Just a few days back, Google, in a not so surprising move, announced open sourcing its machine learning platform called TensorFlow. TensorFlow employs the concept of neural computation using Data Flow Graphs. Data flow graphs are represented as nodes and edges where nodes represent a mathematical operation or an endpoint for data ingestion, sending output, or reading and writing persistent variables. Edges, on the other hand, describe the input output relationship between the nodes. Typically a node represents a computational system and is executed asynchronously in parallel with others.
The primary reason Google quotes for making this an open source platform is to inspire more research, not only in the world of unique computational domains, but also research related to deep neural networks. One of the most important architectural aspects of TensorFlow is that it can be run on a multitude of devices such as desktop, server, mobile, you name it — leveraging computing power from all these different devices and in turn allowing it to demonstrate its capabilities as a truly portable machine learning platform.
Asynchronous computing, threads, and queues are first class citizens in TensorFlow allowing it to maximize performance from the available hardware running the compute elements of the data flow graph. It also supports auto-differentiation out of the box. It can automatically compute derivatives based on the available data combined with the objective function and predictive model definition. It is also extremely flexible in terms of supporting custom libraries. If you can express a computation in terms of a data flow graph, you can leverage TensorFlow’s capabilities. As simple as that!
Today, it supports Python and C++ interfaces as is, but Google is encouraging you to contribute SWIG (simplified wrapper and interface generator) interfaces in JavaScript, R, and other popular machine learning and data science languages.
