%global _empty_manifest_terminate_build 0
Name:		python-pytorch-memlab
Version:	0.2.4
Release:	1
Summary:	A lab to do simple and accurate memory experiments on pytorch
License:	MIT
URL:		https://github.com/Stonesjtu/pytorch_memlab
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/ca/a9/0554fae8883b2a646720e0e1d84d1bdef57e90ee3c244686f589052c3e0a/pytorch_memlab-0.2.4.tar.gz
BuildArch:	noarch


%description
[![Build Status](https://travis-ci.com/Stonesjtu/pytorch_memlab.svg?token=vyTdxHbi1PCRzV6disHp&branch=master)](https://travis-ci.com/Stonesjtu/pytorch_memlab)
![PyPI](https://img.shields.io/pypi/v/pytorch_memlab.svg)
[![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/Stonesjtu/pytorch_memlab.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/Stonesjtu/pytorch_memlab/context:python)
![PyPI - Downloads](https://img.shields.io/pypi/dm/pytorch_memlab.svg)
A simple and accurate **CUDA** memory management laboratory for pytorch,
it consists of different parts about the memory:
- Features:
  - Memory Profiler: A `line_profiler` style CUDA memory profiler with simple API.
  - Memory Reporter: A reporter to inspect tensors occupying the CUDA memory.
  - Courtesy: An interesting feature to temporarily move all the CUDA tensors into
    CPU memory for courtesy, and of course the backward transferring.
  - IPython support through `%mlrun`/`%%mlrun` line/cell magic
    commands.
- Table of Contents
  * [Installation](#installation)
  * [User-Doc](#user-doc)
    + [Memory Profiler](#memory-profiler)
    + [IPython support](#ipython-support)
    + [Memory Reporter](#memory-reporter)
    + [Courtesy](#courtesy)
    + [ACK](#ack)
  * [CHANGES](#changes)

%package -n python3-pytorch-memlab
Summary:	A lab to do simple and accurate memory experiments on pytorch
Provides:	python-pytorch-memlab
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-pytorch-memlab
[![Build Status](https://travis-ci.com/Stonesjtu/pytorch_memlab.svg?token=vyTdxHbi1PCRzV6disHp&branch=master)](https://travis-ci.com/Stonesjtu/pytorch_memlab)
![PyPI](https://img.shields.io/pypi/v/pytorch_memlab.svg)
[![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/Stonesjtu/pytorch_memlab.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/Stonesjtu/pytorch_memlab/context:python)
![PyPI - Downloads](https://img.shields.io/pypi/dm/pytorch_memlab.svg)
A simple and accurate **CUDA** memory management laboratory for pytorch,
it consists of different parts about the memory:
- Features:
  - Memory Profiler: A `line_profiler` style CUDA memory profiler with simple API.
  - Memory Reporter: A reporter to inspect tensors occupying the CUDA memory.
  - Courtesy: An interesting feature to temporarily move all the CUDA tensors into
    CPU memory for courtesy, and of course the backward transferring.
  - IPython support through `%mlrun`/`%%mlrun` line/cell magic
    commands.
- Table of Contents
  * [Installation](#installation)
  * [User-Doc](#user-doc)
    + [Memory Profiler](#memory-profiler)
    + [IPython support](#ipython-support)
    + [Memory Reporter](#memory-reporter)
    + [Courtesy](#courtesy)
    + [ACK](#ack)
  * [CHANGES](#changes)

%package help
Summary:	Development documents and examples for pytorch-memlab
Provides:	python3-pytorch-memlab-doc
%description help
[![Build Status](https://travis-ci.com/Stonesjtu/pytorch_memlab.svg?token=vyTdxHbi1PCRzV6disHp&branch=master)](https://travis-ci.com/Stonesjtu/pytorch_memlab)
![PyPI](https://img.shields.io/pypi/v/pytorch_memlab.svg)
[![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/Stonesjtu/pytorch_memlab.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/Stonesjtu/pytorch_memlab/context:python)
![PyPI - Downloads](https://img.shields.io/pypi/dm/pytorch_memlab.svg)
A simple and accurate **CUDA** memory management laboratory for pytorch,
it consists of different parts about the memory:
- Features:
  - Memory Profiler: A `line_profiler` style CUDA memory profiler with simple API.
  - Memory Reporter: A reporter to inspect tensors occupying the CUDA memory.
  - Courtesy: An interesting feature to temporarily move all the CUDA tensors into
    CPU memory for courtesy, and of course the backward transferring.
  - IPython support through `%mlrun`/`%%mlrun` line/cell magic
    commands.
- Table of Contents
  * [Installation](#installation)
  * [User-Doc](#user-doc)
    + [Memory Profiler](#memory-profiler)
    + [IPython support](#ipython-support)
    + [Memory Reporter](#memory-reporter)
    + [Courtesy](#courtesy)
    + [ACK](#ack)
  * [CHANGES](#changes)

%prep
%autosetup -n pytorch-memlab-0.2.4

%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-pytorch-memlab -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Wed Apr 12 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.4-1
- Package Spec generated