This paper presents Dexterity Network 1.0 (Dex-Net), a new dataset and associated algorithm to study the scaling effects of Big Data and Cloud Computation on robust grasp planning. The algorithm uses a Multi-Armed Bandit model with correlated rewards to leverage prior grasps and 3D object models in a growing dataset that currently includes over 10,000 unique 3D object models and 2.5 million parallel-jaw grasps. Each grasp includes an estimate of the probability of force closure under uncertainty in object and gripper pose and friction. Dex-Net 1.0 uses Multi-View Convolutional Neural Networks (MV-CNNs), a new deep learning method for 3D object classification, as a similarity metric between objects and the Google Cloud Platform to simultaneously run up to 1,500 virtual cores, reducing runtime by three orders of magnitude. Experiments suggest that using prior data can significantly benefit the quality and complexity of robust grasp planning. We report on system sensitivity to varying similarity metrics and pose and friction uncertainty levels. Code and additional information can be found at: berkeleyautomation.github.io