Intelligence is a complex, multi-dimensional concept that encompasses multiple brain circuits. Understanding the underpinnings of the human brain requires not only accurate feature extraction from often noisy non-invasive brain imaging data (e.g., MRI), but also rigorous modeling of the complex relationships among distributed brain systems. In this work, we implement a highly scalable end-to-end computational learning framework – that is, a 3D deep convolutional neural network (CNN) to predict fluid intelligence scores directly from 3D brain MRI without any theory- or rule-based feature engineering. We address and overcome the challenge of processing large data (i.e. 44 GB of MRI) by using distributed deep learning techniques. The dataset originates from the Adolescent Brain Cognitive Development (ABCD) study, with 5832 subjects in the training set, 1251 in the validation set, and 1250 in the test set. The single-task ResNet50-3D model achieved mean squared errors of 0.73637 and 0.74535 respectively on the validation and test sets. The multi-task ResNet50-3D model achieved mean squared errors of 0.74418 and 0.75626 respectively on the validation and test sets. These results demonstrate not only that the prediction of fluid intelligence scores directly from structural and diffusion brain MRI is feasible but also that this scalable computational learning framework could be further developed for data-driven human neurocognitive research.