# XProf (+ Tensorboard Profiler Plugin)
XProf includes a suite of profiling tools for [JAX](https://jax.readthedocs.io/), [TensorFlow](https://www.tensorflow.org/), and [PyTorch/XLA](https://github.com/pytorch/xla). These tools help you understand, debug and optimize machine learning 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.
* High level details of the run environment.
* **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.
To learn more about the various XProf tools, check out the [XProf documentation](https://openxla.org/xprof)
## Demo
First time user? Come and check out this [Colab Demo](https://docs.jaxstack.ai/en/latest/JAX_for_LLM_pretraining.html).
## Quick Start
### Prerequisites
* xprof >= 2.20.0
* (optional) TensorBoard >= 2.20.0
Note: XProf requires access to the Internet to load the [Google Chart library](https://developers.google.com/chart/interactive/docs/basic_load_libs#basic-library-loading).
Some charts and tables may be missing if you run XProf entirely offline on
your local machine, behind a corporate firewall, or in a datacenter.
If you use Google Cloud to run your workloads, we recommend the
[xprofiler tool](https://github.com/AI-Hypercomputer/cloud-diagnostics-xprof).
It provides a streamlined profile collection and viewing experience using VMs
running XProf.
### Installation
To get the most recent release version of XProf, install it via pip:
```
$ pip install xprof
```
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.
## Nightlies
Every night, a nightly version of the package is released under the name of
`xprof-nightly`. This package contains the latest changes made by the XProf
developers.
To install the nightly version of profiler:
```
$ pip uninstall xprof tensorboard-plugin-profile
$ pip install xprof-nightly
```
## Next Steps
* [JAX Profiling Guide](https://jax.readthedocs.io/en/latest/profiling.html#xprof-tensorboard-profiling)
* [PyTorch/XLA Profiling Guide](https://cloud.google.com/tpu/docs/pytorch-xla-performance-profiling-tpu-vm)
* [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)
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"description": "# XProf (+ Tensorboard Profiler Plugin)\r\nXProf includes a suite of profiling tools for [JAX](https://jax.readthedocs.io/), [TensorFlow](https://www.tensorflow.org/), and [PyTorch/XLA](https://github.com/pytorch/xla). These tools help you understand, debug and optimize machine learning programs to run on CPUs, GPUs and TPUs.\r\n\r\nXProf offers a number of tools to analyse and visualize the\r\nperformance of your model across multiple devices. Some of the tools include:\r\n\r\n* **Overview**: A high-level overview of the performance of your model. This\r\n is an aggregated overview for your host and all devices. It includes:\r\n * Performance summary and breakdown of step times.\r\n * A graph of individual step times.\r\n * High level details of the run environment.\r\n* **Trace Viewer**: Displays a timeline of the execution of your model that shows:\r\n * The duration of each op.\r\n * Which part of the system (host or device) executed an op.\r\n * The communication between devices.\r\n* **Memory Profile Viewer**: Monitors the memory usage of your model.\r\n* **Graph Viewer**: A visualization of the graph structure of HLOs of your model.\r\n\r\nTo learn more about the various XProf tools, check out the [XProf documentation](https://openxla.org/xprof)\r\n\r\n## Demo\r\nFirst time user? Come and check out this [Colab Demo](https://docs.jaxstack.ai/en/latest/JAX_for_LLM_pretraining.html).\r\n\r\n## Quick Start\r\n\r\n### Prerequisites\r\n\r\n* xprof >= 2.20.0\r\n* (optional) TensorBoard >= 2.20.0\r\n\r\nNote: XProf requires access to the Internet to load the [Google Chart library](https://developers.google.com/chart/interactive/docs/basic_load_libs#basic-library-loading).\r\nSome charts and tables may be missing if you run XProf entirely offline on\r\nyour local machine, behind a corporate firewall, or in a datacenter.\r\n\r\nIf you use Google Cloud to run your workloads, we recommend the\r\n[xprofiler tool](https://github.com/AI-Hypercomputer/cloud-diagnostics-xprof).\r\nIt provides a streamlined profile collection and viewing experience using VMs\r\nrunning XProf.\r\n\r\n### Installation\r\n\r\nTo get the most recent release version of XProf, install it via pip:\r\n\r\n```\r\n$ pip install xprof\r\n```\r\n\r\nWithout TensorBoard:\r\n\r\n```\r\n$ xprof --logdir=profiler/demo --port=6006\r\n```\r\n\r\nWith TensorBoard:\r\n\r\n```\r\n$ tensorboard --logdir=profiler/demo\r\n```\r\nIf you are behind a corporate firewall, you may need to include the `--bind_all`\r\ntensorboard flag.\r\n\r\nGo to `localhost:6006/#profile` of your browser, you should now see the demo\r\noverview page show up.\r\nCongratulations! You're now ready to capture a profile.\r\n\r\n## Nightlies\r\n\r\nEvery night, a nightly version of the package is released under the name of\r\n`xprof-nightly`. This package contains the latest changes made by the XProf\r\ndevelopers.\r\n\r\nTo install the nightly version of profiler:\r\n\r\n```\r\n$ pip uninstall xprof tensorboard-plugin-profile\r\n$ pip install xprof-nightly\r\n```\r\n\r\n## Next Steps\r\n\r\n* [JAX Profiling Guide](https://jax.readthedocs.io/en/latest/profiling.html#xprof-tensorboard-profiling)\r\n* [PyTorch/XLA Profiling Guide](https://cloud.google.com/tpu/docs/pytorch-xla-performance-profiling-tpu-vm)\r\n* [TensorFlow Profiling Guide](https://tensorflow.org/guide/profiler)\r\n* [Cloud TPU Profiling Guide](https://cloud.google.com/tpu/docs/cloud-tpu-tools)\r\n* [Colab Tutorial](https://www.tensorflow.org/tensorboard/tensorboard_profiling_keras)\r\n* [Tensorflow Colab](https://www.tensorflow.org/tensorboard/tensorboard_profiling_keras)\r\n",
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