XProf (+ Tensorboard Profiler Plugin)

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.

Demo

First time user? Come and check out this Colab Demo.

Prerequisites

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.12.5 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 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.

Quick Start

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 xprof
$ pip install xprof-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.

Next Steps

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

Dependency	Optional
cheroot	false
etils	false
fsspec	false
gviz_api	false
packaging	false
protobuf	false
ratelimit	false
setuptools	false
six	false
werkzeug	false

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xprof

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