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%global _empty_manifest_terminate_build 0
Name: botorch
Version: 0.9.5
Release: 1
Summary: Bayesian optimization in PyTorch
License: MIT
URL: https://pytorch.org/botorch
Source0: https://github.com/pytorch/botorch/archive/refs/tags/v%{version}.tar.gz#/%{name}-%{version}.tar.gz
Requires: python3-gpytorch
Requires: python3-multipledispatch
Requires: python3-pytorch
Requires: python3-pyro-ppl
Requires: python3-scipy
%description
Provides a modular and easily extensible interface for composing Bayesian optimization primitives,
including probabilistic models, acquisition functions, and optimizers. Harnesses the power of PyTorch,
including auto-differentiation, native support for highly parallelized modern hardware (e.g. GPUs)
using device-agnostic code, and a dynamic computation graph. Supports Monte Carlo-based acquisition
functions via the reparameterization trick, which makes it straightforward to implement new ideas
without having to impose restrictive assumptions about the underlying model. Enables seamless integration with deep and/or
convolutional architectures in PyTorch. Has first-class support for state-of-the art probabilistic models in
GPyTorch, including support for multi-task Gaussian Processes (GPs) deep kernel learning, deep GPs, and approximate inference.
%package -n python3-botorch
Summary: Bayesian optimization in PyTorch
Provides: python-botorch
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-setuptools_scm
BuildRequires: python3-pbr
BuildRequires: python3-pip
BuildRequires: python3-wheel
BuildRequires: python3-hatchling
%description -n python3-botorch
Provides a modular and easily extensible interface for composing Bayesian optimization primitives,
including probabilistic models, acquisition functions, and optimizers. Harnesses the power of PyTorch,
including auto-differentiation, native support for highly parallelized modern hardware (e.g. GPUs)
using device-agnostic code, and a dynamic computation graph. Supports Monte Carlo-based acquisition
functions via the reparameterization trick, which makes it straightforward to implement new ideas
without having to impose restrictive assumptions about the underlying model. Enables seamless integration with deep and/or
convolutional architectures in PyTorch. Has first-class support for state-of-the art probabilistic models in
GPyTorch, including support for multi-task Gaussian Processes (GPs) deep kernel learning, deep GPs, and approximate inference.
%package help
Summary: Development documents and examples for botorch
Provides: python3-botorch-doc
%description help
Provides a modular and easily extensible interface for composing Bayesian optimization primitives,
including probabilistic models, acquisition functions, and optimizers. Harnesses the power of PyTorch,
including auto-differentiation, native support for highly parallelized modern hardware (e.g. GPUs)
using device-agnostic code, and a dynamic computation graph. Supports Monte Carlo-based acquisition
functions via the reparameterization trick, which makes it straightforward to implement new ideas
without having to impose restrictive assumptions about the underlying model. Enables seamless integration with deep and/or
convolutional architectures in PyTorch. Has first-class support for state-of-the art probabilistic models in
GPyTorch, including support for multi-task Gaussian Processes (GPs) deep kernel learning, deep GPs, and approximate inference.
%prep
%autosetup -p1 -n %{name}-%{version}
%build
export SETUPTOOLS_SCM_PRETEND_VERSION=%{version}
%pyproject_build
%install
%pyproject_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
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}/doclist.lst .
%files -n python3-botorch
%doc *.md
%license LICENSE
%{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Sun Jan 28 2024 Binshuo Zu <274620705z@gmail.com> - 0.9.5-1
- Package init
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