In this session, you shall be introduced to a new framework for scientific computing, mainly aimed at deep learning workloads. The framework consists of an ndarray library that natively supports GPU execution, an automatic differentiation engine that is flexible and fast, and an optimization package for gradient based optimization methods. We shall discuss practical workflows, our features on top of python multiprocessing for efficient parallel data loaders and finally we shall briefly look at our upcoming just-in-time Tensor compiler to fuse computations and execute them more efficiently.