Comparison of deep learning software

Last updated

The following tables compare notable software frameworks, libraries, and computer programs for deep learning applications.

Contents

Deep learning software by name

SoftwareCreatorInitial releaseSoftware license [a]
Open source
PlatformWritten inInterface OpenMP support OpenCL support CUDA support
ROCm support [1]
Automatic differentiation [2] Has pretrained models
Parallel execution
(multi node)
Actively developed
BigDL Jason Dai (Intel)2016 Apache 2.0 YesApache SparkScalaScala, PythonNoNoYesYesYesYes
Caffe Berkeley Vision and Learning Center2013 BSD Yes Linux, macOS, Windows [3] C++ Python, MATLAB, C++ YesUnder development [4] YesNoYesYes [5] YesYesNo ?No [6]
Chainer Preferred Networks2015 BSD Yes Linux, macOS Python Python NoNoYesNoYesYesYesYesNoYesNo [7]
Deeplearning4j Skymind engineering team; Deeplearning4j community; originally Adam Gibson2014 Apache 2.0 Yes Linux, macOS, Windows, Android (Cross-platform) C++, Java Java, Scala, Clojure, Python (Keras), Kotlin YesNo [8] Yes [9] [10] NoComputational GraphYes [11] YesYesYesYes [12] Yes
Dlib Davis King2002 Boost Software License Yes Cross-platform C++ C++, Python YesNoYesNoYesYesNoYesYesYesYes
Flux Mike Innes2017 MIT license Yes Linux, MacOS, Windows (Cross-platform) Julia Julia YesNoYesYes [13] YesYesNoYesYes
Intel Data Analytics Acceleration Library Intel2015 Apache License 2.0 Yes Linux, macOS, Windows on Intel CPU [14] C++, Python, Java C++, Python, Java [14] YesNoNoNoYesNoYesYesYes
Intel Math Kernel Library 2017 [15] and laterIntel2017 Proprietary No Linux, macOS, Windows on Intel CPU [16] C/C++, DPC++, Fortran C [17] Yes [18] NoNoNoYesNoYes [19] Yes [19] NoYes
Google JAX Google2018 Apache License 2.0 Yes Linux, macOS, Windows Python Python Only on LinuxNoYesNoYesYes
Keras François Chollet2015 MIT license Yes Linux, macOS, Windows Python Python, R Only if using Theano as backendCan use Theano, Tensorflow or PlaidML as backendsYesNoYesYes [20] YesYesNo [21] Yes [22] Yes
MATLAB + Deep Learning Toolbox (formally Neural Network Toolbox) MathWorks 1992 Proprietary No Linux, macOS, Windows C, C++, Java, MATLAB MATLAB NoNoTrain with Parallel Computing Toolbox and generate CUDA code with GPU Coder [23] NoYes [24] Yes [25] [26] Yes [25] Yes [25] YesWith Parallel Computing Toolbox [27] Yes
Microsoft Cognitive Toolkit (CNTK) Microsoft Research 2016 MIT license [28] Yes Windows, Linux [29] (macOS via Docker on roadmap) C++ Python (Keras), C++, Command line, [30] BrainScript [31] (.NET on roadmap [32] )Yes [33] NoYesNoYesYes [34] Yes [35] Yes [35] No [36] Yes [37] No [38]
ML.NET Microsoft2018 MIT license YesWindows, Linux, macOSC#, C++C#, F#Yes
Apache MXNet Apache Software Foundation2015 Apache 2.0 Yes Linux, macOS, Windows, [39] [40] AWS, Android, [41] iOS, JavaScript [42] Small C++ core library C++, Python, Julia, MATLAB, JavaScript, Go, R, Scala, Perl, Clojure YesNoYesNoYes [43] Yes [44] YesYesYesYes [45] No
Neural Designer Artelnics2014 Proprietary No Linux, macOS, Windows C++ Graphical user interface YesNoYesNoAnalytical differentiationNoNoNoNoYesYes
OpenNN Artelnics2003 GNU LGPL Yes Cross-platform C++ C++ YesNoYesNo ? ?NoNoNo ?Yes
PlaidML Vertex.AI, Intel 2017 Apache 2.0 Yes Linux, macOS, Windows Python, C++, OpenCL Python, C++  ?Some OpenCL ICDs are not recognizedNoNoYesYesYesYesYesYes
PyTorch Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan (Facebook)2016 BSD Yes Linux, macOS, Windows, Android [46] Python, C, C++, CUDA Python, C++, Julia, R [47] YesVia separately maintained package [48] [49] [50] YesYesYesYesYesYesYes [51] YesYes
Apache SINGA Apache Software Foundation 2015 Apache 2.0 Yes Linux, macOS, Windows C++ Python, C++, Java NoSupported in V1.0YesNo ?YesYesYesYesYesYes
TensorFlow Google Brain 2015 Apache 2.0 Yes Linux, macOS, Windows, [52] [53] Android C++, Python, CUDA Python (Keras), C/C++, Java, Go, JavaScript, R, [54] Julia, Swift NoOn roadmap [55] but already with SYCL [56] supportYesYesYes [57] Yes [58] YesYesYesYesYes
Theano Université de Montréal 2007 BSD Yes Cross-platform Python Python (Keras)YesUnder development [59] YesNoYes [60] [61] Through Lasagne's model zoo [62] YesYesYesYes [63] No
Torch Ronan Collobert, Koray Kavukcuoglu, Clement Farabet2002 BSD Yes Linux, macOS, Windows, [64] Android, [65] iOS C, Lua Lua, LuaJIT, [66] C, utility library for C++/OpenCL [67] YesThird party implementations [68] [69] Yes [70] [71] NoThrough Twitter's Autograd [72] Yes [73] YesYesYesYes [64] No
Wolfram Mathematica 10 [74] and later Wolfram Research 2014 Proprietary No Windows, macOS, Linux, Cloud computing C++, Wolfram Language, CUDA Wolfram Language YesNoYesNoYesYes [75] YesYesYesYes [76] Yes
SoftwareCreatorInitial releaseSoftware license [a]
Open source
PlatformWritten inInterface OpenMP support OpenCL support CUDA support
ROCm support [77]
Automatic differentiation [2] Has pretrained models
Parallel execution
(multi node)
Actively developed
  1. 1 2 Licenses here are a summary, and are not taken to be complete statements of the licenses. Some libraries may use other libraries internally under different licenses

