[](https://github.com/CentML/skyline/blob/main/LICENSE)
[](https://pypi.org/project/skyline-profiler/)
Skyline is a tool to profile and debug the training performance of [PyTorch](https://pytorch.org) neural networks.
- [Installation](#installation)
- [Usage example](#getting-started)
- [Development Environment Setup](#dev-setup)
- [Release Process](#release-process)
- [Release History](#release-history)
- [Meta](#meta)
- [Contributing](#contributing)
<h2 id="installation">Installation</h2>
Skyline works with *GPU-based* neural networks that are implemented in [PyTorch](https://pytorch.org).
To run Skyline, you need:
- A system equipped with an NVIDIA GPU
- Python 3.7+
- [Poetry](https://python-poetry.org/)
- PyTorch 1.1.0+ with CUDA
- **NOTE:** We assume you have the correct version of PyTorch installed for their GPU. Default PyTorch installation on Linux distros might not have CUDA support. If you see error similar to below, your PyTorch version is incompatible with your version of CUDA. You can download the appropriate version from the [PyTorch site](https://pytorch.org/get-started/locally/)
```NVIDIA GeForce RTX 3060 Ti with CUDA capability sm_86 is not compatible with the current PyTorch installation.
The current PyTorch install supports CUDA capabilities sm_37 sm_50 sm_60 sm_70.
If you want to use the NVIDIA GeForce RTX 3060 Ti GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/
```
- Python 3.6+ or Python 3.7+ on OSX
### Installation from source
```bash
git clone https://github.com/CentML/skyline.git
cd skyline
poetry install
poetry run skyline --help
```
### Installation from PyPi
**Note:** Not implemented yet
Installing with [Poetry](https://python-poetry.org/)
```zsh
poetry add skyline-profiler
poetry run skyline --help
```
Installing with [Pipenv](https://pipenv.pypa.io/en/latest/)
```zsh
pipenv install skyline-profiler
pipenv run skyline --help
```
Installing with [Pip](https://packaging.python.org/en/latest/tutorials/installing-packages/#use-pip-for-installing)
```zsh
python3 -m pip install skyline-profiler
python3 skyline
```
<h2 id="getting-started">Usage example</h2>
To use Skyline in your project, you need to first write an entry point file, which is a regular Python file that describes how your model is created and trained. See the [Entry Point](docs/providers.md) for more information.
Once your entry point file is ready, there are two ways to profile interactive profiling and standalone profiling.
### Interactive Profiling
```zsh
poetry run skyline interactive --skip-atom path/to/entry/point/file
```
### Standalone Profiling
Standalone profiling is useful when you just want access to Skyline's profiling functionality. Skyline will save the profiling results (called a "report") into a [SQLite database file](https://www.sqlite.org/) that you can then query yourself. We describe the database schema for Skyline's run time and memory reports in the [Run Time Report Format](docs/run-time-report.md) and [Memory Report Format](docs/memory-report.md) pages respectively.
To have Skyline perform run time profiling, you use the `skyline time`
subcommand. In addition to the entry point file, you also need to specify the
file where you want Skyline to save the run time profiling report using the
`--output` or `-o` flag.
```zsh
poetry run skyline time entry_point.py --output my_output_file.sqlite
```
Launching memory profiling is almost the same as launching run time profiling.
You just need to use `skyline memory` instead of `skyline time`.
```zsh
poetry run skyline memory entry_point.py --output my_output_file.sqlite
```
<h2 id="dev-setup">Development Environment Setup</h2>
From the project root, do
```zsh
poetry install
```
<h2 id="release-process">Release Process</h2>
1. Make sure you're on main branch and it is clean
1. Run [tools/prepare-release.sh](tools/prepare-release.sh) which will:
* Increment the version
* Create a release branch
* Create a release PR
1. After the PR is merged [build-and-publish-new-version.yml](.github/workflows/build-and-publish-new-version.yml) GitHub action will:
* build the Python Wheels
* GitHub release
* Try to publish to Test PyPI
* Subject to approval publish to PyPI
<h2 id="release-history">Release History</h2>
See [Releases](https://github.com/UofT-EcoSystem/skyline/releases)
<h2 id="meta">Meta</h2>
Skyline began as a research project at the [University of Toronto](https://web.cs.toronto.edu) in collaboration with [Geofrey Yu](mailto:gxyu@cs.toronto.edu), [Tovi Grossman](https://www.tovigrossman.com) and [Gennady Pekhimenko](https://www.cs.toronto.edu/~pekhimenko/).
The accompanying research paper appears in the proceedings of UIST'20. If you are interested, you can read a preprint of the paper [here](https://arxiv.org/pdf/2008.06798.pdf).
