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diff --git a/python-fiber.spec b/python-fiber.spec new file mode 100644 index 0000000..443550c --- /dev/null +++ b/python-fiber.spec @@ -0,0 +1,540 @@ +%global _empty_manifest_terminate_build 0 +Name: python-fiber +Version: 0.2.1 +Release: 1 +Summary: A distributed computing library for modern computer clusters +License: Apache 2.0 +URL: https://github.com/uber/fiber +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/ae/b2/cd58d966f0b3cde99046108dbfb8de80b9935dfb64606c4951321c30a240/fiber-0.2.1.tar.gz +BuildArch: noarch + + +%description +<p align="right"> + <a href="https://travis-ci.com/uber/fiber"> + <img src="https://travis-ci.com/uber/fiber.svg?token=BxMzxQEDDtTBPG9151kk&branch=master" alt="build" /> + </a> +</p> + +<img src="docs/img/fiber_logo.png" alt="drawing" width="550"/> + +[**Project Home**](https://uber.github.io/fiber/) +[**Blog**](https://uber.github.io/fiber/introduction/) +[**Documents**](https://uber.github.io/fiber/getting-started/) +[**Paper**](https://arxiv.org/abs/2003.11164) +[**Media Coverage**](https://venturebeat.com/2020/03/26/uber-details-fiber-a-framework-for-distributed-ai-model-training/) + +# Fiber + +### Distributed Computing for AI Made Simple + +*This project is experimental and the APIs are not considered stable.* + +Fiber is a Python distributed computing library for modern computer clusters. + +* It is easy to use. Fiber allows you to write programs that run on a computer cluster level without the need to dive into the details of computer cluster. +* It is easy to learn. Fiber provides the same API as Python's standard [multiprocessing](https://docs.python.org/3.6/library/multiprocessing.html) library that you are familiar with. If you know how to use multiprocessing, you can program a computer cluster with Fiber. +* It is fast. Fiber's communication backbone is built on top of [Nanomsg](https://nanomsg.org/) which is a high-performance asynchronous messaging library to allow fast and reliable communication. +* It doesn't need deployment. You run it as the same way as running a normal application on a computer cluster and Fiber handles the rest for you. +* It it reliable. Fiber has built-in error handling when you are running a pool of workers. Users can focus on writing the actual application code instead of dealing with crashed workers. + +Originally, it was developed to power large scale parallel scientific computation projects like [POET](https://eng.uber.com/poet-open-ended-deep-learning/) and it has been used to power similar projects within Uber. + + +## Installation + +``` +pip install fiber +``` + +Check [here](https://uber.github.io/fiber/installation/) for details. + +## Quick Start + + +### Hello Fiber +To use Fiber, simply import it in your code and it works very similar to multiprocessing. + +```python +import fiber + +if __name__ == '__main__': + fiber.Process(target=print, args=('Hello, Fiber!',)).start() +``` + +Note that `if __name__ == '__main__':` is necessary because Fiber uses *spawn* method to start new processes. Check [here](https://stackoverflow.com/questions/50781216/in-python-multiprocessing-process-do-we-have-to-use-name-main) for details. + +Let's take look at another more complex example: + +### Estimating Pi + + +```python +import fiber +import random + +@fiber.meta(cpu=1) +def inside(p): + x, y = random.random(), random.random() + return x * x + y * y < 1 + +def main(): + NUM_SAMPLES = int(1e6) + pool = fiber.Pool(processes=4) + count = sum(pool.map(inside, range(0, NUM_SAMPLES))) + print("Pi is roughly {}".format(4.0 * count / NUM_SAMPLES)) + +if __name__ == '__main__': + main() +``` + + +Fiber implements most of multiprocessing's API including `Process`, `SimpleQueue`, `Pool`, `Pipe`, `Manager` and it has its own extension to the multiprocessing's API to make it easy to compose large scale distributed applications. For the detailed API guild, check out [here](https://uber.github.io/fiber/process/). + +### Running on a Kubernetes cluster + +Fiber also has native support for computer clusters. To run the above example on Kubernetes, fiber provided a convenient command line tool to manage the workflow. + +Assume you have a working docker environment locally and have finished configuring [Google Cloud SDK](https://cloud.google.com/sdk/docs/quickstarts). Both `gcloud` and `kubectl` are available locally. Then you can start by writing a Dockerfile which describes the running environment. An example Dockerfile looks like this: + +```dockerfile +# example.docker +FROM python:3.6-buster +ADD examples/pi_estimation.py /root/pi_estimation.py +RUN pip install fiber +``` +**Build an image and launch your job** + +``` +fiber run -a python3 /root/pi_estimation.py +``` + +This command will look for local Dockerfile and build a docker image and push it to your Google Container Registry . It then launches the main job which contains your code and runs the command `python3 /root/pi_estimation.py` inside