automatic import of python-artist-engineering-geek

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+/Artist-Engineering_Geek-0.1.0.tar.gz
diff --git a/python-artist-engineering-geek.spec b/python-artist-engineering-geek.spec
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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.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
diff --git a/sources b/sources
new file mode 100644
index 0000000..6280abf
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+8c23b890baf696c167f336d9671b4d1f  Artist-Engineering_Geek-0.1.0.tar.gz