If you use Skyline in your research, please consider citing our paper:
```bibtex
@inproceedings{skyline-yu20,
title = {{Skyline: Interactive In-Editor Computational Performance Profiling
for Deep Neural Network Training}},
author = {Yu, Geoffrey X. and Grossman, Tovi and Pekhimenko, Gennady},
booktitle = {{Proceedings of the 33rd ACM Symposium on User Interface
Software and Technology (UIST'20)}},
year = {2020},
}
```
It is distributed under Apache 2.0 license. See [LICENSE](LICENSE) and [NOTICE](NOTICE) for more information.
<h2 id="contributing">Contributing</h2>
Check out [CONTRIBUTING.md](CONTRIBUTING.md) for more information on how to help with Skyline.
Raw data
{
"_id": null,
"home_page": "https://github.com/CentML/skyline",
"name": "skyline-profiler",
"maintainer": "Akbar Nurlybayev",
"docs_url": null,
"requires_python": ">=3.8,<4.0",
"maintainer_email": "akbar@centml.ai",
"keywords": "pytorch,neural networks,debugger,profiler",
"author": "Geoffrey Yu",
"author_email": "gxyu@cs.toronto.edu",
"download_url": "https://files.pythonhosted.org/packages/3f/eb/216842ef33bdaacef09be62baae2d6600b681e07e9ad6effc909a4aaa403/skyline_profiler-0.12.1.tar.gz",
"platform": null,
"description": "\n[](https://github.com/CentML/skyline/blob/main/LICENSE)\n\n[](https://pypi.org/project/skyline-profiler/)\n\nSkyline is a tool to profile and debug the training performance of [PyTorch](https://pytorch.org) neural networks.\n\n- [Installation](#installation)\n- [Usage example](#getting-started)\n- [Development Environment Setup](#dev-setup)\n- [Release Process](#release-process)\n- [Release History](#release-history)\n- [Meta](#meta)\n- [Contributing](#contributing)\n\n<h2 id=\"installation\">Installation</h2>\n\nSkyline works with *GPU-based* neural networks that are implemented in [PyTorch](https://pytorch.org).\n\nTo run Skyline, you need:\n- A system equipped with an NVIDIA GPU\n- Python 3.7+\n- [Poetry](https://python-poetry.org/)\n- PyTorch 1.1.0+ with CUDA\n - **NOTE:** We assume you have the correct version of PyTorch installed for their GPU. Default PyTorch installation on Linux distros might not have CUDA support. If you see error similar to below, your PyTorch version is incompatible with your version of CUDA. You can download the appropriate version from the [PyTorch site](https://pytorch.org/get-started/locally/)\n ```NVIDIA GeForce RTX 3060 Ti with CUDA capability sm_86 is not compatible with the current PyTorch installation.\n The current PyTorch install supports CUDA capabilities sm_37 sm_50 sm_60 sm_70.\n If you want to use the NVIDIA GeForce RTX 3060 Ti GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/\n ```\n- Python 3.6+ or Python 3.7+ on OSX\n\n\n### Installation from source\n```bash\ngit clone https://github.com/CentML/skyline.git\ncd skyline\npoetry install\npoetry run skyline --help\n```\n\n### Installation from PyPi\n\n**Note:** Not implemented yet\n\nInstalling with [Poetry](https://python-poetry.org/)\n```zsh\npoetry add skyline-profiler\npoetry run skyline --help\n```\n\nInstalling with [Pipenv](https://pipenv.pypa.io/en/latest/)\n```zsh\npipenv install skyline-profiler\npipenv run skyline --help\n```\n\nInstalling with [Pip](https://packaging.python.org/en/latest/tutorials/installing-packages/#use-pip-for-installing)\n```zsh\npython3 -m pip install skyline-profiler\npython3 skyline\n```\n\n<h2 id=\"getting-started\">Usage example</h2>\n\nTo use Skyline in your project, you need to first write an entry point file, which is a regular Python file that describes how your model is created and trained. See the [Entry Point](docs/providers.md) for more information.\n\nOnce your entry point file is ready, there are two ways to profile interactive profiling and standalone profiling.\n\n### Interactive Profiling\n```zsh\npoetry run skyline interactive --skip-atom path/to/entry/point/file\n```\n\n### Standalone Profiling\nStandalone profiling is useful when you just want access to Skyline's profiling functionality. Skyline will save the profiling results (called a \"report\") into a [SQLite database file](https://www.sqlite.org/) that you can then query yourself. We describe the database schema for Skyline's run time and memory reports in the [Run Time Report Format](docs/run-time-report.md) and [Memory Report Format](docs/memory-report.md) pages respectively.