%global _empty_manifest_terminate_build 0
Name:		python-gpytorch
Version:	1.10
Release:	1
Summary:	An implementation of Gaussian Processes in Pytorch
License:	MIT
URL:		https://gpytorch.ai
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/b4/be/bb6898d9a31f5daa3c0a18f613e87d1970f0cf546cbf5925b3eb908be036/gpytorch-1.10.tar.gz
BuildArch:	noarch

Requires:	python3-scikit-learn
Requires:	python3-linear-operator
Requires:	python3-ufmt
Requires:	python3-twine
Requires:	python3-pre-commit
Requires:	python3-ipython
Requires:	python3-jupyter
Requires:	python3-matplotlib
Requires:	python3-scipy
Requires:	python3-torchvision
Requires:	python3-tqdm
Requires:	python3-pykeops
Requires:	python3-pyro-ppl
Requires:	python3-flake8
Requires:	python3-flake8-print
Requires:	python3-pytest
Requires:	python3-nbval

%description
[![Test Suite](https://github.com/cornellius-gp/gpytorch/actions/workflows/run_test_suite.yml/badge.svg)](https://github.com/cornellius-gp/gpytorch/actions/workflows/run_test_suite.yml)
[![Documentation Status](https://readthedocs.org/projects/gpytorch/badge/?version=latest)](https://gpytorch.readthedocs.io/en/latest/?badge=latest)
[![License](https://img.shields.io/badge/license-MIT-green.svg)](LICENSE)
[![Python Version](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)
[![Conda](https://img.shields.io/conda/v/gpytorch/gpytorch.svg)](https://anaconda.org/gpytorch/gpytorch)
[![PyPI](https://img.shields.io/pypi/v/gpytorch.svg)](https://pypi.org/project/gpytorch)
GPyTorch is a Gaussian process library implemented using PyTorch. GPyTorch is designed for creating scalable, flexible, and modular Gaussian process models with ease.
Internally, GPyTorch differs from many existing approaches to GP inference by performing most inference operations using numerical linear algebra techniques like preconditioned conjugate gradients.
Implementing a scalable GP method is as simple as providing a matrix multiplication routine with the kernel matrix and its derivative via our [LinearOperator](https://github.com/cornellius-gp/linear_operator) interface,
or by composing many of our already existing `LinearOperators`.
This allows not only for easy implementation of popular scalable GP techniques,
but often also for significantly improved utilization of GPU computing compared to solvers based on the Cholesky decomposition.
GPyTorch provides (1) significant GPU acceleration (through MVM based inference);
(2) state-of-the-art implementations of the latest algorithmic advances for scalability and flexibility ([SKI/KISS-GP](http://proceedings.mlr.press/v37/wilson15.pdf), [stochastic Lanczos expansions](https://arxiv.org/abs/1711.03481), [LOVE](https://arxiv.org/pdf/1803.06058.pdf), [SKIP](https://arxiv.org/pdf/1802.08903.pdf), [stochastic variational](https://arxiv.org/pdf/1611.00336.pdf) [deep kernel learning](http://proceedings.mlr.press/v51/wilson16.pdf), ...);
(3) easy integration with deep learning frameworks.
## Examples, Tutorials, and Documentation
See our [**documentation, examples, tutorials**](https://gpytorch.readthedocs.io/en/latest/) on how to construct all sorts of models in GPyTorch.
## Installation
**Requirements**:
- Python >= 3.8
- PyTorch >= 1.11
Install GPyTorch using pip or conda:
```bash
pip install gpytorch
conda install gpytorch -c gpytorch
```
(To use packages globally but install GPyTorch as a user-only package, use `pip install --user` above.)
