Metadata-Version: 2.1
Name: Artist-Engineering-Geek
Version: 0.1.0
Summary: A bunch of GANs and data downloaders to make a custom AI artist
Home-page: https://github.com/Fatima-x-Nikhil/Artist
Author: Nikhil Melgiri
Author-email: nmelgiri@uwaterloo.ca
License: UNKNOWN
Project-URL: Bug Tracker, https://github.com/Fatima-x-Nikhil/Artist/issues
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>
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
