# Tensorboard Profiler Plugin
The profiler includes a suite of 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 programs to run on CPUs, GPUs and TPUs.
The profiler plugin 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](https://www.tensorflow.org/tensorboard/tensorboard_profiling_keras).
## Prerequisites
* TensorFlow >= 2.18.0
* TensorBoard >= 2.18.0
* tensorboard-plugin-profile >= 2.18.0
Note: The Tensorboard Profiler Plugin 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 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:
1. NVIDIA GPU drivers and CUDA Toolkit:
* CUDA 12.5 requires 525.60.13 and higher.
2. Ensure that CUPTI 10.1 exists on the path.
```shell
$ /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:
```shell
$ 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:
```shell
$ sudo apt-get install libcupti-dev
```
To profile a system with **multiple GPUs**, see this [guide](docs/profile_multi_gpu.md) 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
The profiler plugin follows the TensorFlow versioning scheme. As a result, the
`tensorboard-plugin-profile` PyPI package can be behind the `tbp-nightly` PyPI
package. 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
```
Run 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
* MiniGPT Example: https://docs.jaxstack.ai/en/latest/JAX_for_LLM_pretraining.html
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