Google on Monday announced the open source release of TensorFlow -- the company's second-generation machine learning system. TensorFlow is general, flexible, portable, and open source. It succeed Google's internal deep learning infrastructure DistBelief, which launched in 2011 and had allowed Googlers to build larger neural networks and scale training to thousands of cores in Google's datacenters.

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

Compared to DistBelief, TensorFlow offers improved speed, scalability, and production readiness, making it twice as fast as DistBelief.
TensorFlow has built-in support for deep learning, but is far more general than that -- any computation that you can express as a computational flow graph, you can compute with TensorFlow. Any gradient-based machine learning algorithm will benefit from TensorFlow’s auto-differentiation and suite of first-rate optimizers. And it’s easy to express your new ideas in TensorFlow via the Python interface.

TensorBoard, the visualization tool of TensorFlow, can be used for research, but it’s also ready for use in real products too. You can move your idea from training on your desktop GPU to running on your mobile phone. And you can get started quickly with machine learning tech by using Google's example model architectures. For example, Google plans to release a complete, top shelf ImageNet computer vision model on TensorFlow soon.

Google has open-sourced TensorFlow as a standalone library and associated tools, tutorials, and examples with the Apache 2.0 license.

"By sharing what we believe to be one of the best machine learning toolboxes in the world, we hope to create an open standard for exchanging research ideas and putting machine learning in products," Google said.