%global _empty_manifest_terminate_build 0 Name: python-kxy Version: 1.4.11 Release: 1 Summary: A Powerful Serverless Pre-Learning and Post-Learning Analysis Toolkit License: GPLv3 URL: https://www.kxy.ai Source0: https://mirrors.nju.edu.cn/pypi/web/packages/43/d1/a8ce29122712ba3edd8cfabcbd2a0f30bfb3534b4f406f5489fa5c982a4b/kxy-1.4.11.tar.gz BuildArch: noarch Requires: python3-numpy Requires: python3-scipy Requires: python3-pandas Requires: python3-requests Requires: python3-pandarallel Requires: python3-halo Requires: python3-ipywidgets Requires: python3-scikit-learn %description # Boosting The Productivity of Machine Learning Engineers [![License](https://img.shields.io/badge/license-GPLv3%2B-blue)](https://github.com/kxytechnologies/kxy-python/blob/master/LICENSE) [![PyPI Latest Release](https://img.shields.io/pypi/v/kxy.svg)](https://www.kxy.ai/) [![Downloads](https://pepy.tech/badge/kxy)](https://www.kxy.ai/) ## Documentation https://www.kxy.ai/reference/ ## Blog https://blog.kxy.ai ## Installation From PyPi: ```Bash pip install kxy -U ``` From GitHub: ```Bash git clone https://github.com/kxytechnologies/kxy-python.git & cd ./kxy-python & pip install . ``` ## Authentication All heavy-duty computations are run on our serverless infrastructure and require an API key. To configure the package with your API key, run ```Bash kxy configure ``` and follow the instructions. To get your own API key you need an account; you can sign up [here](https://www.kxy.ai/signup/). You'll then be automatically given an API key which you can find [here](https://www.kxy.ai/portal/profile/identity/). ## Docker The Docker image [kxytechnologies/kxy](https://hub.docker.com/repository/docker/kxytechnologies/kxy) has been built for your convenience, and comes with anaconda, auto-sklearn, and the kxy package. To start a Jupyter Notebook server from a sandboxed Docker environment, run ```Bash docker run -i -t -p 5555:8888 kxytechnologies/kxy:latest /bin/bash -c "kxy configure && /opt/conda/bin/jupyter notebook --notebook-dir=/opt/notebooks --ip='*' --port=8888 --no-browser --allow-root --NotebookApp.token=''" ``` where you should replace `` with your API key and navigate to [http://localhost:5555](http://localhost:5555) in your browser. This docker environment comes with [all examples available on the documentation website](https://www.kxy.ai/reference/latest/examples/). To start a Jupyter Notebook server from an existing directory of notebooks, run ```Bash docker run -i -t --mount src=,target=/opt/notebooks,type=bind -p 5555:8888 kxytechnologies/kxy:latest /bin/bash -c "kxy configure && /opt/conda/bin/jupyter notebook --notebook-dir=/opt/notebooks --ip='*' --port=8888 --no-browser --allow-root --NotebookApp.token=''" ``` where you should replace `` with the path to your local notebook folder and navigate to [http://localhost:5555](http://localhost:5555) in your browser. You can also get the same Docker image from GitHub [here](https://github.com/kxytechnologies/kxy-python/pkgs/container/kxy-python). ## Other Programming Language We plan to release friendly API client in more programming language. In the meantime, you can directly issue requests to our [RESTFul API](https://www.kxy.ai/reference/latest/api/index.html) using your favorite programming language. ## Pricing All API keys are given a free quota (a few dozen backend tasks) that should be enough to try out the package and see if you love it. Beyond the free quota you will be billed a small fee per task. KXY is free for academic use; simply signup with your university email. KXY is also free for Kaggle competitions; sign up and email kaggle@kxy.ai to get a promotional code. %package -n python3-kxy Summary: A Powerful Serverless Pre-Learning and Post-Learning Analysis Toolkit Provides: python-kxy BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-kxy # Boosting The Productivity of Machine Learning Engineers [![License](https://img.shields.io/badge/license-GPLv3%2B-blue)](https://github.com/kxytechnologies/kxy-python/blob/master/LICENSE) [![PyPI Latest Release](https://img.shields.io/pypi/v/kxy.svg)](https://www.kxy.ai/) [![Downloads](https://pepy.tech/badge/kxy)](https://www.kxy.ai/) ## Documentation https://www.kxy.ai/reference/ ## Blog https://blog.kxy.ai ## Installation From PyPi: ```Bash pip install kxy -U ``` From GitHub: ```Bash git clone https://github.com/kxytechnologies/kxy-python.git & cd ./kxy-python & pip install . ``` ## Authentication All heavy-duty computations are run on our serverless infrastructure and require an API key. To configure the package with your API key, run ```Bash kxy configure ``` and follow the instructions. To get your own API key you need an account; you can sign up [here](https://www.kxy.ai/signup/). You'll then be automatically given an API key which you can find [here](https://www.kxy.ai/portal/profile/identity/). ## Docker The Docker image [kxytechnologies/kxy](https://hub.docker.com/repository/docker/kxytechnologies/kxy) has been built for your convenience, and comes with anaconda, auto-sklearn, and the kxy package. To start a Jupyter Notebook server from a sandboxed Docker environment, run ```Bash docker run -i -t -p 5555:8888 kxytechnologies/kxy:latest /bin/bash -c "kxy configure && /opt/conda/bin/jupyter notebook --notebook-dir=/opt/notebooks --ip='*' --port=8888 --no-browser --allow-root --NotebookApp.token=''" ``` where you should replace `` with your API key and navigate to [http://localhost:5555](http://localhost:5555) in your browser. This docker environment comes with [all examples available on the documentation website](https://www.kxy.ai/reference/latest/examples/). To start a Jupyter Notebook server from an existing directory of notebooks, run ```Bash docker run -i -t --mount src=,target=/opt/notebooks,type=bind -p 5555:8888 kxytechnologies/kxy:latest /bin/bash -c "kxy configure && /opt/conda/bin/jupyter notebook --notebook-dir=/opt/notebooks --ip='*' --port=8888 --no-browser --allow-root --NotebookApp.token=''" ``` where you should replace `` with the path to your local notebook folder and navigate to [http://localhost:5555](http://localhost:5555) in your browser. You can also get the same Docker image from GitHub [here](https://github.com/kxytechnologies/kxy-python/pkgs/container/kxy-python). ## Other Programming Language We plan to release friendly API client in more programming language. In the meantime, you can directly issue requests to our [RESTFul API](https://www.kxy.ai/reference/latest/api/index.html) using your favorite programming language. ## Pricing All API keys are given a free quota (a few dozen backend tasks) that should be enough to try out the package and see if you love it. Beyond the free quota you will be billed a small fee per task. KXY is free for academic use; simply signup with your university email. KXY is also free for Kaggle competitions; sign up and email kaggle@kxy.ai to get a promotional code. %package help Summary: Development documents and examples for kxy Provides: python3-kxy-doc %description help # Boosting The Productivity of Machine Learning Engineers [![License](https://img.shields.io/badge/license-GPLv3%2B-blue)](https://github.com/kxytechnologies/kxy-python/blob/master/LICENSE) [![PyPI Latest Release](https://img.shields.io/pypi/v/kxy.svg)](https://www.kxy.ai/) [![Downloads](https://pepy.tech/badge/kxy)](https://www.kxy.ai/) ## Documentation https://www.kxy.ai/reference/ ## Blog https://blog.kxy.ai ## Installation From PyPi: ```Bash pip install kxy -U ``` From GitHub: ```Bash git clone https://github.com/kxytechnologies/kxy-python.git & cd ./kxy-python & pip install . ``` ## Authentication All heavy-duty computations are run on our serverless infrastructure and require an API key. To configure the package with your API key, run ```Bash kxy configure ``` and follow the instructions. To get your own API key you need an account; you can sign up [here](https://www.kxy.ai/signup/). You'll then be automatically given an API key which you can find [here](https://www.kxy.ai/portal/profile/identity/). ## Docker The Docker image [kxytechnologies/kxy](https://hub.docker.com/repository/docker/kxytechnologies/kxy) has been built for your convenience, and comes with anaconda, auto-sklearn, and the kxy package. To start a Jupyter Notebook server from a sandboxed Docker environment, run ```Bash docker run -i -t -p 5555:8888 kxytechnologies/kxy:latest /bin/bash -c "kxy configure && /opt/conda/bin/jupyter notebook --notebook-dir=/opt/notebooks --ip='*' --port=8888 --no-browser --allow-root --NotebookApp.token=''" ``` where you should replace `` with your API key and navigate to [http://localhost:5555](http://localhost:5555) in your browser. This docker environment comes with [all examples available on the documentation website](https://www.kxy.ai/reference/latest/examples/). To start a Jupyter Notebook server from an existing directory of notebooks, run ```Bash docker run -i -t --mount src=,target=/opt/notebooks,type=bind -p 5555:8888 kxytechnologies/kxy:latest /bin/bash -c "kxy configure && /opt/conda/bin/jupyter notebook --notebook-dir=/opt/notebooks --ip='*' --port=8888 --no-browser --allow-root --NotebookApp.token=''" ``` where you should replace `` with the path to your local notebook folder and navigate to [http://localhost:5555](http://localhost:5555) in your browser. You can also get the same Docker image from GitHub [here](https://github.com/kxytechnologies/kxy-python/pkgs/container/kxy-python). ## Other Programming Language We plan to release friendly API client in more programming language. In the meantime, you can directly issue requests to our [RESTFul API](https://www.kxy.ai/reference/latest/api/index.html) using your favorite programming language. ## Pricing All API keys are given a free quota (a few dozen backend tasks) that should be enough to try out the package and see if you love it. Beyond the free quota you will be billed a small fee per task. KXY is free for academic use; simply signup with your university email. KXY is also free for Kaggle competitions; sign up and email kaggle@kxy.ai to get a promotional code. %prep %autosetup -n kxy-1.4.11 %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-kxy -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri May 05 2023 Python_Bot - 1.4.11-1 - Package Spec generated