Google’s Machine Learning Software TensorFlow Goes Open Source

Tech giant Google today announced the open source release of TensorFlow, its second-generation machine learning software. Google described TensorFlow as a general, flexible, portable, easy-to-use system that builds on DistBelief, the companyEUs internal deep learning infrastructure developed in 2011. DistBelief has allowed users to build larger neural networks and scale training to thousands of cores in GoogleEUs data centers.

TensorFlow is a tool for writing and executing machine learning algorithms. Computations for the system are performed in a data flow graph in which the nodes are mathematical operations and the edges are tensors, or multidimensional data arrays, that are exchanged between nodes. Users of the system construct the graphs and write the algorithms that get executed on each node, and TensorFlow executes the code asynchronously on different devices, cores, and threads.

Upgrade Over DistBelief

While successful, DistBelief also had limitations, said Jeff Dean, a Google senior fellow, and Rajat Monga, technical lead, on the companyEUs research blog. Targeted to neural networks, DistBelief was also considered hard to configure and closely bound to GoogleEUs internal infrastructure, making it challenging to share research code externally. TensorFlow is designed to correct those flaws, the researchers said.

In part, the enhancements in TensorFlow make it more flexible, portable and easier to use. It also improves on DistBeliefEUs speed, scalability, and production readiness. In fact, TensorFlow is twice as fast as DistBelief on some benchmarks, according to the research team.

In releasing the code for the system, Google will also offer sample neural networking models and algorithms, including models for recognizing photographs, identifying handwritten numbers and analyzing text.

TensorFlow can run on desktop CPUs and GPUs, as well as on server or mobile devices. It can also be deployed to the cloud with Docker containers. The newly released open source version runs on single machines, not on clusters.

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