1. A method of procedurally synthesizing a training dataset for training a machine-learning model, comprising: describing variations in content of training images to be included in said training dataset; generating training image definitions in accordance with said variations in parallel; employing said training image definitions to render corresponding training images in parallel; further employing said training image definitions to generate associated ground truth in parallel; and assembling said training images and associated ground truth into said training dataset.