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%global _empty_manifest_terminate_build 0
Name: python-Artist-Engineering-Geek
Version: 0.1.0
Release: 1
Summary: A bunch of GANs and data downloaders to make a custom AI artist
License: MIT License
URL: https://github.com/Fatima-x-Nikhil/Artist
Source0: https://mirrors.aliyun.com/pypi/web/packages/13/b6/1b3c8960a298e6ad5af273c789133898abac022b270bb3dc6653ad923bcf/Artist-Engineering_Geek-0.1.0.tar.gz
BuildArch: noarch
Requires: python3-torch
Requires: python3-torchvision
Requires: python3-matplotlib
Requires: python3-Pillow
Requires: python3-pytorch-lightning
Requires: python3-numpy
Requires: python3-tqdm
Requires: python3-requests
%description
# Artist
## Motivation
- An easy to edit codebase for Progressive GAN originally published by this [research paper][Progressive GAN Research Paper] and other GANs
- Supplement my personal projects
- Resume builder
- For fun and to understand state-of-the-art AI
## Installation
#### CUDA Installation
Ensure you have a GPU if you want to train in any reasonable amount of time.
- [Install CUDA here][CUDA Install]
- [Install cuDNN here][cuDNN Install]
#### Project Installation
```sh
pip install Artist-Engineering-Geek
# Don't forget to install your specific pytorch and torchvision libraries for your gpu
# in my case, I have the NVIDIA RTX 3090 so this is my version
pip install --no-cache-dir --pre torch torchvision torchaudio -f https://download.pytorch.org/whl/nightly/cu112/torch_nightly.html
```
## Running the program
To train the program, run the "Train.ipynb" notebook and alter your parameters at will on GitHub.
There should be sufficient in-code documentation for you to understand what the hell is going on
[CUDA Install]: <https://developer.nvidia.com/cuda-downloads>
[cuDNN Install]: <https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html>
[Progressive GAN Research Paper]: <https://arxiv.org/abs/1710.10196>
%package -n python3-Artist-Engineering-Geek
Summary: A bunch of GANs and data downloaders to make a custom AI artist
Provides: python-Artist-Engineering-Geek
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-Artist-Engineering-Geek
# Artist
## Motivation
- An easy to edit codebase for Progressive GAN originally published by this [research paper][Progressive GAN Research Paper] and other GANs
- Supplement my personal projects
- Resume builder
- For fun and to understand state-of-the-art AI
## Installation
#### CUDA Installation
Ensure you have a GPU if you want to train in any reasonable amount of time.
- [Install CUDA here][CUDA Install]
- [Install cuDNN here][cuDNN Install]
#### Project Installation
```sh
pip install Artist-Engineering-Geek
# Don't forget to install your specific pytorch and torchvision libraries for your gpu
# in my case, I have the NVIDIA RTX 3090 so this is my version
pip install --no-cache-dir --pre torch torchvision torchaudio -f https://download.pytorch.org/whl/nightly/cu112/torch_nightly.html
```
## Running the program
To train the program, run the "Train.ipynb" notebook and alter your parameters at will on GitHub.
There should be sufficient in-code documentation for you to understand what the hell is going on
[CUDA Install]: <https://developer.nvidia.com/cuda-downloads>
[cuDNN Install]: <https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html>
[Progressive GAN Research Paper]: <https://arxiv.org/abs/1710.10196>
%package help
Summary: Development documents and examples for Artist-Engineering-Geek
Provides: python3-Artist-Engineering-Geek-doc
%description help
# Artist
## Motivation
- An easy to edit codebase for Progressive GAN originally published by this [research paper][Progressive GAN Research Paper] and other GANs
- Supplement my personal projects
- Resume builder
- For fun and to understand state-of-the-art AI
## Installation
#### CUDA Installation
Ensure you have a GPU if you want to train in any reasonable amount of time.
- [Install CUDA here][CUDA Install]
- [Install cuDNN here][cuDNN Install]
#### Project Installation
```sh
pip install Artist-Engineering-Geek
# Don't forget to install your specific pytorch and torchvision libraries for your gpu
# in my case, I have the NVIDIA RTX 3090 so this is my version
pip install --no-cache-dir --pre torch torchvision torchaudio -f https://download.pytorch.org/whl/nightly/cu112/torch_nightly.html
```
## Running the program
To train the program, run the "Train.ipynb" notebook and alter your parameters at will on GitHub.
There should be sufficient in-code documentation for you to understand what the hell is going on
[CUDA Install]: <https://developer.nvidia.com/cuda-downloads>
[cuDNN Install]: <https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html>
[Progressive GAN Research Paper]: <https://arxiv.org/abs/1710.10196>
%prep
%autosetup -n Artist-Engineering_Geek-0.1.0
%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-Artist-Engineering-Geek -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.0-1
- Package Spec generated
|
