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TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
[article]
2016
arXiv
pre-print
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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
unpublished
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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
2017
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '17
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2017 pre-print arXiv:1708.02637v1 fatcat:dz6d45uqe5an3knmy2usxfz52m
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]
2017
arXiv
pre-print
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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]
2019
arXiv
pre-print
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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]
2016
arXiv
pre-print
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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]
2021
arXiv
pre-print
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2020
Proceedings of the 21st International Middleware Conference
unpublished
doi:10.1145/3423211.3425687 fatcat:bez6qbu6xjgavjijponlsznasa
doi:10.1145/3423211.3425687 fatcat:bez6qbu6xjgavjijponlsznasa
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]
2016
arXiv
pre-print
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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]
2019
arXiv
pre-print
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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]
2020
arXiv
pre-print
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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]
2021
arXiv
pre-print
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2021 pre-print arXiv:2103.14407v1 fatcat:mzh53iwcsfamxm4o33wudnbciy
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]
2019
arXiv
pre-print
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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]
2018
arXiv
pre-print
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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]
2017
arXiv
pre-print
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2017
Proceedings of the 1st Workshop on Distributed Infrastructures for Deep Learning - DIDL '17
unpublished
doi:10.1145/3154842.3154843 fatcat:hxak4qwswrfjlfblh47y6a4o7m
doi:10.1145/3154842.3154843 fatcat:hxak4qwswrfjlfblh47y6a4o7m
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]
2020
arXiv
pre-print
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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
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