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@@ -0,0 +1 @@ +/gpytorch-1.9.1.tar.gz diff --git a/python-gpytorch.spec b/python-gpytorch.spec new file mode 100644 index 0000000..679c463 --- /dev/null +++ b/python-gpytorch.spec @@ -0,0 +1,311 @@ +%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 @@ -0,0 +1 @@ +95765d3f604be70b096b0ec7b5ceb961 gpytorch-1.9.1.tar.gz |