The profiler includes a suite of tools. These tools help you understand, debug and optimize TensorFlow programs to run on CPUs, GPUs and TPUs.
First time user? Come and check out this Colab Demo.
- 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:
NVIDIA GPU drivers and CUDA Toolkit:
- CUDA 10.1 requires 418.x and higher.
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.10.1on the path, prepend its installation directory to the $LD_LIBRARY_PATH environmental variable:
$ 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:
$ 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.
Install nightly version of 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
- 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/tensorboard_profiling_keras