Comparison of machine learning model compatibility

[ further explanation needed ]

Format nameDesign goalCompatible with other formatsSelf-contained DNN ModelPre-processing and Post-processingRun-time configuration for tuning & calibrationDNN model interconnectCommon platform
TensorFlow, Keras, Caffe, Torch Algorithm trainingNoNo / Separate files in most formatsNoNoNoYes
ONNX Algorithm trainingYesNo / Separate files in most formatsNoNoNoYes

See also

References

  1. ↑ "Deep Learning — ROCm 4.5.0 documentation". Archived from the original on 2022-12-05. Retrieved 2022-09-27.
  2. 1 2 Atilim Gunes Baydin; Barak A. Pearlmutter; Alexey Andreyevich Radul; Jeffrey Mark Siskind (20 February 2015). "Automatic differentiation in machine learning: a survey". arXiv: 1502.05767 [cs.LG].
  3. ↑ "Microsoft/caffe". GitHub. 30 October 2021.
  4. ↑ "Caffe: a fast open framework for deep learning". July 19, 2019 – via GitHub.
  5. ↑ "Caffe | Model Zoo". caffe.berkeleyvision.org.
  6. ↑ GitHub - BVLC/caffe: Caffe: a fast open framework for deep learning., Berkeley Vision and Learning Center, 2019-09-25, retrieved 2019-09-25
  7. ↑ Preferred Networks Migrates its Deep Learning Research Platform to PyTorch, 2019-12-05, retrieved 2019-12-27
  8. ↑ "Support for Open CL · Issue #27 · deeplearning4j/nd4j". GitHub.
  9. ↑ "N-Dimensional Scientific Computing for Java". Archived from the original on 2016-10-16. Retrieved 2016-02-05.
  10. ↑ "Comparing Top Deep Learning Frameworks". Deeplearning4j. Archived from the original on 2017-11-07. Retrieved 2017-10-31.
  11. ↑ Chris Nicholson; Adam Gibson. "Deeplearning4j Models". Archived from the original on 2017-02-11. Retrieved 2016-03-02.
  12. ↑ Deeplearning4j. "Deeplearning4j on Spark". Deeplearning4j. Archived from the original on 2017-07-13. Retrieved 2016-09-01.{{cite web}}: CS1 maint: numeric names: authors list (link)
  13. ↑ "Metalhead". FluxML. 29 October 2021.
  14. 1 2 "Intel® Data Analytics Acceleration Library (Intel® DAAL)". software.intel.com. November 20, 2018.
  15. ↑ "Intel® Math Kernel Library Release Notes and New Features". Intel.
  16. ↑ "Intel® Math Kernel Library (Intel® MKL)". software.intel.com. September 11, 2018.
  17. ↑ "Deep Neural Network Functions". software.intel.com. May 24, 2019.
  18. ↑ "Using Intel® MKL with Threaded Applications". software.intel.com. June 1, 2017.
  19. 1 2 "Intel® Xeon Phi™ Delivers Competitive Performance For Deep Learning—And Getting Better Fast". software.intel.com. March 21, 2019.
  20. ↑ "Applications - Keras Documentation". keras.io.
  21. ↑ "Is there RBM in Keras? · Issue #461 · keras-team/keras". GitHub.
  22. ↑ "Does Keras support using multiple GPUs? · Issue #2436 · keras-team/keras". GitHub.
  23. ↑ "GPU Coder - MATLAB & Simulink". MathWorks. Retrieved 13 November 2017.
  24. ↑ "Automatic Differentiation Background - MATLAB & Simulink". MathWorks. September 3, 2019. Retrieved November 19, 2019.
  25. 1 2 3 "Neural Network Toolbox - MATLAB". MathWorks. Retrieved 13 November 2017.
  26. ↑ "Deep Learning Models - MATLAB & Simulink". MathWorks. Retrieved 13 November 2017.
  27. ↑ "Parallel Computing Toolbox - MATLAB". MathWorks. Retrieved 13 November 2017.
  28. ↑ "CNTK/LICENSE.md at master · Microsoft/CNTK". GitHub.
  29. ↑ "Setup CNTK on your machine". GitHub.
  30. ↑ "CNTK usage overview". GitHub.
  31. ↑ "BrainScript Network Builder". GitHub.
  32. ↑ ".NET Support · Issue #960 · Microsoft/CNTK". GitHub.