#### Latest (Unstable) Version
To upgrade to the latest (unstable) version, run
```bash
pip install --upgrade git+https://github.com/cornellius-gp/linear_operator.git
pip install --upgrade git+https://github.com/cornellius-gp/gpytorch.git
```
#### Development version
If you are contributing a pull request, it is best to perform a manual installation:
```sh
git clone https://github.com/cornellius-gp/gpytorch.git
cd gpytorch
pip install -e .[dev,examples,test,pyro,keops]
```
To generate the documentation locally, you will also need to run the following command
from the linear_operator folder:
```sh
pip install -r docs/requirements.txt
```
#### ArchLinux Package
**Note**: Experimental AUR package. For most users, we recommend installation by conda or pip.
GPyTorch is also available on the [ArchLinux User Repository](https://wiki.archlinux.org/index.php/Arch_User_Repository) (AUR).
You can install it with an [AUR helper](https://wiki.archlinux.org/index.php/AUR_helpers), like [`yay`](https://aur.archlinux.org/packages/yay/), as follows:
```bash
yay -S python-gpytorch
```
To discuss any issues related to this AUR package refer to the comments section of
[`python-gpytorch`](https://aur.archlinux.org/packages/python-gpytorch/).
## Citing Us
If you use GPyTorch, please cite the following papers:
> [Gardner, Jacob R., Geoff Pleiss, David Bindel, Kilian Q. Weinberger, and Andrew Gordon Wilson. "GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration." In Advances in Neural Information Processing Systems (2018).](https://arxiv.org/abs/1809.11165)
```
@inproceedings{gardner2018gpytorch,
  title={GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration},
  author={Gardner, Jacob R and Pleiss, Geoff and Bindel, David and Weinberger, Kilian Q and Wilson, Andrew Gordon},
  booktitle={Advances in Neural Information Processing Systems},
  year={2018}
}
```
## Contributing
See the contributing guidelines [CONTRIBUTING.md](https://github.com/cornellius-gp/gpytorch/blob/master/CONTRIBUTING.md)
for information on submitting issues and pull requests.
## The Team
GPyTorch is primarily maintained by:
- [Jake Gardner](https://www.cis.upenn.edu/~jacobrg/index.html) (University of Pennsylvania)
- [Geoff Pleiss](http://github.com/gpleiss) (Columbia University)
- [Kilian Weinberger](http://kilian.cs.cornell.edu/) (Cornell University)
- [Andrew Gordon Wilson](https://cims.nyu.edu/~andrewgw/) (New York University)
- [Max Balandat](https://research.fb.com/people/balandat-max/) (Meta)
We would like to thank our other contributors including (but not limited to)
Eytan Bakshy,
Wesley Maddox,
Ke Alexander Wang,
Ruihan Wu,
Sait Cakmak,
David Eriksson,
Sam Daulton,
Martin Jankowiak,
Sam Stanton,
Zitong Zhou,
David Arbour,
Karthik Rajkumar,
Bram Wallace,
Jared Frank,
and many more!
## Acknowledgements
Development of GPyTorch is supported by funding from
the [Bill and Melinda Gates Foundation](https://www.gatesfoundation.org/),
the [National Science Foundation](https://www.nsf.gov/),
[SAP](https://www.sap.com/index.html),
the [Simons Foundation](https://www.simonsfoundation.org),
and the [Gatsby Charitable Trust](https://www.gatsby.org.uk).
## License
GPyTorch is [MIT licensed](https://github.com/cornellius-gp/gpytorch/blob/main/LICENSE).

%package -n python3-gpytorch
Summary:	An implementation of Gaussian Processes in Pytorch
Provides:	python-gpytorch
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-gpytorch
[![Test Suite](https://github.com/cornellius-gp/gpytorch/actions/workflows/run_test_suite.yml/badge.svg)](https://github.com/cornellius-gp/gpytorch/actions/workflows/run_test_suite.yml)
[![Documentation Status](https://readthedocs.org/projects/gpytorch/badge/?version=latest)](https://gpytorch.readthedocs.io/en/latest/?badge=latest)
[![License](https://img.shields.io/badge/license-MIT-green.svg)](LICENSE)
[![Python Version](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)
[![Conda](https://img.shields.io/conda/v/gpytorch/gpytorch.svg)](https://anaconda.org/gpytorch/gpytorch)
[![PyPI](https://img.shields.io/pypi/v/gpytorch.svg)](https://pypi.org/project/gpytorch)
GPyTorch is a Gaussian process library implemented using PyTorch. GPyTorch is designed for creating scalable, flexible, and modular Gaussian process models with ease.
