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Dissecting tf.function to discover AutoGraph strengths and subtleties


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 @tf.fuction 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 tf.Tensor 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 tf.function highlighting all the peculiarities that are worth knowing to build the right skill-set.

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