Filters








4,482 Hits in 4.9 sec

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems [article]

Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp (+24 others)
2016 arXiv   pre-print
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  ...  TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms.  ...  Acknowledgements The development of TensorFlow has benefitted enormously from the large and broad machine learning community at Google, and in particular from the suggestions and contributions from rest  ... 
arXiv:1603.04467v2 fatcat:v7vqnzquxrffrojafup72yggni

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow (+29 others)
unpublished
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  ...  TensorFlow [1] is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms.  ...  Acknowledgements The development of TensorFlow has benefitted enormously from the large and broad machine learning community at Google, and in particular from the suggestions and contributions from rest  ... 
fatcat:3vy2uork6ngb5pgjxhc2rlploi

TensorFlow Estimators

Heng-Tze Cheng, David Soergel, Yuan Tang, Philipp Tucker, Martin Wicke, Cassandra Xia, Jianwei Xie, Zakaria Haque, Lichan Hong, Mustafa Ispir, Clemens Mewald, Illia Polosukhin (+3 others)
2017 Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '17  
Our focus is on simplifying cutting edge machine learning for practitioners in order to bring such technologies into production.  ...  We present a framework for specifying, training, evaluating, and deploying machine learning models.  ...  Scikit-learn has been used in a large number of small to medium scale machine learning tasks.  ... 
doi:10.1145/3097983.3098171 dblp:conf/kdd/ChengHHIMPRSSST17 fatcat:4reu4vdfenglnozd37lskupn5y

Distributed TensorFlow with MPI [article]

Abhinav Vishnu, Charles Siegel, Jeffrey Daily
2017 arXiv   pre-print
In this paper, we extend recently proposed Google TensorFlow for execution on large scale clusters using Message Passing Interface (MPI).  ...  Machine Learning and Data Mining (MLDM) algorithms are becoming increasingly important in analyzing large volume of data generated by simulations, experiments and mobile devices.  ...  A few other toolkits support execution on large scale systems. These toolkits include Microsoft DMTK and Machine Learning Toolkit for Extreme Scale (MaTEx).  ... 
arXiv:1603.02339v2 fatcat:sff2anv5bfbtfipf4wd5ig75qi

TensorFlow Doing HPC [article]

Steven W. D. Chien, Stefano Markidis, Vyacheslav Olshevsky, Yaroslav Bulatov, Erwin Laure, Jeffrey S. Vetter
2019 arXiv   pre-print
TensorFlow is a popular emerging open-source programming framework supporting the execution of distributed applications on heterogeneous hardware.  ...  While TensorFlow has been initially designed for developing Machine Learning (ML) applications, in fact TensorFlow aims at supporting the development of a much broader range of application kinds that are  ...  Experiments were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at PDC Center for High Performance Computing and HPC2N.  ... 
arXiv:1903.04364v1 fatcat:wxdhhe4ewncqjijzqsctkyuvkq

A Tour of TensorFlow [article]

Peter Goldsborough
2016 arXiv   pre-print
In November 2015, Google released TensorFlow, an open source deep learning software library for defining, training and deploying machine learning models.  ...  We then compare TensorFlow to alternative libraries such as Theano, Torch or Caffe on a qualitative as well as quantitative basis and finally comment on observed use-cases of TensorFlow in academia and  ...  As per the initial publication, TensorFlow aims to be "an interface for expressing machine learning algorithms" in "large-scale [. . . ] on heterogeneous distributed systems" [8] .  ... 
arXiv:1610.01178v1 fatcat:aocbniiugnc7di3ocqbiakx25u

secureTF: A Secure TensorFlow Framework [article]

Do Le Quoc, Franz Gregor, Sergei Arnautov, Roland Kunkel, Pramod Bhatotia, Christof Fetzer
2021 arXiv   pre-print
To tackle this challenge, we designed secureTF, a distributed secure machine learning framework based on Tensorflow for the untrusted cloud infrastructure. secureTF is a generic platform to support unmodified  ...  This poses significant security risks since these applications rely on applying machine learning algorithms on large datasets which may contain private and sensitive information.  ...  To generalize the machine learning approach for masses, Google proposed TensorFlow [8] as a machine learning framework designed for heterogeneous distributed systems.  ... 
arXiv:2101.08204v1 fatcat:w5zjlifjrfae5az6yia2owbvre

TensorFlow: A system for large-scale machine learning [article]

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 (+9 others)
2016 arXiv   pre-print
TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments.  ...  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.  ...  Acknowledgments We gratefully acknowledge contributions from our colleagues within Google, and from members of the wider machine learning community.  ... 
arXiv:1605.08695v2 fatcat:pr4tlifatfhdto4nwu7xdvvh54

TensorSCONE: A Secure TensorFlow Framework using Intel SGX [article]

