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diff --git a/python-pythagoras.spec b/python-pythagoras.spec new file mode 100644 index 0000000..ad6d533 --- /dev/null +++ b/python-pythagoras.spec @@ -0,0 +1,448 @@ +%global _empty_manifest_terminate_build 0 +Name: python-pythagoras +Version: 0.4.6 +Release: 1 +Summary: Simple and inexpensive way to create, deploy and run cloud-scale algorithms from within local Python scripts and notebooks. +License: MIT License +URL: https://github.com/vladlpavlov/pythagoras +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/34/8e/2b499c29071bf8275591045bf0f65f94653228138a426510ddc71c8dbb7e/pythagoras-0.4.6.tar.gz +BuildArch: noarch + +Requires: python3-numpy +Requires: python3-scipy +Requires: python3-pandas +Requires: python3-scikit-learn +Requires: python3-jsonpickle +Requires: python3-psutil +Requires: python3-boto3 +Requires: python3-moto +Requires: python3-pytest +Requires: python3-hypothesis + +%description +<div align="center"> + <img src="http://vlpavlov.org/Pythagoras-Logo3.svg"><br> +</div> + +# Pythagoras + +## (1) What Is It? + +An experimental framework that aims to democratize access to distributed serverless compute. +We make it simple and inexpensive to create, deploy and run cloud-hosted massively parallel algorithms +from within local Python scripts and notebooks. Pythagoras makes data scientists' lives easier, +while allowing them to solve more complex problems in a shorter time with smaller budgets. + +## (2) What Is Inside? +Pythagoras offers: +1. a powerful abstraction model for a global-scale serverless compute engine; +2. a simple API for Python programmers to use the engine; +3. a collection of backends that implement the API for various deployment scenarios +(*work in progress, currently only P2P backend is available*) +4. a collection of massively parallel algorithms that take advantage of +Pythagoras serverless compute engine (*planned*) + +### ((2.1)) Pythagoras Abstraction Model For Global Compute Engine + +Pythagoras offers a very powerful, yet simple abstraction model for a serverless compute engine. +It contains three fundamental components: +* Massively parallel serverless code executor for pure functions; +* Global persistent store for immutable values with hash-based addressing scheme; +* Global persistent cache for function execution outputs. + +This [overview](https://docs.google.com/document/d/1lgNOaRcZNGvW4wF894s7KmIWjhLX2cDVM_15a4lE02Y) +provides detailed information about the abstraction model. + +### ((2.2)) Pythagoras API + +Pythagoras makes it very easy to scale computationally expensive code to the cloud, +without a need to explicitly parallelize the code, to provision any infrastructure, +or to orchestrate code deployment or execution. + +Data scientists can continue using their local workstations. +They can keep working with their local Python scripts and Jupyter notebooks. +They will need to add just a few extra lines to their existing code. +After this change, computationally expensive parts of the local code will be +automatically executed in the cloud on dozens (or even hundreds or thousands) servers, +and the execution results will seamlessly get back to local workstations. + +This [introduction tutorial](https://github.com/vladlpavlov/pythagoras/blob/master/pythagoras_introduction.ipynb) +explains how to use the API. + +### ((2.3)) Pythagoras Backends +Backends are seamlessly interchangeable. +They differ by their under-the-hood implementations of deployment models and provisioning / orchestration algorithms. +A collection of backends will offer engineers a wide variety of trade-offs that fit different use-cases. + +As of now, Pythagoras team has implemented a +[P2P version](https://github.com/vladlpavlov/pythagoras/blob/master/pythagoras_P2P_tutorial.ipynb) +of the backend. It allows to parallelize program execution by simply launching the program simultaneously +on a swarm of local computers (e.g. desktops and laptops in your office or dormitory). + +The Pythagoras team is currently working on a reference implementation for AWS-based backend. +Over time, we anticipate to have dozens of alternative backends. +Please, reach to [Volodymyr (Vlad) Pavlov](https://www.linkedin.com/in/vlpavlov/) +if you want to help to create one. + +## (3) Roadmap + +Pythagoras has just started and is actively evolving. Here is our roadmap for 2022: + +1. Develop an abstraction model and API for the Global Compute Engine - **DONE**; +2. Develop several alternative backend implementations: + 1. P2P reference backend implementation - **DONE**; + 2. Basic AWS reference backend implementation - **IN PROGRESS**; + 3. Basic GCP backend implementation; + 4. Basic Azure backend implementation; + 5. Various advanced backend