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%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 <Python_Bot@openeuler.org> - 0.7.3-1
- Package Spec generated
|
