CNTK: Microsoft's Open-Source Deep-Learning Toolkit

被引:252
|
作者
Seide, Frank [1 ]
Agarwal, Amit [1 ]
机构
[1] Microsoft Res, One Microsoft Way, Redmond, WA 98052 USA
关键词
D O I
10.1145/2939672.2945397
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This tutorial will introduce the Computational Network Toolkit, or CNTK, Microsoft's cutting-edge open-source deep learning toolkit for Windows and Linux. CNTK is a powerful computation-graph based deep-learning toolkit for training and evaluating deep neural networks. Microsoft product groups use CNTK, for example to create the Cortana speech models and web ranking. CNTK supports feed-forward, convolutional, and recurrent networks for speech, image, and text workloads, also in combination. Popular network types are supported either natively (convolution) or can be described as a CNTK configuration (LSTM, sequence-to-sequence). CNTK scales to multiple GPU servers and is designed around efficiency. The tutorial will give an overview of CNTK's general architecture and describe the specific methods and algorithms used for automatic differentiation, recurrent-loop inference and execution, memory sharing, on-the-fly randomization of large corpora, and multi-server parallelization. We will then show how typical uses looks like for relevant tasks like image recognition, sequence-to-sequence modeling, and speech recognition.
引用
收藏
页码:2135 / 2135
页数:1
相关论文
共 50 条
  • [31] μDIC: An open-source toolkit for digital image correlation
    Olufsen, Sindre Nordmark
    Andersen, Marius Endre
    Fagerholt, Egil
    [J]. SOFTWAREX, 2020, 11
  • [32] OpenNMT: Open-Source Toolkit for Neural Machine Translation
    Klein, Guillaume
    Kim, Yoon
    Deng, Yuntian
    Senellart, Jean
    Rush, Alexander M.
    [J]. PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017): SYSTEM DEMONSTRATIONS, 2017, : 67 - 72
  • [33] KIPET - AN OPEN-SOURCE KINETIC PARAMETER ESTIMATION TOOLKIT
    Short, Michael
    Schenk, Christina
    Thierry, David
    Rodriguez, Jose Santiago
    Biegler, Lorenz T.
    Garcia-Munoz, Salvador
    [J]. PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON FOUNDATIONS OF COMPUTER-AIDED PROCESS DESIGN, 2019, 47 : 299 - 304
  • [34] GDP: an open-source GNSS data preprocessing toolkit
    Zhengsheng Chen
    Yang Cui
    Linyang Li
    Qinghua Zhang
    Zhiping Lu
    Xuerui Li
    Yingcai Kuang
    Kaichun Yang
    Fengjuan Rong
    [J]. GPS Solutions, 2020, 24
  • [35] QPlane: An Open-Source Reinforcement Learning Toolkit for Autonomous Fixed Wing Aircraft Simulation
    Richter, David J.
    Calix, Ricardo A.
    [J]. MMSYS '21: PROCEEDINGS OF THE 2021 MULTIMEDIA SYSTEMS CONFERENCE, 2021, : 261 - 266
  • [36] Implementing Deep Learning Algorithms in Anatomic Pathology Using Open-source Deep Learning Libraries
    McAlpine, Ewen
    Michelow, Pamela
    [J]. ADVANCES IN ANATOMIC PATHOLOGY, 2020, 27 (04) : 260 - 268
  • [37] Deep open-source machine translation
    Bond, Francis
    Oepen, Stephan
    Nichols, Eric
    Flickinger, Dan
    Velldal, Erik
    Haugereid, Petter
    [J]. MACHINE TRANSLATION, 2011, 25 (02) : 87 - 105
  • [38] MemTorch: An Open-source Simulation Framework for Memristive Deep Learning Systems
    Lammie, Corey
    Xiang, Wei
    Linares-Barranco, Bernabe
    Azghadi, Mostafa Rahimi
    [J]. NEUROCOMPUTING, 2022, 485 : 124 - 133
  • [39] ClinicaDL: An open-source deep learning software for reproducible neuroimaging processing
    Thibeau-Sutre, Elina
    Diaz, Mauricio
    Hassanaly, Ravi
    Routier, Alexandre
    Dormont, Didier
    Colliot, Olivier
    Burgos, Ninon
    [J]. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2022, 220
  • [40] Performing a Research Study Using Open-Source Deep Learning Models
    Kim, Hyungjin
    [J]. KOREAN JOURNAL OF RADIOLOGY, 2024, 25 (03) : 217 - 219