Performance Guide, which contains a collection of best practices for optimizing your TensorFlow code.
High-Performance Models, which contains a collection of advanced techniques to build highly scalable models targeting different system types and network topologies.
Benchmarks, which contains a collection of benchmark results.
XLA (Accelerated Linear Algebra) is an experimental compiler for linear algebra that optimizes TensorFlow computations. The following guides explore XLA:
tfcompile, a standalone tool that compiles TensorFlow graphs into executable code in order to optimize performance.
And finally, we offer the following guide:
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.