%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]: [cuDNN Install]: [Progressive GAN Research Paper]: %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]: [cuDNN Install]: [Progressive GAN Research Paper]: %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]: [cuDNN Install]: [Progressive GAN Research Paper]: %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 - 0.1.0-1 - Package Spec generated