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
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.10.1
on 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.
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
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.
- 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