Internally, GPyTorch differs from many existing approaches to GP inference by performing most inference operations using numerical linear algebra techniques like preconditioned conjugate gradients.
Implementing a scalable GP method is as simple as providing a matrix multiplication routine with the kernel matrix and its derivative via our [LinearOperator](https://github.com/cornellius-gp/linear_operator) interface,
or by composing many of our already existing `LinearOperators`.
This allows not only for easy implementation of popular scalable GP techniques,
but often also for significantly improved utilization of GPU computing compared to solvers based on the Cholesky decomposition.
GPyTorch provides (1) significant GPU acceleration (through MVM based inference);
(2) state-of-the-art implementations of the latest algorithmic advances for scalability and flexibility ([SKI/KISS-GP](http://proceedings.mlr.press/v37/wilson15.pdf), [stochastic Lanczos expansions](https://arxiv.org/abs/1711.03481), [LOVE](https://arxiv.org/pdf/1803.06058.pdf), [SKIP](https://arxiv.org/pdf/1802.08903.pdf), [stochastic variational](https://arxiv.org/pdf/1611.00336.pdf) [deep kernel learning](http://proceedings.mlr.press/v51/wilson16.pdf), ...);
(3) easy integration with deep learning frameworks.
## Examples, Tutorials, and Documentation
See our [**documentation, examples, tutorials**](https://gpytorch.readthedocs.io/en/latest/) on how to construct all sorts of models in GPyTorch.
## Installation
**Requirements**:
- Python >= 3.8
- PyTorch >= 1.11
Install GPyTorch using pip or conda:
```bash
pip install gpytorch
conda install gpytorch -c gpytorch
```
(To use packages globally but install GPyTorch as a user-only package, use `pip install --user` above.)
#### Latest (Unstable) Version
To upgrade to the latest (unstable) version, run
```bash
pip install --upgrade git+https://github.com/cornellius-gp/linear_operator.git
pip install --upgrade git+https://github.com/cornellius-gp/gpytorch.git
```
#### Development version
If you are contributing a pull request, it is best to perform a manual installation:
```sh
git clone https://github.com/cornellius-gp/gpytorch.git
cd gpytorch
pip install -e .[dev,examples,test,pyro,keops]
```
To generate the documentation locally, you will also need to run the following command
from the linear_operator folder:
```sh
pip install -r docs/requirements.txt
```
#### ArchLinux Package
**Note**: Experimental AUR package. For most users, we recommend installation by conda or pip.
GPyTorch is also available on the [ArchLinux User Repository](https://wiki.archlinux.org/index.php/Arch_User_Repository) (AUR).
You can install it with an [AUR helper](https://wiki.archlinux.org/index.php/AUR_helpers), like [`yay`](https://aur.archlinux.org/packages/yay/), as follows:
```bash
yay -S python-gpytorch
```
To discuss any issues related to this AUR package refer to the comments section of
[`python-gpytorch`](https://aur.archlinux.org/packages/python-gpytorch/).
## Citing Us
If you use GPyTorch, please cite the following papers:
> [Gardner, Jacob R., Geoff Pleiss, David Bindel, Kilian Q. Weinberger, and Andrew Gordon Wilson. "GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration." In Advances in Neural Information Processing Systems (2018).](https://arxiv.org/abs/1809.11165)
```
@inproceedings{gardner2018gpytorch,
  title={GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration},
  author={Gardner, Jacob R and Pleiss, Geoff and Bindel, David and Weinberger, Kilian Q and Wilson, Andrew Gordon},
  booktitle={Advances in Neural Information Processing Systems},
  year={2018}
}
```
## Contributing
See the contributing guidelines [CONTRIBUTING.md](https://github.com/cornellius-gp/gpytorch/blob/master/CONTRIBUTING.md)
for information on submitting issues and pull requests.