  33. ↑ "How to train a model using multiple machines? · Issue #59 · Microsoft/CNTK". GitHub.
  34. ↑ "Prebuilt models for image classification · Issue #140 · microsoft/CNTK". GitHub.
  35. 1 2 "CNTK - Computational Network Toolkit". Microsoft Corporation.
  36. ↑ "Restricted Boltzmann Machine with CNTK #534". GitHub, Inc. 27 May 2016. Retrieved 30 October 2023.
  37. ↑ "Multiple GPUs and machines". Microsoft Corporation.
  38. ↑ "Disclaimer". CNTK TEAM. 6 November 2021.
  39. ↑ "Releases · dmlc/mxnet". Github.
  40. ↑ "Installation Guide — mxnet documentation". Readthdocs.
  41. ↑ "MXNet Smart Device". ReadTheDocs. Archived from the original on 2016-09-21. Retrieved 2016-05-19.
  42. ↑ "MXNet.js". Github. 28 October 2021.
  43. ↑ "— Redirecting to mxnet.io". mxnet.readthedocs.io.
  44. ↑ "Model Gallery". GitHub. 29 October 2022.
  45. ↑ "Run MXNet on Multiple CPU/GPUs with Data Parallel". GitHub.
  46. ↑ "PyTorch". Dec 17, 2021.
  47. ↑ "Falbel D, Luraschi J (2023). torch: Tensors and Neural Networks with 'GPU' Acceleration". torch.mlverse.org. Retrieved 2023-11-28.
  48. ↑ "OpenCL build of pytorch: (in-progress, not useable) - hughperkins/pytorch-coriander". July 14, 2019 – via GitHub.
  49. ↑ "DLPrimitives/OpenCL out of tree backend for pytorch - artyom-beilis/pytorch_dlprim". Jan 21, 2022 – via GitHub.
  50. ↑ "OpenCL Support · Issue #488 · pytorch/pytorch". GitHub.
  51. ↑ "Restricted Boltzmann Machines (RBMs) in PyTorch". GitHub. 14 November 2022.
  52. ↑ "Install TensorFlow with pip".
  53. ↑ "TensorFlow 0.12 adds support for Windows".
  54. ↑ Allaire, J.J.; Kalinowski, T.; Falbel, D.; Eddelbuettel, D.; Yuan, T.; Golding, N. (28 September 2023). "tensorflow: R Interface to 'TensorFlow'". The Comprehensive R Archive Network. Retrieved 30 October 2023.
  55. ↑ "tensorflow/roadmap.md at master". GitHub. January 23, 2017. Retrieved May 21, 2017.
  56. ↑ "OpenCL support". GitHub.
  57. ↑ "TensorFlow". TensorFlow.
  58. ↑ "Models and examples built with TensorFlow". July 19, 2019 – via GitHub.
  59. ↑ "Using the GPU: Theano 0.8.2 documentation". Archived from the original on 2017-04-01. Retrieved 2016-01-21.
  60. ↑ "gradient – Symbolic Differentiation — Theano 1.0.0 documentation". deeplearning.net.
  61. ↑ "Automatic vs. Symbolic differentiation".
  62. ↑ "Recipes/modelzoo at master · Lasagne/Recipes". GitHub.
  63. ↑ "Using multiple GPUs — Theano 1.0.0 documentation". deeplearning.net.
  64. 1 2 "torch/torch7". July 18, 2019 – via GitHub.
  65. ↑ "GitHub - soumith/torch-android: Torch-7 for Android". GitHub. 13 October 2021.
  66. ↑ "Torch7: A MATLAB-like Environment for Machine Learning" (PDF).
  67. ↑ "GitHub - jonathantompson/jtorch: An OpenCL Torch Utility Library". GitHub. 18 November 2020.
  68. ↑ "Cheatsheet". GitHub.
  69. ↑ "cltorch". GitHub.
  70. ↑ "Torch CUDA backend". GitHub.
  71. ↑ "Torch CUDA backend for nn". GitHub.
  72. ↑ "Autograd automatically differentiates native Torch code: twitter/torch-autograd". July 9, 2019 – via GitHub.
  73. ↑ "ModelZoo". GitHub.
  74. ↑ "Launching Mathematica 10". Wolfram.
  75. ↑ "Wolfram Neural Net Repository of Neural Network Models". resources.wolframcloud.com.
  76. ↑ "Parallel Computing—Wolfram Language Documentation". reference.wolfram.com.
  77. ↑ "Deep Learning — ROCm 4.5.0 documentation". Archived from the original on 2022-12-05. Retrieved 2022-09-27.
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