In this work, we design a single portrait relighting algorithm. We first apply a physically-based portrait relighting method to generate a large scale, high quality, "in the wild" portrait relighting dataset (DPR). A deep Convolutional Neural Network (CNN) is then trained using this dataset to generate a relighted portrait image by using a source image and a target lighting as input. Our trained network can relight portrait images with resolutions as high as 1024 X 1024.