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:
-
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.
-
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
- 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
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.