Despite the large increase of deep learning solutions in recent years, no deep learning iris pipelines have yet been developed. Inspired by conventional iris recognition pipelines, we present our general deep architecture for iris recognition. The presented deep iris pipeline is an end-to-end convolutional neural network consisting of two high-level blocks: segmentation and recognition. The segmentation part is tasked with the generation of binary mask, which corresponds with the surface of the iris. These masks are multiplied with the original iris image and then fed to the recognition part. The recognition part extracts meaningful iris features, which are then used for matching. Our model achieved high results on both testing datasets. On Casia-Iris-Thousand it achieved a Rank-1 accuracy of 95.12% and on SBVPI an accuracy of 92.33%. We also implemented a cross-database model, trained on samples from both dataset, which achieved an accuracy of 88.53%. Our deep pipeline outperformed a conventional iris pipeline in speed and accuracy. As far as we are aware, our pipeline is the rst implementation of an end-to-end deep neural network, which is able to segment and recognize the iris image. As opposed to current deep models, which perform recognition on a pre-normalized iris image, our method uses original iris images.