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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

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  
We present a framework for specifying, training, evaluating, and deploying machine learning models.  ...  Our focus is on simplifying cutting edge machine learning for practitioners in order to bring such technologies into production.  ...  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

User-transparent Distributed TensorFlow [article]

Abhinav Vishnu, Joseph Manzano, Charles Siegel, Jeff Daily
2017 arXiv   pre-print
Lastly -- and arguably most importantly -- we make our implementation available for broader use, under the umbrella of Machine Learning Toolkit for Extreme Scale (MaTEx) at  ...  Distributed DL implementations capable of execution on large scale systems are becoming important to address the computational needs of large data produced by scientific simulations and experiments.  ...  For large scale execution of machine learning models in general, several programming models have been proposed.  ... 
arXiv:1704.04560v1 fatcat:seekm66p3bbwjemrf64vihkhxy

TensorFlow Doing HPC [article]

Steven W. D. Chien, Stefano Markidis, Vyacheslav Olshevsky, Yaroslav Bulatov, Erwin Laure, Jeffrey S. Vetter
2019 arXiv   pre-print
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  ...  TensorFlow is a popular emerging open-source programming framework supporting the execution of distributed applications on heterogeneous hardware.  ...  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.  ...  We thank our shepherd Professor Sara Bouchenak and the anonymous reviewers for their insightful comments and suggestions.  ... 
arXiv:2101.08204v1 fatcat:w5zjlifjrfae5az6yia2owbvre

CrypTFlow: Secure TensorFlow Inference [article]

Nishant Kumar, Mayank Rathee, Nishanth Chandran, Divya Gupta, Aseem Rastogi, Rahul Sharma
2020 arXiv   pre-print
We present CrypTFlow, a first of its kind system that converts TensorFlow inference code into Secure Multi-party Computation (MPC) protocols at the push of a button.  ...  Moreover, our system matches the inference accuracy of plaintext TensorFlow.  ...  ACKNOWLEDGEMENTS We thank our shepherd Xiao Wang, and anonymous reviewers for their valuable feedback.  ... 
arXiv:1909.07814v2 fatcat:e776uzl6crgv3mibgykniljweu

Non-Determinism in TensorFlow ResNets [article]

Miguel Morin, Matthew Willetts
2020 arXiv   pre-print
These results call for more robust evaluation strategies of deep learning models, as a significant amount of the variation in results across runs can arise simply from GPU randomness.  ...  We show that the stochasticity in training ResNets for image classification on GPUs in TensorFlow is dominated by the non-determinism from GPUs, rather than by the initialisation of the weights and biases  ...  While the existence of GPU non-determinism is well-known, the scale of its effect is perhaps less well understoodespecially in the context of contemporary machine learning algorithms.  ... 
arXiv:2001.11396v1 fatcat:2abzagj7nvdj5kuejat63qh5lq

Horovod: fast and easy distributed deep learning in TensorFlow [article]

Alexander Sergeev, Mike Del Balso
2018 arXiv   pre-print
Training modern deep learning models requires large amounts of computation, often provided by GPUs.  ...  In this paper we introduce Horovod, an open source library that improves on both obstructions to scaling: it employs efficient inter-GPU communication via ring reduction and requires only a few lines of  ...  Acknowledgements The authors would like to thank Molly Vorwerck and Jason Yosinski for the help in preparing this paper.  ... 
arXiv:1802.05799v3 fatcat:xteimxdbofculfvm4a6lsgnkg4

Underwater Object Detection using Tensorflow

Avinash Mahavarkar, Ritika Kadwadkar, Sneha Maurya, Smitha Raveendran, M.D. Patil, V.A. Vyawahare
2020 ITM Web of Conferences  
A suitable environment will be created so that Machine Learning algorithm will be used to train different images of the object.  ...  In this article, we conduct Underwater Object Detection using Machine Learning through Tensorflow and Image Processing along with Faster R-CNN (Regions with Convolution Neural Network) as an algorithm  ...  Tensorflow and faster R-CNN In this paper, underwater object detection using Tensorflow in order to train the system and Faster R-CNN as a machine learning algorithm for detection and implementation has  ... 
doi:10.1051/itmconf/20203203037 fatcat:r5mpwwhqxvf65ie3sry7mv3lke

XES Tensorflow - Process Prediction using the Tensorflow Deep-Learning Framework [article]

Joerg Evermann and Jana-Rebecca Rehse and Peter Fettke
2017 arXiv   pre-print
This demo paper describes a software application that applies the Tensorflow deep-learning framework to process prediction.  ...  The software application reads industry-standard XES files for training and presents the user with an easy-to-use graphical user interface for both training and prediction.  ...  Conclusion We presented a flexible deep-learning software application to predict business processes from industry-standard XES event logs.  ... 
arXiv:1705.01507v1 fatcat:33kdpyr5rra5hot53vwttqa2vi

ShapeFlow: Dynamic Shape Interpreter for TensorFlow [article]

Sahil Verma, Zhendong Su
2020 arXiv   pre-print
We believe ShapeFlow is a practical tool that benefits machine learning developers.  ...  We present ShapeFlow, a dynamic abstract interpreter for TensorFlow which quickly catches tensor shape incompatibility errors, one of the most common bugs in deep learning code.  ...  [10] proposed a validation algorithm for incoming data. The system is used on a large scale at Google.  ... 
arXiv:2011.13452v1 fatcat:7lijyti42rblnghmgyy2sfq4lq

Private Machine Learning in TensorFlow using Secure Computation [article]

Morten Dahl, Jason Mancuso, Yann Dupis, Ben Decoste, Morgan Giraud, Ian Livingstone, Justin Patriquin, Gavin Uhma
2018 arXiv   pre-print
We present a framework for experimenting with secure multi-party computation directly in TensorFlow.  ...  We give an open source implementation of a state-of-the-art protocol and report on concrete benchmarks using typical models from private machine learning.  ...  Introduction Several fields come together in private machine learning: cryptography, machine learning, distributed systems, and high-performance computing.  ... 
arXiv:1810.08130v2 fatcat:i46i3lsvvvarhgcqfzumoudj3y

Tensor Train decomposition on TensorFlow (T3F) [article]

Alexander Novikov, Pavel Izmailov, Valentin Khrulkov, Michael Figurnov, Ivan Oseledets
2020 arXiv   pre-print
Tensor Train decomposition is used across many branches of machine learning. We present T3F -- a library for Tensor Train decomposition based on TensorFlow.  ...  The library makes it easier to implement machine learning papers that rely on the Tensor Train decomposition. T3F includes documentation, examples and 94% test coverage.  ...  Acknowledgments This work was partially funded by the Ministry of Science and Education of Russian Federation as a part of Mega Grant Research Project 14.756.31.0001  ... 
arXiv:1801.01928v2 fatcat:hmpi4ywpsrd25esp6roth2vrhe
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