\n\nTo have Skyline perform run time profiling, you use the `skyline time`\nsubcommand. In addition to the entry point file, you also need to specify the\nfile where you want Skyline to save the run time profiling report using the\n`--output` or `-o` flag.\n\n```zsh\npoetry run skyline time entry_point.py --output my_output_file.sqlite\n```\n\nLaunching memory profiling is almost the same as launching run time profiling.\nYou just need to use `skyline memory` instead of `skyline time`.\n\n```zsh\npoetry run skyline memory entry_point.py --output my_output_file.sqlite\n```\n\n<h2 id=\"dev-setup\">Development Environment Setup</h2>\n\nFrom the project root, do\n```zsh\npoetry install\n```\n<h2 id=\"release-process\">Release Process</h2>\n\n1. Make sure you're on main branch and it is clean\n1. Run [tools/prepare-release.sh](tools/prepare-release.sh) which will:\n * Increment the version\n * Create a release branch\n * Create a release PR\n1. After the PR is merged [build-and-publish-new-version.yml](.github/workflows/build-and-publish-new-version.yml) GitHub action will:\n * build the Python Wheels\n * GitHub release\n * Try to publish to Test PyPI\n * Subject to approval publish to PyPI\n\n<h2 id=\"release-history\">Release History</h2>\n\nSee [Releases](https://github.com/UofT-EcoSystem/skyline/releases)\n\n<h2 id=\"meta\">Meta</h2>\n\nSkyline began as a research project at the [University of Toronto](https://web.cs.toronto.edu) in collaboration with [Geofrey Yu](mailto:gxyu@cs.toronto.edu), [Tovi Grossman](https://www.tovigrossman.com) and [Gennady Pekhimenko](https://www.cs.toronto.edu/~pekhimenko/).\n\nThe accompanying research paper appears in the proceedings of UIST'20. If you are interested, you can read a preprint of the paper [here](https://arxiv.org/pdf/2008.06798.pdf).\n\nIf you use Skyline in your research, please consider citing our paper:\n\n```bibtex\n@inproceedings{skyline-yu20,\n title = {{Skyline: Interactive In-Editor Computational Performance Profiling\n for Deep Neural Network Training}},\n author = {Yu, Geoffrey X. and Grossman, Tovi and Pekhimenko, Gennady},\n booktitle = {{Proceedings of the 33rd ACM Symposium on User Interface\n Software and Technology (UIST'20)}},\n year = {2020},\n}\n```\n\nIt is distributed under Apache 2.0 license. See [LICENSE](LICENSE) and [NOTICE](NOTICE) for more information.\n\n<h2 id=\"contributing\">Contributing</h2>\n\nCheck out [CONTRIBUTING.md](CONTRIBUTING.md) for more information on how to help with Skyline.\n\n",
"bugtrack_url": null,
"license": "Apache-2.0",
"summary": "Interactive performance profiling and debugging tool for PyTorch neural networks.",
"version": "0.12.1",
"split_keywords": [
"pytorch",
"neural networks",
"debugger",
"profiler"
],
"urls": [
{
"comment_text": "",
"digests": {
"md5": "0efa30fa8881bab4c3aef36a6626b26f",
"sha256": "8483a7fcf84007ed6043ab7602d2e4630a691997c76dc87733e131a960494b4e"
},
"downloads": -1,
"filename": "skyline_profiler-0.12.1-py3-none-any.whl",
"has_sig": false,
"md5_digest": "0efa30fa8881bab4c3aef36a6626b26f",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.8,<4.0",
"size": 71442,
"upload_time": "2022-12-14T22:50:04",
"upload_time_iso_8601": "2022-12-14T22:50:04.826730Z",
"url": "https://files.pythonhosted.org/packages/8a/3d/01499693b3a38c8ef2c129b193af3ebf32beae31a0acd0e7dca3adf46676/skyline_profiler-0.12.1-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"md5": "91bbb185591fc881c3457d6506308de0",
"sha256": "116b19da9b43219d92bb8a41a3df7dd38ea25a149879c70efec2fca54b499c91"
},
"downloads": -1,
"filename": "skyline_profiler-0.12.1.tar.gz",
"has_sig": false,
"md5_digest": "91bbb185591fc881c3457d6506308de0",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.8,<4.0",
"size": 53455,
"upload_time": "2022-12-14T22:50:06",
"upload_time_iso_8601": "2022-12-14T22:50:06.327413Z",
"url": "https://files.pythonhosted.org/packages/3f/eb/216842ef33bdaacef09be62baae2d6600b681e07e9ad6effc909a4aaa403/skyline_profiler-0.12.1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2022-12-14 22:50:06",
"github": true,
"gitlab": false,
"bitbucket": false,
"github_user": "CentML",
"github_project": "skyline",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "skyline-profiler"
}
JSON
