We present a novel format of machine learning competitions where a user submits code that generates images trained on training samples, the code then runs on Kaggle, produces dog images, and user receives scores for the performance of their generative content based on 1. quality of images, 2. diversity of images, and 3. memorization penalty. This style of competition targets the usage of Generative Adversarial Networks (GAN)[4], but is open for all generative models. Our implementation addresses overfitting by incorporating two different pre-trained neural networks, as well as two separate "ground truth" image datasets, for the public and private leaderboards. We also have an enclosed compute environment to prevent submissions of non-generated images. In this paper, we describe both the algorithmic and system design of our competition, as well as sharing our lessons learned from running this competition [6] in July 2019 with 900+ teams participating and over 37,000 submissions and their code received.