Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/tensorflow/profiler
A profiling and performance analysis tool for TensorFlow
https://github.com/tensorflow/profiler
Last synced: 3 months ago
JSON representation
A profiling and performance analysis tool for TensorFlow
- Host: GitHub
- URL: https://github.com/tensorflow/profiler
- Owner: tensorflow
- License: apache-2.0
- Created: 2020-03-10T15:58:33.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2024-03-29T18:20:55.000Z (3 months ago)
- Last Synced: 2024-03-30T00:34:17.834Z (3 months ago)
- Language: TypeScript
- Size: 30.8 MB
- Stars: 340
- Watchers: 19
- Forks: 51
- Open Issues: 128
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome-tf2 - TensorFlow Profiler
README
# TensorFlow Profiler
The profiler includes a suite of tools. These tools help you understand, debug and optimize TensorFlow programs to run on CPUs, GPUs and TPUs.
## Demo
First time user? Come and check out this [Colab Demo](https://www.tensorflow.org/tensorboard/tensorboard_profiling_keras).
## Prerequisites
* TensorFlow >= 2.2.0
* TensorBoard >= 2.2.0
* tensorboard-plugin-profile >= 2.2.0
Note: The TensorFlow Profiler 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 10.1 requires 418.x 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.10.1` 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 10.1 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
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.
## Next Steps
* 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