From 33347632858c36c16ad6e7a5d7dcb7cf2a4c1116 Mon Sep 17 00:00:00 2001 From: CoprDistGit Date: Fri, 21 Apr 2023 16:24:50 +0000 Subject: automatic import of python-gpytorch --- .gitignore | 1 + python-gpytorch.spec | 223 ++++++++++++++++++++++++++++++++++----------------- sources | 2 +- 3 files changed, 151 insertions(+), 75 deletions(-) diff --git a/.gitignore b/.gitignore index b04128f..5f1fb45 100644 --- a/.gitignore +++ b/.gitignore @@ -1 +1,2 @@ /gpytorch-1.9.1.tar.gz +/gpytorch-1.10.tar.gz diff --git a/python-gpytorch.spec b/python-gpytorch.spec index 679c463..e6f32db 100644 --- a/python-gpytorch.spec +++ b/python-gpytorch.spec @@ -1,16 +1,16 @@ %global _empty_manifest_terminate_build 0 Name: python-gpytorch -Version: 1.9.1 +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/af/23/9683f34e84d79d5ec564548bb6c4f88e107f1a6687ea8b1615d98cfbdfcb/gpytorch-1.9.1.tar.gz +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-black +Requires: python3-ufmt Requires: python3-twine Requires: python3-pre-commit Requires: python3-ipython @@ -29,11 +29,21 @@ 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 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. +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 numerous [**examples and tutorials**](https://gpytorch.readthedocs.io/en/latest/) on how to construct all sorts of models in GPyTorch. +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 @@ -44,14 +54,26 @@ 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 +#### 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. +**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 @@ -70,23 +92,9 @@ If you use GPyTorch, please cite the following papers: 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. +## 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) @@ -94,7 +102,22 @@ GPyTorch is primarily maintained by: - [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. +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/), @@ -102,6 +125,8 @@ 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 @@ -112,11 +137,21 @@ 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 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. +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 numerous [**examples and tutorials**](https://gpytorch.readthedocs.io/en/latest/) on how to construct all sorts of models in GPyTorch. +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 @@ -127,14 +162,26 @@ 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 +#### 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. +**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 @@ -153,23 +200,9 @@ If you use GPyTorch, please cite the following papers: 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. +## 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) @@ -177,7 +210,22 @@ GPyTorch is primarily maintained by: - [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. +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/), @@ -185,6 +233,8 @@ 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 @@ -192,11 +242,21 @@ 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 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. +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 numerous [**examples and tutorials**](https://gpytorch.readthedocs.io/en/latest/) on how to construct all sorts of models in GPyTorch. +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 @@ -207,14 +267,26 @@ 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 +#### 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. +**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 @@ -233,23 +305,9 @@ If you use GPyTorch, please cite the following papers: 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. +## 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) @@ -257,7 +315,22 @@ GPyTorch is primarily maintained by: - [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. +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/), @@ -265,9 +338,11 @@ 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-1.9.1 +%autosetup -n gpytorch-1.10 %build %py3_build @@ -307,5 +382,5 @@ mv %{buildroot}/doclist.lst . %{_docdir}/* %changelog -* Mon Apr 10 2023 Python_Bot - 1.9.1-1 +* Fri Apr 21 2023 Python_Bot - 1.10-1 - Package Spec generated diff --git a/sources b/sources index a32ddd9..e50383f 100644 --- a/sources +++ b/sources @@ -1 +1 @@ -95765d3f604be70b096b0ec7b5ceb961 gpytorch-1.9.1.tar.gz +ff64c884751c6d6364889f6111fc584a gpytorch-1.10.tar.gz -- cgit v1.2.3