mic_none

Comparison of deep-learning software Source: en.wikipedia.org/wiki/Comparison_of_deep-learning_software

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

Deep learning software by name

[edit]
Software Creator Initial release Software license[a]
Open source
 Platform Written in Interface 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 Yes Apache Spark Scala Scala, Python No No Yes Yes Yes Yes
Caffe Berkeley Vision and Learning Center 2013 BSD Yes Linux, macOS, Windows[3] C++ Python, MATLAB, C++ Yes Under development[4] Yes No Yes Yes[5] Yes Yes No ? No[6]
Chainer Preferred Networks 2015 BSD Yes Linux, macOS Python Python No No Yes No Yes Yes Yes Yes No Yes No[7]
Deeplearning4j Skymind engineering team; Deeplearning4j community; originally Adam Gibson 2014 Apache 2.0 Yes Linux, macOS, Windows, Android (Cross-platform) C++, Java Java, Scala, Clojure, Python (Keras), Kotlin Yes No[8] Yes[9][10] No Computational Graph Yes[11] Yes Yes Yes Yes[12] Yes
Dlib Davis King 2002 Boost Software License Yes Cross-platform C++ C++, Python Yes No Yes No Yes Yes No Yes Yes Yes Yes
Flux Mike Innes 2017 MIT license Yes Linux, MacOS, Windows (Cross-platform) Julia Julia Yes No Yes Yes[13] Yes Yes No Yes Yes
Intel Data Analytics Acceleration Library Intel 2015 Apache 2.0 Yes Linux, macOS, Windows on Intel CPU[14] C++, Python, Java C++, Python, Java[14] Yes No No No Yes No Yes Yes Yes
Intel Math Kernel Library 2017 [15] and later Intel 2017 Proprietary No Linux, macOS, Windows on Intel CPU[16] C/C++, DPC++, Fortran C[17] Yes[18] No No No Yes No Yes[19] Yes[19] No Yes
Google JAX Google 2018 Apache 2.0 Yes Linux, macOS, Windows Python Python Only on Linux No Yes No Yes Yes
Keras François Chollet 2015 MIT license Yes Linux, macOS, Windows Python Python, R Only if using Theano as backend Can use Theano, Tensorflow or PlaidML as backends Yes No Yes Yes[20] Yes Yes No[21] Yes[22] Yes
MATLAB + Deep Learning Toolbox (formally Neural Network Toolbox) MathWorks 1992 Proprietary No Linux, macOS, Windows C, C++, Java, MATLAB MATLAB No No Train with Parallel Computing Toolbox and generate CUDA code with GPU Coder[23] No Yes[24] Yes[25][26] Yes[25] Yes[25] Yes With 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] No Yes No Yes Yes[34] Yes[35] Yes[35] No[36] Yes[37] No[38]
MindSpore Huawei 2020 Apache 2.0 Yes Linux, Windows, macOS, EulerOS, openEuler, OpenHarmony, Oniro OS, HarmonyOS, Android C++, Rust, Julia, Python, ArkTS, Cangjie, Java (Lite)
ML.NET Microsoft 2018 MIT license Yes Windows, Linux, macOS C#, C++ C#, F# Yes
Apache MXNet Apache Software Foundation 2015 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 Yes No Yes No Yes[43] Yes[44] Yes Yes Yes Yes[45] No
Neural Designer Artelnics 2014 Proprietary No Linux, macOS, Windows C++ Graphical user interface Yes No Yes No Analytical differentiation No No No No Yes Yes
OpenNN Artelnics 2003 GNU LGPL Yes Cross-platform C++ C++ Yes No Yes No ? Yes[46] No No No ? Yes
PlaidML Vertex.AI, Intel 2017 Apache 2.0 Yes Linux, macOS, Windows Python, C++, OpenCL Python, C++ ? Some OpenCL ICDs are not recognized No No Yes Yes Yes Yes Yes Yes
PyTorch Meta AI 2016 BSD Yes Linux, macOS, Windows, Android[47] Python, C, C++, CUDA Python, C++, Julia, R[48] Yes Via separately maintained package[49][50][51] Yes Yes Yes Yes Yes Yes Yes[52] Yes Yes
Apache SINGA Apache Software Foundation 2015 Apache 2.0 Yes Linux, macOS, Windows C++ Python, C++, Java No Supported in V1.0 Yes No ? Yes Yes Yes Yes Yes Yes
TensorFlow Google Brain 2015 Apache 2.0 Yes Linux, macOS, Windows,[53][54] Android C++, Python, CUDA Python (Keras), C/C++, Java, Go, JavaScript, R,[55] Julia, Swift No On roadmap[56] but already with SYCL[57] support Yes Yes Yes[58] Yes[59] Yes Yes Yes Yes Yes
Theano Université de Montréal 2007 BSD Yes Cross-platform Python Python (Keras) Yes Under development[60] Yes No Yes[61][62] Through Lasagne's model zoo[63] Yes Yes Yes Yes[64] No
Torch Ronan Collobert, Koray Kavukcuoglu, Clement Farabet 2002 BSD Yes Linux, macOS, Windows,[65] Android,[66] iOS C, Lua Lua, LuaJIT,[67] C, utility library for C++/OpenCL[68] Yes Third party implementations[69][70] Yes[71][72] No Through Twitter's Autograd[73] Yes[74] Yes Yes Yes Yes[65] No
Wolfram Mathematica 10[75] and later Wolfram Research 2014 Proprietary No Windows, macOS, Linux, Cloud computing C++, Wolfram Language, CUDA Wolfram Language Yes No Yes No Yes Yes[76] Yes Yes Yes Yes[77] Yes
Software Creator Initial release Software license[a]
Open source
 Platform Written in Interface OpenMP support OpenCL support CUDA support
ROCm support[78]
 Automatic differentiation[2] Has pretrained models
Parallel execution
(multi node)
Actively developed
  1. ^ a b 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

