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* ID :  [https://www.wikidata.org/wiki/Q23680120 Q23680120]

2020년 12월 26일 (토) 05:12 판

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  1. This page describes the Python API for CNTK version 2.6.[1]
  2. Note We no longer include the CNTK, Caffe, Caffe2 and Theano Conda environments in the AWS Deep Learning AMI starting with the v28 release.[2]
  3. To run CNTK on the DLAMI with Conda To activate CNTK, open an Amazon Elastic Compute Cloud (Amazon EC2) instance of the DLAMI with Conda.[2]
  4. If you have a CPU instance, run this quick CNTK program.[2]
  5. A result of True is what you would expect if CNTK can access the GPU.[2]
  6. CNTK allows users to easily realize and combine popular model types such as feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs).[3]
  7. Today’s 2.7 release will be the last main release of CNTK.[3]
  8. The CNTK 2.7 release has full support for ONNX 1.4.1, and we encourage those seeking to operationalize their CNTK models to take advantage of ONNX and the ONNX Runtime.[3]
  9. We are incredibly grateful for all the support we have received from contributors and users over the years since the initial open-source release of CNTK.[3]
  10. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs).[4]
  11. CNTK can be included as a library in your Python, C#, or C++ programs, or used as a standalone machine-learning tool through its own model description language (BrainScript).[4]
  12. CNTK supports 64-bit Linux or 64-bit Windows operating systems.[4]
  13. It is recommended you install CNTK from precompiled binaries.[5]
  14. The GPU build also includes the MSR-developed 1bit-quantized SGD and block-momentum SGD parallel training algorithms, which allow for even faster distributed training in CNTK.[5]
  15. CNTK-R is an R package for CNTK, which uses the reticulate package to bind to CNTK’s Python API.[6]
  16. To use CNTK with R you’ll need to have the appropriate Python wheel for your system already installed.[6]
  17. In this session, you’ll discover how to deploy the Microsoft Cognitive Toolkit (CNTK) inside of Spark clusters on the Azure cloud platform.[7]
  18. Thanks for all the CNTK supporters, and I am privileged to have worked on it, and learned a lot in the process.[8]
  19. You can continue to use CNTK for training and inference in the way it currently is, as other Microsoft internal teams that still runs old models even in BrainScript or NDL.[8]
  20. Stopping adding new features does not mean CNTK is no longer open source, it just means that going forward, there will be no new GPU support (say, CUDA 11+), and no major new features added.[8]
  21. Deep learning newcomers: IMO CNTK is still a good entry to understand basics of deep learning, if you found CNTK documents/tutorials/examples useful.[8]
  22. using CNTK; using System ; using System .Collections.[9]
  23. TestHelper.cs -- Help functions for CNTK Library C# model training tests.[9]
  24. This talk will introduce the Computational Network Toolkit, or CNTK, Microsoft’s scalable open-source deep-learning toolkit for Windows and Linux.[10]
  25. CNTK is a powerful computation-graph based deep-learning toolkit for training and evaluating deep neural networks.[10]
  26. Participants will get to understand CNTK’s core concepts and usage, and practice to run neural-network trainings with CNTK for image recognition and text processing.[10]
  27. CNTK supports feed-forward, convolutional, and recurrent networks for speech, image, and text workloads, also in combination.[10]
  28. This tutorial will introduce the Computational Network Toolkit, or CNTK, Microsoft's cutting-edge open-source deep-learning toolkit for Windows and Linux.[11]
  29. CNTK is a powerful computation-graph based deep-learning toolkit for training and evaluating deep neural networks...[11]
  30. Microsoft product groups use CNTK, for example to create the Cortana speech models and web ranking.[11]
  31. Succinctly, author James McCaffrey offers instruction on the basics of installing and running CNTK, and also addresses machine-learning regression and classification techniques.[12]
  32. If you're looking to bring your deep learning to production you should definitely take a look at CNTK.[13]
  33. In this article I will show you how you can build a neural network with Microsoft Cognitive Toolkit.[13]
  34. I’ve found that CNTK is a good fit for building models in Python and running them in another language like Java or C#.[13]
  35. Microsoft Research develops CNTK to enable researchers to build learning machines.[13]
  36. CNTK is a low-level tensor library for building, training, and running deep neural networks.[14]
  37. Note the CreateFeatureVariable override which tells CNTK that our neural network will use a 1-dimensional tensor of 8 float values as input.[14]
  38. And the CreateLabelVariable override tells CNTK that we want our neural network to output a single float value.[14]
  39. Are you ready to start writing C# machine learning apps with CNTK?[14]
  40. However, there is another contending framework which I think may actually be better – it is called the Microsoft Cognitive Toolkit, or more commonly known as CNTK.[15]
  41. It also can be used as a back-end to Keras, but I would argue that there is little benefit to doing so as CNTK is already very streamlined.[15]
  42. Should you switch from using TensorFlow to CNTK?[15]
  43. However, Microsoft has opened up a lot, and CNTK is now open-source, so I would recommend giving it a try.[15]
  44. But CNTK can’t train on an enumeration of class instances.[16]
  45. And the second Var method tells CNTK that we want our neural network to output a single float value.[16]
  46. CNTK is actually written in C++, which can access the toolkit directly, but apparently few developers actually do that, preferring Python.[17]
  47. From its initial release, CNTK has allowed access from BrainScript, which is actually more of a configuration language.[17]
  48. Obsessing over CNTK, the Microsoft deep-learning library.[18]
  49. Using CNTK here is complete overkill, and not worth the overhead; I would not use it for something that simple.[18]
  50. Our goal here is simply to illustrate the basics of how CNTK works, from F#.[18]
  51. In future posts, we will look into scenarios where CNTK is actually useful.[18]
  52. Microsoft Cognitive Toolkit or CNTK is an open-source deep-learning toolkit.[19]
  53. NVidia development tools also required to build the Microsoft CNTK and support libraries.[19]
  54. After you run this script, CNTK will have loaded the model.[20]
  55. This summer Microsoft released the CNTK 2.0, C++ open source, cross-platform and cross-OS library for deep learning based on deep neural network (NN).[21]
  56. Actually CNTK is created by Microsoft speech researcher in 2012, and few years after it became the open source library at the codeplex site in early 2015.[21]
  57. A year later it is moved to GitHub, by announcing CNTK 1.0.[21]
  58. In June this year the CNTK 2.0 is released with lot of improvements and benchmarks.[21]
  59. The Microsoft Cognitive Toolkit (CNTK) is an open-source toolkit for commercial-grade distributed deep learning.[22]
  60. “Many of the AI services Microsoft has are now created using CNTK.[23]
  61. It’s not only Cognitive Services that have been created using CNTK but many other production-ready models.[23]
  62. “This was a major adoption barrier for CNTK in the past,” he explained.[23]
  63. “CNTK 2 remains the fastest deep learning toolkit for distributed deep learning,” claimed Huang, “and I want to highlight the word distributed.[23]
  64. CNTK ONNX Format Support Update CNTK to support load and save ONNX format from https://github.com/onnx/onnx, please try it and provide feedback.[24]
  65. Support saving a model in ONNX format, not all CNTK models are currently supported.[24]
  66. Only a subset of CNTK models are supported and no RNN.[24]
  67. The Microsoft Cognitive Toolkit (https://cntk.ai), is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph.[24]

