Learn about the design, training, and analysis of a state-of-the-art, deep learning-based, instance-level segmentation pipeline enabled by NVIDIA DGX-1. Instance segmentation is the task of assigning semantic class labels to each pixel of an image (for example, car, person, etc.), as well as a coherent instance identifier such that every pixel belonging to the same object instance shares the same identifier. This has a wide array of applications, including object recognition and tracking, pose estimation, and scene understanding. In the context of autonomous driving, this will allow vehicles to accurately delineate multiple vehicles and pedestrians within an image. We'll present a simple yet powerful end-to-end convolutional neural network to tackle this task with state-of-the-art performance on the challenging Cityscapes Instance-Level Segmentation task. Our model consists of two independently trained individual deep neural networks with innovative training targets, followed by joint fine-tuning. The 30 million parameter network is trained on the new NVIDIA DGX-1 deep learning accelerator in approximately 30 hours. This is a 50% speedup compared to the NVIDIA Maxwell TITAN X, and is immeasurably faster than any CPU implementation.