Roland Kunkel and Do Le Quoc and Franz Gregor and Sergei Arnautov and Pramod Bhatotia and Christof Fetzer
2019 arXiv   pre-print
Typically, the training phase of machine learning techniques requires to process large-scale datasets which may contain private and sensitive information of customers.  ...  More specifically, we propose a generic and secure machine learning framework based on Tensorflow, which enables secure execution of existing applications on the commodity untrusted infrastructure.  ...  To generalize the machine learning approach for masses, Google proposed TensorFlow [12] as a machine learning framework designed for heterogeneous distributed systems.  ... 
arXiv:1902.04413v1 fatcat:uddmchv2k5bkjn3cicnl4pf2cq

tfShearlab: The TensorFlow Digital Shearlet Transform for Deep Learning [article]

Héctor Andrade-Loarca, Gitta Kutyniok
2020 arXiv   pre-print
This requires in particular an implementation of the shearlet transform in the current deep learning frameworks, such as TensorFlow.  ...  In this paper, we will also present several numerical experiments such as image denoising and inpainting, where the TensorFlow version can be shown to outperform previous libraries as well as the learned  ...  TensorFlow Scalability and Heterogeneity Since its release in 2015, Tensorflow has become the most widely used machine learning framework.  ... 
arXiv:2006.04591v1 fatcat:rur4ghxctvdwtljtixua4nvuxm

Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlow [article]

John McLeod, Hrvoje Stojic, Vincent Adam, Dongho Kim, Jordi Grau-Moya, Peter Vrancx, Felix Leibfried
2021 arXiv   pre-print
In the past decade, model-free reinforcement learning (RL) has provided solutions to challenging domains such as robotics.  ...  The latter is particularly important in industry, e.g. in production systems that directly impact a company's revenue.  ...  TensorFlow: Large-scale machine learning on heterogeneous distributed systems. arXiv, 2016. Z I Botev, D P Kroese, R Y Rubinstein, and P L'Ecuyer. The cross-entropy method for optimization.  ... 
arXiv:2103.14407v2 fatcat:c7mzfbm7w5g4vapexxzj4p66li

Throughput Prediction of Asynchronous SGD in TensorFlow [article]

Zhuojin Li, Wumo Yan, Marco Paolieri, Leana Golubchik
2019 arXiv   pre-print
We validate our approach on TensorFlow training jobs for popular image classification neural networks, on AWS and on our in-house cluster, using nodes equipped with GPUs or only with CPUs.  ...  Modern machine learning frameworks can train neural networks using multiple nodes in parallel, each computing parameter updates with stochastic gradient descent (SGD) and sharing them asynchronously through  ...  The work before the existence of modern machine learning frameworks [24] estimates the speedups of distributed deep learning workloads through detailed analysis of the training process.  ... 
arXiv:1911.04650v1 fatcat:wk7wbys2czen7c77imtotkv3ve

TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank [article]

Rama Kumar Pasumarthi, Sebastian Bruch, Xuanhui Wang, Cheng Li, Michael Bendersky, Marc Najork, Jan Pfeifer, Nadav Golbandi, Rohan Anil, Stephan Wolf
2018 arXiv   pre-print
We propose TensorFlow Ranking, the first open source library for solving large-scale ranking problems in a deep learning framework.  ...  We also show that ranking models built using our model scale well for distributed training, without significant impact on metrics.  ...  Our main contributions are: • We propose a unified library for training large scale learning-to-rank models using deep learning in TensorFlow. • The library is flexible and highly configurable: it provides  ... 
arXiv:1812.00073v1 fatcat:ohmqa5i3o5g3jgsyfivnzkad3y

The TensorFlow Partitioning and Scheduling Problem: It's the Critical Path! [article]

Ruben Mayer and Christian Mayer and Larissa Laich
2017 arXiv   pre-print
State-of-the-art data flow systems such as TensorFlow impose iterative calculations on large graphs that need to be partitioned on heterogeneous devices such as CPUs, GPUs, and TPUs.  ...  We simulate the performance of the proposed strategies in heterogeneous environments with communication-intensive workloads that are common to TensorFlow.  ...  Distributed machine learning systems such as TensorFlow express the computation as a directed data flow graph where graph vertices represent computational operations and edges transport data between these  ... 
arXiv:1711.01912v1 fatcat:ke7gccuoiben3fnsv2lowpem44

Towards ML Engineering: A Brief History Of TensorFlow Extended (TFX) [article]

Konstantinos Katsiapis, Abhijit Karmarkar, Ahmet Altay, Aleksandr Zaks, Neoklis Polyzotis, Anusha Ramesh, Ben Mathes, Gautam Vasudevan, Irene Giannoumis, Jarek Wilkiewicz, Jiri Simsa, Justin Hong (+7 others)
2020 arXiv   pre-print
Meanwhile, Machine Learning (ML) has also grown over the past 2+ decades. ML is used more and more for research, experimentation and production workloads.  ...  We will share the lessons learned from over a decade of applied ML built on these platforms, explain both their similarities and their differences, and expand on the shifts (both mental and technical)  ...  Environment And Device Portability Sibyl was a massive scale ML platform designed to be deployed on Google's large-scale cluster, namely Borg [37] .  ... 
arXiv:2010.02013v2 fatcat:qjvd6wy3qvg3jnyoe7y4dmbwtm
« Previous Showing results 1 — 15 out of 4,482 results