Why DeepMind made Sonnet open source

Why Sonnet if you have TensorFlow?

Since adopting TensorFlow as the choice of their framework, DeepMind has enjoyed the flexibility and adaptiveness of TF for building higher-level frameworks. In order to build neural network modules with Tensorflow, they created a framework called Sonnet. Sonnet doesn’t typically replace TensorFlow; it just eases the process of constructing neural networks. Prior to Sonnet, DeepMind developers were forced to become intimately familiar with the underlying TensorFlow graphs in order to correctly architect its applications. With Sonnet, the creation of neural network components is quite easy as it first constructs Python objects which represent some part of a neural network, and then separately connect these objects into the TensorFlow computation graph.

What makes Sonnet special?

Sonnet uses Modules. Modules encapsulate elements of a neural network which in turn abstracts low-level aspects of TensorFlow applications. Sonnet enables developers to build their own Modules using a simple programming model. These Modules simplify the neural network training and can help to implement individual neural networks that can be combined to implement higher-level networks. Developers can also easily extend Sonnet by implementing their own modules.

Using Sonnet, it becomes easier to switch between different models, allowing engineers to freely conduct experiments without worrying about hampering their entire projects.

Why open source Sonnet?

The announcement of Sonnet open sourcing came on April 7, 2017. Most people appreciated it as a move in the right direction. One of the focal purpose of DeepMind to open source Sonnet was to make the developer community to use Sonnet to take their own research forwards.  According to FossBytes, "DeepMind foresees Sonnet to be used by the community as a research propellant."

With this open sourcing, the machine learning community can then more actively contribute back by utilizing Sonnet in their own projects. Moreover, if the community becomes accustomed and acquainted with DeepMind’s internal libraries, it will become easier for the DeepMind group to release other Machine learning models alongside research papers.

Certain experienced developers also point out that using TensorFlow and Sonnet together is similar to using TensorFlow and Torch together, with a Reddit comment stating “DeepMind's trying to turn TensorFlow into Torch”. Nevertheless, open sourcing of Sonnet is seen as DeepMind’s part of their broader commitment to open source AI research. Also, as Sonnet is adopted by the community more similar frameworks are also likely to develop that make neural network construction easier using TensorFlow as the underlying runtime. Taking a further step towards democratization of machine learning and its subsidies.

Sonnet is already available on GitHub and will be regularly updated by the DeepMind team to match the in-house version.