implementations for specific use-cases; +3. Implement massively parallel algorithms for Data Science and Machine Learning: + 1. Reference implementation for massively parallel grid search for hyperparameter optimisation - **IN PROGRESS**; + 2. Massively parallel implementations of popular DS/ML algorithms + that can benefit from highly scalable serverless compute; + 3. Brand-new DS/ML algorithms that were impossible to ideate in pre-serverless era. + +## (4) How To Get It? + +The source code is hosted on GitHub at: +[https://github.com/vladlpavlov/pythagoras](https://github.com/vladlpavlov/pythagoras) + +Binary installers for the latest released version are available at the Python package index at: +[https://pypi.org/project/pythagoras](https://pypi.org/project/pythagoras) + + pip install pythagoras + +## (5) Dependencies + +* [pandas](https://pandas.pydata.org) +* [scikit-learn](https://scikit-learn.org) +* [numpy](https://numpy.org) +* [scipy](https://www.scipy.org) +* [psutil](https://pypi.org/project/psutil) +* [boto3](https://boto3.readthedocs.io) +* [moto](http://getmoto.org) +* [jsonpickle](https://jsonpickle.github.io) +* [pytest](https://pytest.org) +* [hypothesis](https://hypothesis.works) + + +## (6) Key Contacts + +* [Volodymyr (Vlad) Pavlov](https://www.linkedin.com/in/vlpavlov/) +* [Illia Shestakov](https://www.linkedin.com/in/illia-shestakov-88716a21b/) +* [Kai Zhao](https://www.linkedin.com/in/kaimzhao/) + +## (7) What Do The Name And Logo Mean? + +Pythagoras was a famous ancient Greek thinker and scientist +who was the first man to call himself a philosopher ("lover of wisdom"). +He is most recognised for his many mathematical findings, +including the Pythagorean theorem. + +But not everyone knows that in antiquity, Pythagoras was also credited with major astronomical discoveries, +such as sphericity of the Earth and the identity of the morning and evening stars as the planet Venus. +Our logo depicts these, lesser known but not less important achievements of Pythagoras. + + + +%package -n python3-pythagoras +Summary: Simple and inexpensive way to create, deploy and run cloud-scale algorithms from within local Python scripts and notebooks. +Provides: python-pythagoras +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-pythagoras +<div align="center"> + <img src="http://vlpavlov.org/Pythagoras-Logo3.svg"><br> +</div> + +# Pythagoras + +## (1) What Is It? + +An experimental framework that aims to democratize access to distributed serverless compute. +We make it simple and inexpensive to create, deploy and run cloud-hosted massively parallel algorithms +from within local Python scripts and notebooks. Pythagoras makes data scientists' lives easier, +while allowing them to solve more complex problems in a shorter time with smaller budgets. + +## (2) What Is Inside? +Pythagoras offers: +1. a powerful abstraction model for a global-scale serverless compute engine; +2. a simple API for Python programmers to use the engine; +3. a collection of backends that implement the API for various deployment scenarios +(*work in progress, currently only P2P backend is available*) +4. a collection of massively parallel algorithms that take advantage of +Pythagoras serverless compute engine (*planned*) + +### ((2.1)) Pythagoras Abstraction Model For Global Compute Engine + +Pythagoras offers a very powerful, yet simple abstraction model for a serverless compute engine. +It contains three fundamental components: +* Massively parallel serverless code executor for pure functions; +* Global persistent store for immutable values with hash-based addressing scheme; +* Global persistent cache for function execution outputs. + +This [overview](https://docs.google.com/document/d/1lgNOaRcZNGvW4wF894s7KmIWjhLX2cDVM_15a4lE02Y) +provides detailed information about the abstraction model. + +### ((2.2)) Pythagoras API + +Pythagoras makes it very easy to scale computationally expensive code to the cloud, +without a need to explicitly parallelize the code, to provision any infrastructure, +or to orchestrate code deployment or execution. + +Data scientists can continue using their local workstations. +They can keep working with their local Python scripts and Jupyter notebooks. +They will need to add just a few extra lines to their existing code. +After this change, computationally expensive parts of the local code will be +automatically executed in the cloud on dozens (or even hundreds or thousands) servers, +and the execution results will seamlessly get back to local workstations. + +This [introduction tutorial](https://github.com/vladlpavlov/pythagoras/blob/master/pythagoras_introduction.ipynb) +explains how to use the API. + +### ((2.3)) Pythagoras Backends +Backends are seamlessly interchangeable. +They differ by their under-the-hood implementations of deployment models and provisioning / orchestration algorithms. +A collection of backends will offer engineers a wide variety of trade-offs that fit different use-cases. + +As of now, Pythagoras team has implemented a +[P2P version](https://github.com/vladlpavlov/pythagoras/blob/master/pythagoras_P2P_tutorial.ipynb) +of the backend. It allows to parallelize program execution by simply launching the program simultaneously +on a swarm of local computers (e.g. desktops and laptops in your office or dormitory). + +The Pythagoras team is currently working on a reference implementation for AWS-based backend. +Over time, we anticipate to have dozens of alternative backends. +Please, reach to [Volodymyr (Vlad) Pavlov](https://www.linkedin.com/in/vlpavlov/) +if you want to help to create one. + +## (3) Roadmap + +Pythagoras has just started and is actively evolving. Here is our roadmap for 2022: + +1. Develop an abstraction model and API for the Global Compute Engine - **DONE**; +2. Develop several alternative backend implementations: + 1. P2P reference backend implementation - **DONE**; + 2. Basic AWS reference backend implementation - **IN PROGRESS**; + 3. Basic GCP backend implementation; + 4. Basic Azure backend implementation; + 5. Various advanced backend implementations for specific use-cases; +3. Implement massively parallel algorithms for Data Science and Machine Learning: + 1. Reference implementation for massively parallel grid search for hyperparameter optimisation - **IN PROGRESS**; + 2. Massively parallel implementations of popular DS/ML algorithms + that can benefit from highly scalable serverless compute; + 3. Brand-new DS/ML algorithms that were impossible to ideate in pre-serverless era. + +## (4) How To Get It? + +The source code is hosted on GitHub at: +[https://github.com/vladlpavlov/pythagoras](https://github.com/vladlpavlov/pythagoras) + +Binary installers for the latest released version are available at the Python package index at: +[https://pypi.org/project/pythagoras](https://pypi.org/project/pythagoras) + + pip install pythagoras + +## (5) Dependencies + +* [pandas](https://pandas.pydata.org) +* [scikit-learn](https://scikit-learn.org) +* [numpy](https://numpy.org) +* [scipy](https://www.scipy.org) +* [psutil](https://pypi.org/project/psutil) +* [boto3](https://boto3.readthedocs.io) +* [moto](http://getmoto.org) +* [jsonpickle](https://jsonpickle.github.io) +* [pytest](https://pytest.org) +* [hypothesis](https://hypothesis.works) + + +## (6) Key Contacts + +* [Volodymyr (Vlad) Pavlov](https://www.linkedin.com/in/vlpavlov/) +* [Illia Shestakov](https://www.linkedin.com/in/illia-shestakov-88716a21b/) +* [Kai Zhao](https://www.linkedin.com/in/kaimzhao/) + +## (7) What Do The Name And Logo Mean? + +Pythagoras was a famous ancient Greek thinker and scientist +who was the first man to call himself a philosopher ("lover of wisdom"). +He is most recognised for his many mathematical findings, +including the Pythagorean theorem. + +But not everyone knows that in antiquity, Pythagoras was also credited with major astronomical discoveries, +such as sphericity of the Earth and the identity of the morning and evening stars as the planet Venus. +Our logo depicts these, lesser known but not less important achievements of Pythagoras. + + + +%package help +Summary: Development documents and examples for pythagoras +Provides: python3-pythagoras-doc +%description help +<div align="center"> + <img src="http://vlpavlov.org/Pythagoras-Logo3.svg"><br> +</div> + +# Pythagoras + +## (1) What Is It? + +An experimental framework that aims to democratize access to distributed serverless compute. +We make it simple and inexpensive to create, deploy and run cloud-hosted massively parallel algorithms +from within local Python scripts and notebooks. Pythagoras makes data scientists' lives easier, +while allowing them to solve more complex problems in a shorter time with smaller budgets. + +## (2) What Is Inside? +Pythagoras offers: +1. a powerful abstraction model for a global-scale serverless compute engine; +2. a simple API for Python programmers to use the engine; +3. a collection of backends that implement the API for various deployment scenarios +(*work in progress, currently only P2P backend is available*) +4. a collection of massively parallel algorithms that take advantage of +Pythagoras serverless compute engine (*planned*) + +### ((2.1)) Pythagoras Abstraction Model For Global Compute Engine + +Pythagoras offers a very powerful, yet simple abstraction model for a serverless compute engine. +It contains three fundamental components: +* Massively parallel serverless code executor for pure functions; +* Global persistent store for immutable values with