[edit]

[further explanation needed]

Format name Design goal Compatible with other formats Self-contained DNN Model Pre-processing and Post-processing Run-time configuration for tuning & calibration DNN model interconnect Common platform
TensorFlow, Keras, Caffe, Torch Algorithm training No No / Separate files in most formats No No No Yes
ONNX Algorithm training Yes No / Separate files in most formats No No No Yes

See also

[edit]

References

[edit]
  1. ^ "Deep Learning — ROCm 4.5.0 documentation". Archived from the original on 2022-12-05. Retrieved 2022-09-27.
  2. ^ a b 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. ^ a b "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. ^ a b "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. ^ a b c "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. ^ a b "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. ^ "Model Types".
  47. ^ "PyTorch". Dec 17, 2021.
  48. ^ "Falbel D, Luraschi J (2023). torch: Tensors and Neural Networks with 'GPU' Acceleration". torch.mlverse.org. Retrieved 2023-11-28.
  49. ^ "OpenCL build of pytorch: (in-progress, not useable) - hughperkins/pytorch-coriander". July 14, 2019 – via GitHub.
  50. ^ "DLPrimitives/OpenCL out of tree backend for pytorch - artyom-beilis/pytorch_dlprim". Jan 21, 2022 – via GitHub.
  51. ^ "OpenCL Support · Issue #488 · pytorch/pytorch". GitHub.
  52. ^ "Restricted Boltzmann Machines (RBMs) in PyTorch". GitHub. 14 November 2022.
  53. ^ "Install TensorFlow with pip".
  54. ^ "TensorFlow 0.12 adds support for Windows".
  55. ^ 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.
  56. ^ "tensorflow/roadmap.md at master". GitHub. January 23, 2017. Retrieved May 21, 2017.
  57. ^ "OpenCL support". GitHub.
  58. ^ "TensorFlow". TensorFlow.
  59. ^ "Models and examples built with TensorFlow". July 19, 2019 – via GitHub.
  60. ^ "Using the GPU: Theano 0.8.2 documentation". Archived from the original on 2017-04-01. Retrieved 2016-01-21.
  61. ^ "gradient – Symbolic Differentiation — Theano 1.0.0 documentation". deeplearning.net.
  62. ^ "Automatic vs. Symbolic differentiation".
  63. ^ "Recipes/modelzoo at master · Lasagne/Recipes". GitHub.
  64. ^ "Using multiple GPUs — Theano 1.0.0 documentation". deeplearning.net.
  65. ^ a b "torch/torch7". July 18, 2019 – via GitHub.
  66. ^ "GitHub - soumith/torch-android: Torch-7 for Android". GitHub. 13 October 2021.
  67. ^ "Torch7: A MATLAB-like Environment for Machine Learning" (PDF).
  68. ^ "GitHub - jonathantompson/jtorch: An OpenCL Torch Utility Library". GitHub. 18 November 2020.
  69. ^ "Cheatsheet". GitHub.
  70. ^ "cltorch". GitHub.
  71. ^ "Torch CUDA backend". GitHub.
  72. ^ "Torch CUDA backend for nn". GitHub.
  73. ^ "Autograd automatically differentiates native Torch code: twitter/torch-autograd". July 9, 2019 – via GitHub.
  74. ^ "ModelZoo". GitHub.
  75. ^ "Launching Mathematica 10". Wolfram.
  76. ^ "Wolfram Neural Net Repository of Neural Network Models". resources.wolframcloud.com.
  77. ^ "Parallel Computing—Wolfram Language Documentation". reference.wolfram.com.
  78. ^ "Deep Learning — ROCm 4.5.0 documentation". Archived from the original on 2022-12-05. Retrieved 2022-09-27.