## The Team
GPyTorch is primarily maintained by:
- [Jake Gardner](https://www.cis.upenn.edu/~jacobrg/index.html) (University of Pennsylvania)
- [Geoff Pleiss](http://github.com/gpleiss) (Columbia University)
- [Kilian Weinberger](http://kilian.cs.cornell.edu/) (Cornell University)
- [Andrew Gordon Wilson](https://cims.nyu.edu/~andrewgw/) (New York University)
- [Max Balandat](https://research.fb.com/people/balandat-max/) (Meta)
We would like to thank our other contributors including (but not limited to)
Eytan Bakshy,
Wesley Maddox,
Ke Alexander Wang,
Ruihan Wu,
Sait Cakmak,
David Eriksson,
Sam Daulton,
Martin Jankowiak,
Sam Stanton,
Zitong Zhou,
David Arbour,
Karthik Rajkumar,
Bram Wallace,
Jared Frank,
and many more!
## Acknowledgements
Development of GPyTorch is supported by funding from
the [Bill and Melinda Gates Foundation](https://www.gatesfoundation.org/),
the [National Science Foundation](https://www.nsf.gov/),
[SAP](https://www.sap.com/index.html),
the [Simons Foundation](https://www.simonsfoundation.org),
and the [Gatsby Charitable Trust](https://www.gatsby.org.uk).
## License
GPyTorch is [MIT licensed](https://github.com/cornellius-gp/gpytorch/blob/main/LICENSE).

%package help
Summary:	Development documents and examples for gpytorch
Provides:	python3-gpytorch-doc
%description help
[![Test Suite](https://github.com/cornellius-gp/gpytorch/actions/workflows/run_test_suite.yml/badge.svg)](https://github.com/cornellius-gp/gpytorch/actions/workflows/run_test_suite.yml)
[![Documentation Status](https://readthedocs.org/projects/gpytorch/badge/?version=latest)](https://gpytorch.readthedocs.io/en/latest/?badge=latest)
[![License](https://img.shields.io/badge/license-MIT-green.svg)](LICENSE)
[![Python Version](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)
[![Conda](https://img.shields.io/conda/v/gpytorch/gpytorch.svg)](https://anaconda.org/gpytorch/gpytorch)
[![PyPI](https://img.shields.io/pypi/v/gpytorch.svg)](https://pypi.org/project/gpytorch)
GPyTorch is a Gaussian process library implemented using PyTorch. GPyTorch is designed for creating scalable, flexible, and modular Gaussian process models with ease.
Internally, GPyTorch differs from many existing approaches to GP inference by performing most inference operations using numerical linear algebra techniques like preconditioned conjugate gradients.
Implementing a scalable GP method is as simple as providing a matrix multiplication routine with the kernel matrix and its derivative via our [LinearOperator](https://github.com/cornellius-gp/linear_operator) interface,
or by composing many of our already existing `LinearOperators`.
This allows not only for easy implementation of popular scalable GP techniques,
but often also for significantly improved utilization of GPU computing compared to solvers based on the Cholesky decomposition.
GPyTorch provides (1) significant GPU acceleration (through MVM based inference);
(2) state-of-the-art implementations of the latest algorithmic advances for scalability and flexibility ([SKI/KISS-GP](http://proceedings.mlr.press/v37/wilson15.pdf), [stochastic Lanczos expansions](https://arxiv.org/abs/1711.03481), [LOVE](https://arxiv.org/pdf/1803.06058.pdf), [SKIP](https://arxiv.org/pdf/1802.08903.pdf), [stochastic variational](https://arxiv.org/pdf/1611.00336.pdf) [deep kernel learning](http://proceedings.mlr.press/v51/wilson16.pdf), ...);
(3) easy integration with deep learning frameworks.
## Examples, Tutorials, and Documentation
See our [**documentation, examples, tutorials**](https://gpytorch.readthedocs.io/en/latest/) on how to construct all sorts of models in GPyTorch.
