We present MxNet Gluon, an easy to use tool for designing a wide range of networks from image processing (LeNet, inception, etc.) to advanced NLP (TreeLSTM). It combines the convenience of imperative frameworks (PyTorch, Torch, Chainer) with efficient symbolic execution (TensorFlow, CNTK). The tutorial covers the following issues: basic distributed linear algebra with NDArray, automatic differentiation of code, and designing networks from scratch (and using Gluon). Subsequently we cover convenience and efficiency features such as automagic shape inference, deferred initialization and lazy evaluation, and hybridization of compute graphs. We then discuss structured architectures such as TreeLSTMs, which are key for natural language processing. We conclude by showing how to perform parallel and distributed training on multiple GPUs and multiple machines. For Jupyter notebooks and details see http://gluon.mxnet.io and https://github.com/zackchase/mxnet-the-straight-dope