your job. Once the main job is running, it will start 4 subsequent jobs on the cluster and each of them is a Pool worker. + + +## Supported platforms + +* Operating system: Linux +* Python: 3.6+ +* Supported cluster management systems: + * Kubernetes (Tested with Google Kubernetes Engine on Google cloud) + +We are interested in supporting other cluster management systems like [Slurm](https://slurm.schedmd.com/), if you want to contribute to it please let us know. + + +Check [here](https://uber.github.io/fiber/platforms/) for details. + +## Documentation + +The documentation, including method/API references, can be found [here](https://uber.github.io/fiber/getting-started/). + + +## Testing + +Install test dependencies. You'll also need to make sure [docker](https://docs.docker.com/install/) is available on the testing machine. + +```bash +$ pip install -e .[test] +``` + +Run tests + +```bash +$ make test +``` + +## Contributing +Please read our [code of conduct](CODE_OF_CONDUCT.md) before you contribute! You can find details for submitting pull requests in the [CONTRIBUTING.md](CONTRIBUTING.md) file. Issue [template](https://help.github.com/articles/about-issue-and-pull-request-templates/). + +## Versioning +We document versions and changes in our changelog - see the [CHANGELOG.md](CHANGELOG.md) file for details. + +## License +This project is licensed under the Apache 2.0 License - see the [LICENSE](LICENSE) file for details. + +## Cite Fiber + +``` +@misc{zhi2020fiber, + title={Fiber: A Platform for Efficient Development and Distributed Training for Reinforcement Learning and Population-Based Methods}, + author={Jiale Zhi and Rui Wang and Jeff Clune and Kenneth O. Stanley}, + year={2020}, + eprint={2003.11164}, + archivePrefix={arXiv}, + primaryClass={cs.LG} +} +``` + +## Acknowledgments +* Special thanks to Piero Molino for designing the logo for Fiber + +%package -n python3-fiber +Summary: A distributed computing library for modern computer clusters +Provides: python-fiber +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-fiber +<p align="right"> + <a href="https://travis-ci.com/uber/fiber"> + <img src="https://travis-ci.com/uber/fiber.svg?token=BxMzxQEDDtTBPG9151kk&branch=master" alt="build" /> + </a> +</p> + +<img src="docs/img/fiber_logo.png" alt="drawing" width="550"/> + +[**Project Home**](https://uber.github.io/fiber/) +[**Blog**](https://uber.github.io/fiber/introduction/) +[**Documents**](https://uber.github.io/fiber/getting-started/) +[**Paper**](https://arxiv.org/abs/2003.11164) +[**Media Coverage**](https://venturebeat.com/2020/03/26/uber-details-fiber-a-framework-for-distributed-ai-model-training/) + +# Fiber + +### Distributed Computing for AI Made Simple + +*This project is experimental and the APIs are not considered stable.* + +Fiber is a Python distributed computing library for modern computer clusters. + +* It is easy to use. Fiber allows you to write programs that run on a computer cluster level without the need to dive into the details of computer cluster. +* It is easy to learn. Fiber provides the same API as Python's standard [multiprocessing](https://docs.python.org/3.6/library/multiprocessing.html) library that you are familiar with. If you know how to use multiprocessing, you can program a computer cluster with Fiber. +* It is fast. Fiber's communication backbone is built on top of [Nanomsg](https://nanomsg.org/) which is a high-performance asynchronous messaging library to allow fast and reliable communication. +* It doesn't need deployment. You run it as the same way as running a normal application on a computer cluster and Fiber handles the rest for you. +* It it reliable. Fiber has built-in error handling when you are running a pool of workers. Users can focus on writing the actual application code instead of dealing with crashed workers. + +Originally, it was developed to power large scale parallel scientific computation projects like [POET](https://eng.uber.com/poet-open-ended-deep-learning/) and it has been used to power similar projects within Uber. + + +## Installation + +``` +pip install fiber +``` + +Check [here](https://uber.github.io/fiber/installation/) for details. + +## Quick Start + + +### Hello Fiber +To use Fiber, simply import it in your code and it works very similar to multiprocessing. + +```python +import fiber + +if __name__ == '__main__': + fiber.Process(target=print, args=('Hello, Fiber!',)).start() +``` + +Note that `if __name__ == '__main__':` is necessary because Fiber uses *spawn* method to start new processes. Check [here](https://stackoverflow.com/questions/50781216/in-python-multiprocessing-process-do-we-have-to-use-name-main) for details. + +Let's take look at another more complex example: + +### Estimating Pi + + +```python +import fiber +import random + +@fiber.meta(cpu=1) +def inside(p): + x, y = random.random(), random.random() + return x * x + y * y < 1 + +def main(): + NUM_SAMPLES = int(1e6) + pool = fiber.Pool(processes=4) + count = sum(pool.map(inside, range(0, NUM_SAMPLES))) + print("Pi is roughly {}".format(4.0 * count / NUM_SAMPLES)) + +if __name__ == '__main__': + main() +``` + + +Fiber implements most of multiprocessing's API including `Process`, `SimpleQueue`, `Pool`, `Pipe`, `Manager` and it has its own extension to the multiprocessing's API to make it easy to compose large scale distributed applications. For the detailed API guild, check out [here](https://uber.github.io/fiber/process/). + +### Running on a Kubernetes cluster + +Fiber also has native support for computer clusters. To run the above example on Kubernetes, fiber provided a convenient command line tool to manage the workflow. + +Assume you have a working docker environment locally and have finished configuring [Google Cloud SDK](https://cloud.google.com/sdk/docs/quickstarts). Both `gcloud` and `kubectl` are available locally. Then you can start by writing a Dockerfile which describes the running environment. An example Dockerfile looks like this: + +```dockerfile +# example.docker +FROM python:3.6-buster +ADD examples/pi_estimation.py /root/pi_estimation.py +RUN pip install fiber +``` +**Build an image and launch your job** + +``` +fiber run -a python3 /root/pi_estimation.py +``` + +This command will look for local Dockerfile and build a docker image and push it to your Google Container Registry . It then launches the main job which contains your code and runs the command `python3 /root/pi_estimation.py` inside your job. Once the main job is running, it will start 4 subsequent jobs on the cluster and each of them is a Pool worker. + + +## Supported platforms + +* Operating system: Linux +* Python: 3.6+ +* Supported cluster management systems: + * Kubernetes (Tested with Google Kubernetes Engine on Google cloud) + +We are interested in supporting other cluster management systems like [Slurm](https://slurm.schedmd.com/), if you want to contribute to it please let us know. + + +Check [here](https://uber.github.io/fiber/platforms/) for details. + +## Documentation + +The documentation, including method/API references, can be found [here](https://uber.github.io/fiber/getting-started/). + + +## Testing + +Install test dependencies. You'll also need to make sure [docker](https://docs.docker.com/install/) is available on the testing machine. + +```bash +$ pip install -e .[test] +``` + +Run tests + +```bash +$ make test +``` + +## Contributing +Please read our [code of conduct](CODE_OF_CONDUCT.md) before you contribute! You can find details for submitting pull requests in the [CONTRIBUTING.md](CONTRIBUTING.md) file. Issue [template](https://help.github.com/articles/about-issue-and-pull-request-templates/). + +## Versioning +We document versions and changes in our changelog - see the [CHANGELOG.md](CHANGELOG.md) file for details. + +## License +This project is licensed under the Apache 2.0 License - see the [LICENSE](LICENSE) file for details. + +## Cite Fiber + +``` +@misc{zhi2020fiber, + title={Fiber: A Platform for Efficient Development and Distributed Training for Reinforcement Learning and Population-Based Methods}, + author={Jiale Zhi and Rui Wang and Jeff Clune and Kenneth O. Stanley}, + year={2020}, + eprint={2003.11164}, + archivePrefix={arXiv}, + primaryClass={cs.LG} +} +``` + +## Acknowledgments +* Special thanks to Piero Molino for designing the logo for Fiber + +%package help +Summary: Development documents and examples for fiber +Provides: python3-fiber-doc +%description help +<p align="right"> + <a href="https://travis-ci.com/uber/fiber"> + <img src="https://travis-ci.com/uber/fiber.svg?token=BxMzxQEDDtTBPG9151kk&branch=master" alt="build" /> + </a> +</p> + +<img src="docs/img/fiber_logo.png" alt="drawing" width="550"/> + +[**Project Home**](https://uber.github.io/fiber/) +[**Blog**](https://uber.github.io/fiber/introduction/) +[**Documents**](https://uber.github.io/fiber/getting-started/) +[**Paper**](https://arxiv.org/abs/2003.11164) +[**Media Coverage**](https://venturebeat.com/2020/03/26/uber-details-fiber-a-framework-for-distributed-ai-model-training/) + +# Fiber + +### Distributed Computing for AI Made Simple + +*This project is experimental and the APIs are not considered stable.* + +Fiber is a Python distributed computing library for modern computer clusters. + +* It is easy to use. Fiber allows you to write programs that run on a computer cluster level without the need to dive into the details of computer cluster. +* It is easy to learn. Fiber provides the same API as Python's standard [multiprocessing](https://docs.python.org/3.6/library/multiprocessing.html) library that you are familiar with. If you know how to use multiprocessing, you can program a computer cluster with Fiber. +* It is fast. Fiber's communication backbone is built on top of [Nanomsg](https://nanomsg.org/) which is a high-performance asynchronous messaging library to allow fast and reliable communication. +* It doesn't need deployment. You run it as