Josh Levenberg

Josh Levenberg

Reed College, B.A. Math UC Berkeley, Ph.D. Math
Authored Publications
Google Publications
Other Publications
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    TensorFlow: A system for large-scale machine learning
    Jianmin Chen
    Matthieu Devin
    Geoffrey Irving
    Manjunath Kudlur
    Rajat Monga
    Benoit Steiner
    Paul Tucker
    Vijay Vasudevan
    Pete Warden
    Yuan Yu
    Xiaoqiang Zheng
    12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), USENIX Association(2016), pp. 265-283
    Preview abstract 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 a focus on 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 and demonstrate the compelling performance that Tensor- Flow achieves for several real-world applications. View details
    Why Google Stores Billions of Lines of Code in a Single Repository
    Rachel Potvin
    Communications of the ACM, 59(2016), pp. 78-87
    Preview abstract Google's monolithic repository provides a common source of truth for tens of thousands of developers around the world. This article outlines the scale of Google’s codebase, describes Google’s custom-built monolithic source repository, and discusses the reasons behind choosing this model. We provide background on the systems and workflows that make managing and working productively with a large repository feasible. We also review the advantages and trade-offs of this model of source code management. View details
    TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
    Ashish Agarwal
    Eugene Brevdo
    Craig Citro
    Matthieu Devin
    Ian Goodfellow
    Andrew Harp
    Geoffrey Irving
    Yangqing Jia
    Rafal Jozefowicz
    Lukasz Kaiser
    Manjunath Kudlur
    Dan Mané
    Rajat Monga
    Chris Olah
    Mike Schuster
    Jonathon Shlens
    Benoit Steiner
    Ilya Sutskever
    Kunal Talwar
    Paul Tucker
    Vijay Vasudevan
    Pete Warden
    Yuan Yu
    Xiaoqiang Zheng
    tensorflow.org(2015)
    Preview abstract 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 variety of heterogeneous systems, ranging from mobile devices such as phones and tablets up to large-scale distributed systems of hundreds of machines and thousands of computational devices such as GPU cards. The system is flexible and can be used to express a wide variety of algorithms, including training and inference algorithms for deep neural network models, and it has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields, including speech recognition, computer vision, robotics, information retrieval, natural language processing, geographic information extraction, and computational drug discovery. This paper describes the TensorFlow interface and an implementation of that interface that we have built at Google. The TensorFlow API and a reference implementation were released as an open-source package under the Apache 2.0 license in November, 2015 and are available at www.tensorflow.org. View details
    Fast View-Dependent Level-of-Detail Rendering Using Cached Geometry
    IEEE Visualization 2002, 13(2002)