hash-based addressing scheme; +* Global persistent cache for function execution outputs. + +This [overview](https://docs.google.com/document/d/1lgNOaRcZNGvW4wF894s7KmIWjhLX2cDVM_15a4lE02Y) +provides detailed information about the abstraction model. + +### ((2.2)) Pythagoras API + +Pythagoras makes it very easy to scale computationally expensive code to the cloud, +without a need to explicitly parallelize the code, to provision any infrastructure, +or to orchestrate code deployment or execution. + +Data scientists can continue using their local workstations. +They can keep working with their local Python scripts and Jupyter notebooks. +They will need to add just a few extra lines to their existing code. +After this change, computationally expensive parts of the local code will be +automatically executed in the cloud on dozens (or even hundreds or thousands) servers, +and the execution results will seamlessly get back to local workstations. + +This [introduction tutorial](https://github.com/vladlpavlov/pythagoras/blob/master/pythagoras_introduction.ipynb) +explains how to use the API. + +### ((2.3)) Pythagoras Backends +Backends are seamlessly interchangeable. +They differ by their under-the-hood implementations of deployment models and provisioning / orchestration algorithms. +A collection of backends will offer engineers a wide variety of trade-offs that fit different use-cases. + +As of now, Pythagoras team has implemented a +[P2P version](https://github.com/vladlpavlov/pythagoras/blob/master/pythagoras_P2P_tutorial.ipynb) +of the backend. It allows to parallelize program execution by simply launching the program simultaneously +on a swarm of local computers (e.g. desktops and laptops in your office or dormitory). + +The Pythagoras team is currently working on a reference implementation for AWS-based backend. +Over time, we anticipate to have dozens of alternative backends. +Please, reach to [Volodymyr (Vlad) Pavlov](https://www.linkedin.com/in/vlpavlov/) +if you want to help to create one. + +## (3) Roadmap + +Pythagoras has just started and is actively evolving. Here is our roadmap for 2022: + +1. Develop an abstraction model and API for the Global Compute Engine - **DONE**; +2. Develop several alternative backend implementations: + 1. P2P reference backend implementation - **DONE**; + 2. Basic AWS reference backend implementation - **IN PROGRESS**; + 3. Basic GCP backend implementation; + 4. Basic Azure backend implementation; + 5. Various advanced backend implementations for specific use-cases; +3. Implement massively parallel algorithms for Data Science and Machine Learning: + 1. Reference implementation for massively parallel grid search for hyperparameter optimisation - **IN PROGRESS**; + 2. Massively parallel implementations of popular DS/ML algorithms + that can benefit from highly scalable serverless compute; + 3. Brand-new DS/ML algorithms that were impossible to ideate in pre-serverless era. + +## (4) How To Get It? + +The source code is hosted on GitHub at: +[https://github.com/vladlpavlov/pythagoras](https://github.com/vladlpavlov/pythagoras) + +Binary installers for the latest released version are available at the Python package index at: +[https://pypi.org/project/pythagoras](https://pypi.org/project/pythagoras) + + pip install pythagoras + +## (5) Dependencies + +* [pandas](https://pandas.pydata.org) +* [scikit-learn](https://scikit-learn.org) +* [numpy](https://numpy.org) +* [scipy](https://www.scipy.org) +* [psutil](https://pypi.org/project/psutil) +* [boto3](https://boto3.readthedocs.io) +* [moto](http://getmoto.org) +* [jsonpickle](https://jsonpickle.github.io) +* [pytest](https://pytest.org) +* [hypothesis](https://hypothesis.works) + + +## (6) Key Contacts + +* [Volodymyr (Vlad) Pavlov](https://www.linkedin.com/in/vlpavlov/) +* [Illia Shestakov](https://www.linkedin.com/in/illia-shestakov-88716a21b/) +* [Kai Zhao](https://www.linkedin.com/in/kaimzhao/) + +## (7) What Do The Name And Logo Mean? + +Pythagoras was a famous ancient Greek thinker and scientist +who was the first man to call himself a philosopher ("lover of wisdom"). +He is most recognised for his many mathematical findings, +including the Pythagorean theorem. + +But not everyone knows that in antiquity, Pythagoras was also credited with major astronomical discoveries, +such as sphericity of the Earth and the identity of the morning and evening stars as the planet Venus. +Our logo depicts these, lesser known but not less important achievements of Pythagoras. + + + +%prep +%autosetup -n pythagoras-0.4.6 + +%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-pythagoras -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Thu May 18 2023 Python_Bot <Python_Bot@openeuler.org> - 0.4.6-1 +- Package Spec generated |