Wikipedia

Comparison of deep learning software

The following table compares notable software frameworks, libraries and computer programs for deep learning.

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 Recurrent nets Convolutional nets RBM/DBNs 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
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
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 License 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
Intel Math Kernel Library 2017 [15] and later Intel 2017 Proprietary No Linux, macOS, Windows on Intel CPU[16] C[17] Yes[18] No No No Yes No Yes[19] Yes[19] No
Google JAX Google 2018 Apache License 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]
ML.NET Microsoft Yes Windows, Linux, macOS 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 ? ? No No No ?
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 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] Yes Via separately maintained package[48][49][50] Yes Yes Yes Yes Yes Yes Yes[51] 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
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 No On roadmap[55] but already with SYCL[56] support Yes Yes Yes[57] Yes[58] Yes Yes Yes Yes Yes
Theano Université de Montréal 2007 BSD Yes Cross-platform Python Python (Keras) Yes Under development[59] Yes No Yes[60][61] Through Lasagne's model zoo[62] Yes Yes Yes Yes[63] No
Torch Ronan Collobert, Koray Kavukcuoglu, Clement Farabet 2002 BSD Yes Linux, macOS, Windows,[64] Android,[65] iOS C, Lua Lua, LuaJIT,[66] C, utility library for C++/OpenCL[67] Yes Third party implementations[68][69] Yes[70][71] No Through Twitter's Autograd[72] Yes[73] Yes Yes Yes Yes[64] No
Wolfram Mathematica 10[74] and later Wolfram Research 2014 Proprietary No Windows, macOS, Linux, Cloud computing C++, Wolfram Language, CUDA Wolfram Language Yes No Yes No Yes Yes[75] Yes Yes Yes Yes[76] Yes
Software Creator Initial release Software license[a] Open source Platform Written in Interface OpenMP support OpenCL support CUDA support ROCm support[77] Automatic differentiation[2] Has pretrained models Recurrent nets Convolutional nets RBM/DBNs 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 compatibility of machine learning models 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].
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  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
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  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.
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  28. ^ "CNTK/LICENSE.md at master · Microsoft/CNTK · GitHub". GitHub.
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  31. ^ "BrainScript Network Builder". GitHub.
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  34. ^ "Prebuilt models for image classification · Issue #140 · microsoft/CNTK". GitHub.
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  38. ^ "Disclaimer". CNTK TEAM. 6 November 2021.
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  41. ^ "MXNet Smart Device". ReadTheDocs. Archived from the original on 2016-09-21. Retrieved 2016-05-19.
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  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.
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  55. ^ "tensorflow/roadmap.md at master · tensorflow/tensorflow · GitHub". GitHub. January 23, 2017. Retrieved May 21, 2017.
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  77. ^ "Deep Learning — ROCm 4.5.0 documentation". Archived from the original on 2022-12-05. Retrieved 2022-09-27.
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