%global _empty_manifest_terminate_build 0 Name: python-BxTorch Version: 0.7.3 Release: 1 Summary: Large-Scale Machine and Deep Learning in PyTorch. License: License :: OSI Approved :: MIT License URL: https://github.com/borchero/bxtorch Source0: https://mirrors.nju.edu.cn/pypi/web/packages/5b/92/2877000b7111453fc7b3af3aa7714e7cc98cfb5fe2f39ed008906d6f47e9/BxTorch-0.7.3.tar.gz BuildArch: noarch Requires: python3-torch Requires: python3-numpy Requires: python3-scipy Requires: python3-numba Requires: python3-scikit-learn %description # BxTorch BxTorch is a high-level library for large-scale machine learning in [PyTorch](https://pytorch.org). It is engineered both to cut obsolete boilerplate code while preserving the flexibility of PyTorch to create just about any deep learning model. ## Installation BxTorch is available on PyPi, so simply run the following command: ```bash pip install bxtorch ``` ## Features Generally, BxTorch provides an object-oriented approach to abstracting PyTorch's API. The core design objective is to provide an API both as simple and as extensible as possible. The goal of this library is to be able to iterate between different models easily instead of squeezing out milliseconds where it is not required. Still, being focused on large-scale machine learning, BxTorch aims to make it as easy as possible working with large datasets. This includes out-of-the-box multi-GPU support where the user *does not need to write a single line of code*. Currently, BxTorch only provides means for running training/inference on a single machine. In case this is insufficient, you might be better off using PyTorch's `distributed` package directly. It must be emphasized that BxTorch is not meant to be a wrapper for PyTorch as Keras is for TensorFlow - it only provides *extensions*. ## Documentation Examples of the usage of BxTorch can be found in the [docs folder](docs). Method documentation is currently only available as [docstrings](bxtorch). ## License BxTorch is licensed under the [MIT License](LICENSE). %package -n python3-BxTorch Summary: Large-Scale Machine and Deep Learning in PyTorch. Provides: python-BxTorch BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-BxTorch # BxTorch BxTorch is a high-level library for large-scale machine learning in [PyTorch](https://pytorch.org). It is engineered both to cut obsolete boilerplate code while preserving the flexibility of PyTorch to create just about any deep learning model. ## Installation BxTorch is available on PyPi, so simply run the following command: ```bash pip install bxtorch ``` ## Features Generally, BxTorch provides an object-oriented approach to abstracting PyTorch's API. The core design objective is to provide an API both as simple and as extensible as possible. The goal of this library is to be able to iterate between different models easily instead of squeezing out milliseconds where it is not required. Still, being focused on large-scale machine learning, BxTorch aims to make it as easy as possible working with large datasets. This includes out-of-the-box multi-GPU support where the user *does not need to write a single line of code*. Currently, BxTorch only provides means for running training/inference on a single machine. In case this is insufficient, you might be better off using PyTorch's `distributed` package directly. It must be emphasized that BxTorch is not meant to be a wrapper for PyTorch as Keras is for TensorFlow - it only provides *extensions*. ## Documentation Examples of the usage of BxTorch can be found in the [docs folder](docs). Method documentation is currently only available as [docstrings](bxtorch). ## License BxTorch is licensed under the [MIT License](LICENSE). %package help Summary: Development documents and examples for BxTorch Provides: python3-BxTorch-doc %description help # BxTorch BxTorch is a high-level library for large-scale machine learning in [PyTorch](https://pytorch.org). It is engineered both to cut obsolete boilerplate code while preserving the flexibility of PyTorch to create just about any deep learning model. ## Installation BxTorch is available on PyPi, so simply run the following command: ```bash pip install bxtorch ``` ## Features Generally, BxTorch provides an object-oriented approach to abstracting PyTorch's API. The core design objective is to provide an API both as simple and as extensible as possible. The goal of this library is to be able to iterate between different models easily instead of squeezing out milliseconds where it is not required. Still, being focused on large-scale machine learning, BxTorch aims to make it as easy as possible working with large datasets. This includes out-of-the-box multi-GPU support where the user *does not need to write a single line of code*. Currently, BxTorch only provides means for running training/inference on a single machine. In case this is insufficient, you might be better off using PyTorch's `distributed` package directly. It must be emphasized that BxTorch is not meant to be a wrapper for PyTorch as Keras is for TensorFlow - it only provides *extensions*. ## Documentation Examples of the usage of BxTorch can be found in the [docs folder](docs). Method documentation is currently only available as [docstrings](bxtorch). ## License BxTorch is licensed under the [MIT License](LICENSE). %prep %autosetup -n BxTorch-0.7.3 %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-BxTorch -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Wed Apr 12 2023 Python_Bot - 0.7.3-1 - Package Spec generated