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@@ -0,0 +1 @@ +/Artist-Engineering_Geek-0.1.0.tar.gz diff --git a/python-artist-engineering-geek.spec b/python-artist-engineering-geek.spec new file mode 100644 index 0000000..aca1711 --- /dev/null +++ b/python-artist-engineering-geek.spec @@ -0,0 +1,164 @@ +%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.nju.edu.cn/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 +* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.0-1 +- Package Spec generated @@ -0,0 +1 @@ +8c23b890baf696c167f336d9671b4d1f Artist-Engineering_Geek-0.1.0.tar.gz |