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|
%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
[](https://github.com/kxytechnologies/kxy-python/blob/master/LICENSE)
[](https://www.kxy.ai/)
[](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 <YOUR API KEY> && /opt/conda/bin/jupyter notebook --notebook-dir=/opt/notebooks --ip='*' --port=8888 --no-browser --allow-root --NotebookApp.token=''"
```
where you should replace `<YOUR API KEY>` 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=</path/to/your/local/dir>,target=/opt/notebooks,type=bind -p 5555:8888 kxytechnologies/kxy:latest /bin/bash -c "kxy configure <YOUR API KEY> && /opt/conda/bin/jupyter notebook --notebook-dir=/opt/notebooks --ip='*' --port=8888 --no-browser --allow-root --NotebookApp.token=''"
```
where you should replace `</path/to/your/local/dir>` 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
[](https://github.com/kxytechnologies/kxy-python/blob/master/LICENSE)
[](https://www.kxy.ai/)
[](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 <YOUR API KEY> && /opt/conda/bin/jupyter notebook --notebook-dir=/opt/notebooks --ip='*' --port=8888 --no-browser --allow-root --NotebookApp.token=''"
```
where you should replace `<YOUR API KEY>` 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=</path/to/your/local/dir>,target=/opt/notebooks,type=bind -p 5555:8888 kxytechnologies/kxy:latest /bin/bash -c "kxy configure <YOUR API KEY> && /opt/conda/bin/jupyter notebook --notebook-dir=/opt/notebooks --ip='*' --port=8888 --no-browser --allow-root --NotebookApp.token=''"
```
where you should replace `</path/to/your/local/dir>` 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
[](https://github.com/kxytechnologies/kxy-python/blob/master/LICENSE)
[](https://www.kxy.ai/)
[](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 <YOUR API KEY> && /opt/conda/bin/jupyter notebook --notebook-dir=/opt/notebooks --ip='*' --port=8888 --no-browser --allow-root --NotebookApp.token=''"
```
where you should replace `<YOUR API KEY>` 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=</path/to/your/local/dir>,target=/opt/notebooks,type=bind -p 5555:8888 kxytechnologies/kxy:latest /bin/bash -c "kxy configure <YOUR API KEY> && /opt/conda/bin/jupyter notebook --notebook-dir=/opt/notebooks --ip='*' --port=8888 --no-browser --allow-root --NotebookApp.token=''"
```
where you should replace `</path/to/your/local/dir>` 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 <Python_Bot@openeuler.org> - 1.4.11-1
- Package Spec generated
|
