AutoGraph is one of the most exciting new features of Tensorflow 2.0: it allows transforming a subset of Python syntax into its portable, high-performance and language agnostic graph representation bridging the gap between Tensorflow 1.x and the 2.0 release based on eager execution. Using AutoGraph with the code@tf.fuction/code decorator seems easy, but in practice, writing efficient and correctly graph-convertible code requires to know in detail how AutoGraph and tf.function work. In particular, knowing how: A graph is created and when it is re-used; To deal with functions that create a state; To correctly use the Tensorflow codetf.Tensor/code object instead of using the Python native types to speed-up the computation; defines the minimum skill-set required to write correct graph-accelerable code. The talk will guide you trough AutoGraph and codetf.function/code highlighting all the peculiarities that are worth knowing to build the right skill-set.