Reproducible machine learning with PyTorch and Quilt

In this article, we'll train a PyTorch model to perform super-resolution imaging, a technique for gracefully upscaling images. Super-resolution imaging (right) infers pixel values from a lower-resolution image (left). Machine learning projects typically begin by acquiring data, cleaning the data, and converting the data into model-native formats. Such manual data pipelines are tedious to create and difficult to reproduce over time, across collaborators, and across machines. Moreover, trained models are often stored haphazardly, without version control.