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TensorFlow: A system for large-scale machine learning

2016-05-27 · Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, Xiaoqiang Zheng

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, general-purpose 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.

📄 PDF Abstract BibTeX arXiv:1605.08695

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tensorflow/tensorflow 공식 구현 tf

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TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

2016-03-14 · Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo 외

TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms. A computation expressed using TensorFlow can be executed with little or no change on a wide vari…

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Scaling TensorFlow to 300 million predictions per second

2021-09-20 · Jan Hartman, Davorin Kopič

We present the process of transitioning machine learning models to the TensorFlow framework at a large scale in an online advertising ecosystem. In this talk we address the key challenges we faced and describe how we suc…

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TF.Learn: TensorFlow's High-level Module for Distributed Machine Learning

2016-12-13 · Yuan Tang

TF.Learn is a high-level Python module for distributed machine learning inside TensorFlow. It provides an easy-to-use Scikit-learn style interface to simplify the process of creating, configuring, training, evaluating, a…

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TensorFlow.js: Machine Learning for the Web and Beyond

2019-01-16 · Daniel Smilkov, Nikhil Thorat, Yannick Assogba, Ann Yuan 외

TensorFlow.js is a library for building and executing machine learning algorithms in JavaScript. TensorFlow.js models run in a web browser and in the Node.js environment. The library is part of the TensorFlow ecosystem, …

BIG-bench Machine Learning

TensorSCONE: A Secure TensorFlow Framework using Intel SGX

2019-02-12 · Roland Kunkel, Do Le Quoc, Franz Gregor, Sergei Arnautov 외

Machine learning has become a critical component of modern data-driven online services. Typically, the training phase of machine learning techniques requires to process large-scale datasets which may contain private and …

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