From c945e1585b8bd34f2be0eb52a1c845ebad89ecf4 Mon Sep 17 00:00:00 2001 From: CoprDistGit Date: Mon, 29 Jan 2024 16:20:14 +0000 Subject: automatic import of botorch --- .gitignore | 1 + botorch.spec | 94 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ sources | 1 + 3 files changed, 96 insertions(+) create mode 100644 botorch.spec create mode 100644 sources 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 -- cgit v1.2.3