TensorFlow: A system for large-scale machine learning

TensorFlow is a machine learning system that operates at
large scale and in heterogeneous environments. TensorFlow
uses dataflow graphs to represent computation,
shared state, and the operations that mutate that state. It
maps the nodes of a dataflow graph across many machines
in a cluster, and within a machine across multiple computational
devices, including multicore CPUs, generalpurpose
GPUs, and custom designed ASICs known as
Tensor Processing Units (TPUs). This architecture gives
flexibility to the application developer: whereas in previous
“parameter server” designs the management of shared
state is built into the system, TensorFlow enables developers
to experiment with novel optimizations and training
algorithms. TensorFlow supports a variety of applications,
with particularly strong support for training and
inference on deep neural networks. Several Google services
use TensorFlow in production, we have released it
as an open-source project, and it has become widely used
for machine learning research. In this paper, we describe
the TensorFlow dataflow model in contrast to existing systems,
and demonstrate the compelling performance that
TensorFlow achieves for several real-world applications.

Source: https://arxiv.org/pdf/1605.08695v2.pdf

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