automatic import of botorchopeneuler23.09

3 files changed, 96 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
index e69de29..d5809ea 100644
--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1 @@
+/botorch-0.9.5.tar.gz
diff --git a/botorch.spec b/botorch.spec
new file mode 100644
index 0000000..9c52f85
--- /dev/null
+++ b/botorch.spec
@@ -0,0 +1,94 @@
+%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
diff --git a/sources b/sources
new file mode 100644
index 0000000..b8ba976
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+a567a6b01152955abaccb8e58c3473d4  botorch-0.9.5.tar.gz