소스

  1. ↑ Python API for CNTK (2.6) — Python API for CNTK 2.6 documentation
  2. ↑ 2.0 2.1 2.2 2.3 Deep Learning AMI
  3. ↑ 3.0 3.1 3.2 3.3 microsoft/CNTK: Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit
  4. ↑ 4.0 4.1 4.2 The Microsoft Cognitive Toolkit - Cognitive Toolkit - CNTK
  5. ↑ 5.0 5.1 Setup CNTK on your machine - Cognitive Toolkit - CNTK
  6. ↑ 6.0 6.1 R Interface to the Microsoft Cognitive Toolkit (CNTK) • cntk
  7. ↑ Natural Language Processing with CNTK and Apache Spark
  8. ↑ 8.0 8.1 8.2 8.3 Has Microsoft abandoned CNTK?
  9. ↑ 9.0 9.1 CNTK CNTK CPU Example 코드 작성하기]
  10. ↑ 10.0 10.1 10.2 10.3 KDD 2016 Tutorials: CNTK—Microsoft’s open-source deep-learning toolkit
  11. ↑ 11.0 11.1 11.2 CNTK: Microsoft's Open-Source Deep-Learning Toolkit
  12. ↑ Introduction to CNTK Succinctly
  13. ↑ 13.0 13.1 13.2 13.3 Bring your deep learning models to production with CNTK on Java
  14. ↑ 14.0 14.1 14.2 14.3 Use C# and a CNTK Neural Network To Predict House Prices In California
  15. ↑ 15.0 15.1 15.2 15.3 A Microsoft CNTK tutorial in Python
  16. ↑ 16.0 16.1 Predict Heart Disease With C# And A CNTK Deep Neural Network
  17. ↑ 17.0 17.1 .NET Coders Clamor for C# Support in Microsoft's CNTK AI Toolkit -- Visual Studio Magazine
  18. ↑ 18.0 18.1 18.2 18.3 Baby steps with CNTK and F# ·
  19. ↑ 19.0 19.1 Microsoft CNTK (Cognitive Toolkit) on E2E's GPU Cloud | World-class cloud from India | High performance cloud infrastructure | E2E Cloud
  20. ↑ Use CNTK for Inference with an ONNX Model
  21. ↑ 21.0 21.1 21.2 21.3 Introduction to CNTK – Microsoft Cognitive Toolkit
  22. ↑ How to use the Microsoft Cognitive Toolkit with Container Station
  23. ↑ 23.0 23.1 23.2 23.3 Microsoft Solidifies CNTK Deep Learning Toolkit for Industrial-Grade AI – The New Stack
  24. ↑ 24.0 24.1 24.2 24.3 Microsoft/CNTK

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