As data scientists frequently training models while in grad school and at work, we've faced many challenges in the model building process. This problem has recently gotten significant awareness in the community and here is a blog by Pete Warden from Google. These problems gave us countless headaches, and after talking to our friends, we knew we weren't alone. We wanted something that would not only keep track of configuration and results during the experiment but also allow data scientists to reproduce any experiment with re-runs! We initially built it as an internal solution for tracking our experiments, making them reproducible, and with easy setup of environments.