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 >= 2.2.0
• TensorBoard >= 2.2.0
• tensorboard-plugin-profile >= 2.2.0

Note: The TensorFlow Profiler requires access to the Internet to load the Google Chart library. Some charts and tables may be missing if you run TensorBoard entirely offline on your local machine, behind a corporate firewall, or in a datacenter.

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

1. NVIDIA GPU drivers and CUDA Toolkit:
   • CUDA 10.1 requires 418.x and higher.
2. Ensure that CUPTI 10.1 exists on the path.

   $ /sbin/ldconfig -N -v $(sed 's/:/ /g' <<< $LD_LIBRARY_PATH) | grep libcupti

If you don't see

libcupti.so.10.1

   $ export LD_LIBRARY_PATH=/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH

Run the ldconfig command above again to verify that the CUPTI 10.1 library is found.

If this doesn't work, try:

shell
   $ sudo apt-get install libcupti-dev

To profile a system with multiple GPUs, see this guide for details.

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 nightly version of profiler by downloading and running the

install_and_run.py

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

localhost:6006/#profile

Next Steps

• GPU Profiling Guide: https://tensorflow.org/guide/profiler
• Cloud TPU Profiling Guide: https://cloud.google.com/tpu/docs/cloud-tpu-tools
• Colab Tutorial: https://www.tensorflow.org/tensorboard/tensorboardprofilingkeras