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A profiling and performance analysis tool for TensorFlow

A profiling and performance analysis tool for TensorFlow

TensorFlow Profiler

The profiler includes a suite of tools. These tools help you understand, debug and optimize TensorFlow programs to run on CPUs, GPUs and TPUs.

Demo

First time user? Come and check out this Colab Demo.

Prerequisites

  • TensorFlow >= 2.2.0rc0
  • TensorBoard >= 2.2.0 (or tb-nightly)
  • tensorboard-plugin-profile >= 2.2.0rc0

To profile on the GPU, the following NVIDIA software must be installed on your system:

  1. NVIDIA GPU drivers and CUDA Toolkit:

    • CUDA 10.2 requires 440.33 (Linux) / 441.22 (Windows) and higher. (recommended)
    • CUDA 10.1 requires 418.x and higher.
  2. Ensure that CUPTI exists on the path.

    • Run ldconfig -p | grep libcupti

    • If you don't have CUPTI on the path, run:

      export LD_LIBRARY_PATH=/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH
      
    • Run the ldconfig command above again to verify that the CUPTI library
      is found

To profile multi-worker GPU configurations, profile individual workers
independently.

To profile cloud TPUs, you must have access to Google Cloud TPUs.

Quick Start

Install the profiler by downloading and running the install_and_run.py script from this directory.

$ git clone https://github.com/tensorflow/profiler.git profiler
$ mkdir profile_env
$ python3 profiler/install_and_run.py --envdir=profile_env --logdir=profiler/demo

Go to localhost:6006/#profile of your browser, you should now see the demo overview page show up.

Congratulations! You're now ready to capture a profile.

Next Steps

Known Issues

Multi-GPU Profiling does not work with CUDA 10.1. While CUDA 10.2 is not officially supported by TF, profiling on CUDA 10.2 is known to work on some configurations.

GitHub

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