## Installation
**Requirements**:
- Python >= 3.8
- PyTorch >= 1.11
Install GPyTorch using pip or conda:
```bash
pip install gpytorch
conda install gpytorch -c gpytorch
```
(To use packages globally but install GPyTorch as a user-only package, use `pip install --user` above.)
#### Latest (Unstable) Version
To upgrade to the latest (unstable) version, run
```bash
pip install --upgrade git+https://github.com/cornellius-gp/linear_operator.git
pip install --upgrade git+https://github.com/cornellius-gp/gpytorch.git
```
#### Development version
If you are contributing a pull request, it is best to perform a manual installation:
```sh
git clone https://github.com/cornellius-gp/gpytorch.git
cd gpytorch
pip install -e .[dev,examples,test,pyro,keops]
```
To generate the documentation locally, you will also need to run the following command
from the linear_operator folder:
```sh
pip install -r docs/requirements.txt
```
#### ArchLinux Package
**Note**: Experimental AUR package. For most users, we recommend installation by conda or pip.
GPyTorch is also available on the [ArchLinux User Repository](https://wiki.archlinux.org/index.php/Arch_User_Repository) (AUR).
You can install it with an [AUR helper](https://wiki.archlinux.org/index.php/AUR_helpers), like [`yay`](https://aur.archlinux.org/packages/yay/), as follows:
```bash
yay -S python-gpytorch
```
To discuss any issues related to this AUR package refer to the comments section of
[`python-gpytorch`](https://aur.archlinux.org/packages/python-gpytorch/).
## Citing Us
If you use GPyTorch, please cite the following papers:
> [Gardner, Jacob R., Geoff Pleiss, David Bindel, Kilian Q. Weinberger, and Andrew Gordon Wilson. "GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration." In Advances in Neural Information Processing Systems (2018).](https://arxiv.org/abs/1809.11165)
```
@inproceedings{gardner2018gpytorch,
  title={GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration},
  author={Gardner, Jacob R and Pleiss, Geoff and Bindel, David and Weinberger, Kilian Q and Wilson, Andrew Gordon},
  booktitle={Advances in Neural Information Processing Systems},
  year={2018}
}
```
## Contributing
See the contributing guidelines [CONTRIBUTING.md](https://github.com/cornellius-gp/gpytorch/blob/master/CONTRIBUTING.md)
for information on submitting issues and pull requests.
## The Team
GPyTorch is primarily maintained by:
- [Jake Gardner](https://www.cis.upenn.edu/~jacobrg/index.html) (University of Pennsylvania)
- [Geoff Pleiss](http://github.com/gpleiss) (Columbia University)
- [Kilian Weinberger](http://kilian.cs.cornell.edu/) (Cornell University)
- [Andrew Gordon Wilson](https://cims.nyu.edu/~andrewgw/) (New York University)
- [Max Balandat](https://research.fb.com/people/balandat-max/) (Meta)
We would like to thank our other contributors including (but not limited to)
Eytan Bakshy,
Wesley Maddox,
Ke Alexander Wang,
Ruihan Wu,
Sait Cakmak,
David Eriksson,
Sam Daulton,
Martin Jankowiak,
Sam Stanton,
Zitong Zhou,
David Arbour,
Karthik Rajkumar,
Bram Wallace,
Jared Frank,
and many more!
## Acknowledgements
Development of GPyTorch is supported by funding from
the [Bill and Melinda Gates Foundation](https://www.gatesfoundation.org/),
the [National Science Foundation](https://www.nsf.gov/),
[SAP](https://www.sap.com/index.html),
the [Simons Foundation](https://www.simonsfoundation.org),
and the [Gatsby Charitable Trust](https://www.gatsby.org.uk).
## License
GPyTorch is [MIT licensed](https://github.com/cornellius-gp/gpytorch/blob/main/LICENSE).

%prep
%autosetup -n gpytorch-1.10

%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-gpytorch -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Fri Apr 21 2023 Python_Bot <Python_Bot@openeuler.org> - 1.10-1
- Package Spec generated