<jupyter_start><jupyter_text>Running ProGAN Pytorch on Google ColabProGAN Pytorch package by [Akanimax](https://github.com/akanimax). Colab Notebook by Ianmcmill.This is a step-by-step instruction on how to initially setup ProGAN on your Google Drive/Colab. 1. Switch to a GPU runtime. At the top of the notebook click "Runtime" - "Change runtime type" - choose "Python 3" & "GPU" for Hardware acceleration. 2. Install the python package.<jupyter_code>!pip3 install pro-gan-pth<jupyter_output><empty_output><jupyter_text>3. Mount your Google Drive for persistently saving training results.<jupyter_code>from google.colab import drive
drive.mount('/content/drive')<jupyter_output><empty_output><jupyter_text>4. Prepare Google Drive and git clone the pro_gan_pytorch-examples This repository contains scripts to train the network  >4.1. Navigate to your Google Drive.<jupyter_code>cd drive/My\ Drive/<jupyter_output><empty_output><jupyter_text>>4.2 Git clone the example repo<jupyter_code>!git clone https://github.com/akanimax/pro_gan_pytorch-examples.git
cd pro_gan_pytorch-examples<jupyter_output><empty_output><jupyter_text>>4.3 **Outside** of this notebook, upload your dataset to the /data folder inside the cloned git. This can be done using the **Google Drive interface**.Uploading a ZIP file is recommended. The unzipping is done in the colab notebook. Navigate to the /data folder where you uploaded you dataset.Use !unzip<jupyter_code>cd data
!unzip YOUR-DATASET-FILE.zip<jupyter_output><empty_output><jupyter_text>After unzipping delete the zip to free up Drive space. 5. Creating a config file is the same as for running your model locally.  6. Navigate to the `implementation` folder and start your training.Note that you always need a "!" infront of a command else you will get an error.<jupyter_code>!python train_network.py --config=configs/yourconfigfile.conf<jupyter_output><empty_output>
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{'chain_length': '6', 'dir': 'jupyter-structured-clean-dedup', 'id': '340903', 'license': 'MIT', 'n_tokens_mistral': 596, 'n_tokens_neox': 581, 'n_words': 206, 'path': 'pro_gan_pytorch_example.ipynb', 'repo_name': 'akanimax/pro-gan', 'size': '1905'}