XProf includes a suite of tools for JAX, TensorFlow, and PyTorch/XLA. These tools help you understand, debug and optimize programs to run on CPUs, GPUs and TPUs.
XProf offers a number of tools to analyse and visualize the performance of your model across multiple devices. Some of the tools include:
- Overview: A high-level overview of the performance of your model. This
is an aggregated overview for your host and all devices. It includes:
- Performance summary and breakdown of step times.
- A graph of individual step times.
- A table of the top 10 most expensive operations.
- Trace Viewer: Displays a timeline of the execution of your model that shows:
- The duration of each op.
- Which part of the system (host or device) executed an op.
- The communication between devices.
- Memory Profile Viewer: Monitors the memory usage of your model.
- Graph Viewer: A visualization of the graph structure of HLOs of your model.
First time user? Come and check out this Colab Demo.
- tensorboard-plugin-profile >= 2.19.0
- (optional) TensorBoard >= 2.19.0
Note: XProf 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 12.5 requires 525.60.13 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.12.5on 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 12.5 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.
In order to get the latest version of the profiler plugin, you can install the nightly package.
To install the nightly version of profiler:
$ pip uninstall tensorboard-plugin-profile
$ pip install tbp-nightly
Without TensorBoard:
$ xprof --logdir=profiler/demo --port=6006
With TensorBoard:
$ tensorboard --logdir=profiler/demo
If you are behind a corporate firewall, you may need to include the --bind_all
tensorboard flag.
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.
- JAX Profiling Guide: https://jax.readthedocs.io/en/latest/profiling.html
- TensorFlow 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
- Tensorflow Colab: https://www.tensorflow.org/tensorboard/tensorboard_profiling_keras