the same way as running a normal application on a computer cluster and Fiber handles the rest for you. +* It it reliable. Fiber has built-in error handling when you are running a pool of workers. Users can focus on writing the actual application code instead of dealing with crashed workers. + +Originally, it was developed to power large scale parallel scientific computation projects like [POET](https://eng.uber.com/poet-open-ended-deep-learning/) and it has been used to power similar projects within Uber. + + +## Installation + +``` +pip install fiber +``` + +Check [here](https://uber.github.io/fiber/installation/) for details. + +## Quick Start + + +### Hello Fiber +To use Fiber, simply import it in your code and it works very similar to multiprocessing. + +```python +import fiber + +if __name__ == '__main__': + fiber.Process(target=print, args=('Hello, Fiber!',)).start() +``` + +Note that `if __name__ == '__main__':` is necessary because Fiber uses *spawn* method to start new processes. Check [here](https://stackoverflow.com/questions/50781216/in-python-multiprocessing-process-do-we-have-to-use-name-main) for details. + +Let's take look at another more complex example: + +### Estimating Pi + + +```python +import fiber +import random + +@fiber.meta(cpu=1) +def inside(p): + x, y = random.random(), random.random() + return x * x + y * y < 1 + +def main(): + NUM_SAMPLES = int(1e6) + pool = fiber.Pool(processes=4) + count = sum(pool.map(inside, range(0, NUM_SAMPLES))) + print("Pi is roughly {}".format(4.0 * count / NUM_SAMPLES)) + +if __name__ == '__main__': + main() +``` + + +Fiber implements most of multiprocessing's API including `Process`, `SimpleQueue`, `Pool`, `Pipe`, `Manager` and it has its own extension to the multiprocessing's API to make it easy to compose large scale distributed applications. For the detailed API guild, check out [here](https://uber.github.io/fiber/process/). + +### Running on a Kubernetes cluster + +Fiber also has native support for computer clusters. To run the above example on Kubernetes, fiber provided a convenient command line tool to manage the workflow. + +Assume you have a working docker environment locally and have finished configuring [Google Cloud SDK](https://cloud.google.com/sdk/docs/quickstarts). Both `gcloud` and `kubectl` are available locally. Then you can start by writing a Dockerfile which describes the running environment. An example Dockerfile looks like this: + +```dockerfile +# example.docker +FROM python:3.6-buster +ADD examples/pi_estimation.py /root/pi_estimation.py +RUN pip install fiber +``` +**Build an image and launch your job** + +``` +fiber run -a python3 /root/pi_estimation.py +``` + +This command will look for local Dockerfile and build a docker image and push it to your Google Container Registry . It then launches the main job which contains your code and runs the command `python3 /root/pi_estimation.py` inside your job. Once the main job is running, it will start 4 subsequent jobs on the cluster and each of them is a Pool worker. + + +## Supported platforms + +* Operating system: Linux +* Python: 3.6+ +* Supported cluster management systems: + * Kubernetes (Tested with Google Kubernetes Engine on Google cloud) + +We are interested in supporting other cluster management systems like [Slurm](https://slurm.schedmd.com/), if you want to contribute to it please let us know. + + +Check [here](https://uber.github.io/fiber/platforms/) for details. + +## Documentation + +The documentation, including method/API references, can be found [here](https://uber.github.io/fiber/getting-started/). + + +## Testing + +Install test dependencies. You'll also need to make sure [docker](https://docs.docker.com/install/) is available on the testing machine. + +```bash +$ pip install -e .[test] +``` + +Run tests + +```bash +$ make test +``` + +## Contributing +Please read our [code of conduct](CODE_OF_CONDUCT.md) before you contribute! You can find details for submitting pull requests in the [CONTRIBUTING.md](CONTRIBUTING.md) file. Issue [template](https://help.github.com/articles/about-issue-and-pull-request-templates/). + +## Versioning +We document versions and changes in our changelog - see the [CHANGELOG.md](CHANGELOG.md) file for details. + +## License +This project is licensed under the Apache 2.0 License - see the [LICENSE](LICENSE) file for details. + +## Cite Fiber + +``` +@misc{zhi2020fiber, + title={Fiber: A Platform for Efficient Development and Distributed Training for Reinforcement Learning and Population-Based Methods}, + author={Jiale Zhi and Rui Wang and Jeff Clune and Kenneth O. Stanley}, + year={2020}, + eprint={2003.11164}, + archivePrefix={arXiv}, + primaryClass={cs.LG} +} +``` + +## Acknowledgments +* Special thanks to Piero Molino for designing the logo for Fiber + +%prep +%autosetup -n fiber-0.2.1 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-fiber -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.1-1 +- Package Spec generated |