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|
%global _empty_manifest_terminate_build 0
Name: python-gpytorch
Version: 1.9.1
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/af/23/9683f34e84d79d5ec564548bb6c4f88e107f1a6687ea8b1615d98cfbdfcb/gpytorch-1.9.1.tar.gz
BuildArch: noarch
Requires: python3-scikit-learn
Requires: python3-linear-operator
Requires: python3-black
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
[](https://github.com/cornellius-gp/gpytorch/actions/workflows/run_test_suite.yml)
[](https://gpytorch.readthedocs.io/en/latest/?badge=latest)
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 all inference operations using modern 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` 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 numerous [**examples and 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
```
#### 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}
}
```
## Development
To run the unit tests:
```bash
python -m unittest
```
By default, the random seeds are locked down for some of the tests.
If you want to run the tests without locking down the seed, run
```bash
UNLOCK_SEED=true python -m unittest
```
If you plan on submitting a pull request, please make use of our pre-commit hooks to ensure that your commits adhere
to the general style guidelines enforced by the repo. To do this, navigate to your local repository and run:
```bash
pip install pre-commit
pre-commit install
```
From then on, this will automatically run flake8, isort, black and other tools over the files you commit each time you commit to gpytorch or a fork of it.
## 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) David Arbour, Eytan Bakshy, David Eriksson, Jared Frank, Sam Stanton, Bram Wallace, Ke Alexander Wang, Ruihan Wu.
## 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).
%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
[](https://github.com/cornellius-gp/gpytorch/actions/workflows/run_test_suite.yml)
[](https://gpytorch.readthedocs.io/en/latest/?badge=latest)
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 all inference operations using modern 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` 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 numerous [**examples and 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
```
#### 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}
}
```
## Development
To run the unit tests:
```bash
python -m unittest
```
By default, the random seeds are locked down for some of the tests.
If you want to run the tests without locking down the seed, run
```bash
UNLOCK_SEED=true python -m unittest
```
If you plan on submitting a pull request, please make use of our pre-commit hooks to ensure that your commits adhere
to the general style guidelines enforced by the repo. To do this, navigate to your local repository and run:
```bash
pip install pre-commit
pre-commit install
```
From then on, this will automatically run flake8, isort, black and other tools over the files you commit each time you commit to gpytorch or a fork of it.
## 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) David Arbour, Eytan Bakshy, David Eriksson, Jared Frank, Sam Stanton, Bram Wallace, Ke Alexander Wang, Ruihan Wu.
## 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).
%package help
Summary: Development documents and examples for gpytorch
Provides: python3-gpytorch-doc
%description help
[](https://github.com/cornellius-gp/gpytorch/actions/workflows/run_test_suite.yml)
[](https://gpytorch.readthedocs.io/en/latest/?badge=latest)
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 all inference operations using modern 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` 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 numerous [**examples and 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
```
#### 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}
}
```
## Development
To run the unit tests:
```bash
python -m unittest
```
By default, the random seeds are locked down for some of the tests.
If you want to run the tests without locking down the seed, run
```bash
UNLOCK_SEED=true python -m unittest
```
If you plan on submitting a pull request, please make use of our pre-commit hooks to ensure that your commits adhere
to the general style guidelines enforced by the repo. To do this, navigate to your local repository and run:
```bash
pip install pre-commit
pre-commit install
```
From then on, this will automatically run flake8, isort, black and other tools over the files you commit each time you commit to gpytorch or a fork of it.
## 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) David Arbour, Eytan Bakshy, David Eriksson, Jared Frank, Sam Stanton, Bram Wallace, Ke Alexander Wang, Ruihan Wu.
## 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).
%prep
%autosetup -n gpytorch-1.9.1
%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-gpytorch -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 1.9.1-1
- Package Spec generated
|
