Performance is a key consideration of successful ML research and production solutions. Faster model training leads to faster iterations and reduced overhead. It is sometimes an essential requirement to make a particular ML solution feasible.
What is the TensorFlow Profiler?
The TensorFlow Profiler (or the Profiler) provides a set of tools that you can use to measure the training performance and resource consumption of your TensorFlow models. This new version of the Profiler is integrated into TensorBoard, and builds upon existing capabilities such as the Trace Viewer.
The Profiler has the following new profiling tools available:
Overview Page: Provides a top-level view of model performance and recommendations to optimize performance
Input Pipeline Analyzer: Analyzes your model’s data input pipeline for bottlenecks and recommends improvements to improve performance
TensorFlow Stats: Displays performance statistics for every TensorFlow operation executed during the profiling session
GPU Kernel Stats: Displays performance statistics and the originating operation for every GPU accelerated kernel
Check out theProfiler guidein the TensorFlow documentation to learn more about these tools.