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Comparison of deep learning software
Comparison

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

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Deep learning software by name

SoftwareCreatorInitial releaseSoftware license1Open sourcePlatformWritten inInterfaceOpenMP supportOpenCL supportCUDA supportROCm support2Automatic differentiation3Has pretrained modelsRecurrent netsConvolutional netsRBM/DBNsParallel execution(multi node)Actively developed
BigDLJason Dai (Intel)2016Apache 2.0YesApache SparkScalaScala, PythonNoNoYesYesYesYes
CaffeBerkeley Vision and Learning Center2013BSDYesLinux, macOS, Windows4C++Python, MATLAB, C++YesUnder development5YesNoYesYes6YesYesNo?No7
ChainerPreferred Networks2015BSDYesLinux, macOSPythonPythonNoNoYesNoYesYesYesYesNoYesNo8
Deeplearning4jSkymind engineering team; Deeplearning4j community; originally Adam Gibson2014Apache 2.0YesLinux, macOS, Windows, Android (Cross-platform)C++, JavaJava, Scala, Clojure, Python (Keras), KotlinYesNo9Yes1011NoComputational GraphYes12YesYesYesYes13Yes
DlibDavis King2002Boost Software LicenseYesCross-platformC++C++, PythonYesNoYesNoYesYesNoYesYesYesYes
FluxMike Innes2017MIT licenseYesLinux, MacOS, Windows (Cross-platform)JuliaJuliaYesNoYesYes14YesYesNoYesYes
Intel Data Analytics Acceleration LibraryIntel2015Apache License 2.0YesLinux, macOS, Windows on Intel CPU15C++, Python, JavaC++, Python, Java16YesNoNoNoYesNoYesYesYes
Intel Math Kernel Library 2017 17 and laterIntel2017ProprietaryNoLinux, macOS, Windows on Intel CPU18C/C++, DPC++, FortranC19Yes20NoNoNoYesNoYes21Yes22NoYes
Google JAXGoogle2018Apache License 2.0YesLinux, macOS, WindowsPythonPythonOnly on LinuxNoYesNoYesYes
KerasFrançois Chollet2015MIT licenseYesLinux, macOS, WindowsPythonPython, ROnly if using Theano as backendCan use Theano, Tensorflow or PlaidML as backendsYesNoYesYes23YesYesNo24Yes25Yes
MATLAB + Deep Learning Toolbox (formally Neural Network Toolbox)MathWorks1992ProprietaryNoLinux, macOS, WindowsC, C++, Java, MATLABMATLABNoNoTrain with Parallel Computing Toolbox and generate CUDA code with GPU Coder26NoYes27Yes2829Yes30Yes31YesWith Parallel Computing Toolbox32Yes
Microsoft Cognitive Toolkit (CNTK)Microsoft Research2016MIT license33YesWindows, Linux34 (macOS via Docker on roadmap)C++Python (Keras), C++, Command line,35 BrainScript36 (.NET on roadmap37)Yes38NoYesNoYesYes39Yes40Yes41No42Yes43No44
ML.NETMicrosoft2018MIT licenseYesWindows, Linux, macOSC#, C++C#, F#Yes
Apache MXNetApache Software Foundation2015Apache 2.0YesLinux, macOS, Windows,4546 AWS, Android,47 iOS, JavaScript48Small C++ core libraryC++, Python, Julia, MATLAB, JavaScript, Go, R, Scala, Perl, ClojureYesNoYesNoYes49Yes50YesYesYesYes51No
Neural DesignerArtelnics2014ProprietaryNoLinux, macOS, WindowsC++Graphical user interfaceYesNoYesNoAnalytical differentiationNoNoNoNoYesYes
OpenNNArtelnics2003GNU LGPLYesCross-platformC++C++YesNoYesNo??NoNoNo?Yes
PlaidMLVertex.AI, Intel2017Apache 2.0YesLinux, macOS, WindowsPython, C++, OpenCLPython, C++?Some OpenCL ICDs are not recognizedNoNoYesYesYesYesYesYes
PyTorchAdam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan (Facebook)2016BSDYesLinux, macOS, Windows, Android52Python, C, C++, CUDAPython, C++, Julia, R53YesVia separately maintained package545556YesYesYesYesYesYesYes57YesYes
Apache SINGAApache Software Foundation2015Apache 2.0YesLinux, macOS, WindowsC++Python, C++, JavaNoSupported in V1.0YesNo?YesYesYesYesYesYes
TensorFlowGoogle Brain2015Apache 2.0YesLinux, macOS, Windows,5859 AndroidC++, Python, CUDAPython (Keras), C/C++, Java, Go, JavaScript, R,60 Julia, SwiftNoOn roadmap61 but already with SYCL62 supportYesYesYes63Yes64YesYesYesYesYes
TheanoUniversité de Montréal2007BSDYesCross-platformPythonPython (Keras)YesUnder development65YesNoYes6667Through Lasagne's model zoo68YesYesYesYes69No
TorchRonan Collobert, Koray Kavukcuoglu, Clement Farabet2002BSDYesLinux, macOS, Windows,70 Android,71 iOSC, LuaLua, LuaJIT,72 C, utility library for C++/OpenCL73YesThird party implementations7475Yes7677NoThrough Twitter's Autograd78Yes79YesYesYesYes80No
Wolfram Mathematica 1081 and laterWolfram Research2014ProprietaryNoWindows, macOS, Linux, Cloud computingC++, Wolfram Language, CUDAWolfram LanguageYesNoYesNoYesYes82YesYesYesYes83Yes
SoftwareCreatorInitial releaseSoftware license84Open sourcePlatformWritten inInterfaceOpenMP supportOpenCL supportCUDA supportROCm support85Automatic differentiation86Has pretrained modelsRecurrent netsConvolutional netsRBM/DBNsParallel execution(multi node)Actively developed

Comparison of machine learning model compatibility

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

See also

References

  1. 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 ↩

  2. "Deep Learning — ROCm 4.5.0 documentation". Archived from the original on 2022-12-05. Retrieved 2022-09-27. https://web.archive.org/web/20221205102733/https://rocmdocs.amd.com/en/latest/Deep_learning/Deep-learning.html ↩

  3. 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]. /wiki/ArXiv_(identifier) ↩

  4. "Microsoft/caffe". GitHub. 30 October 2021. https://github.com/Microsoft/caffe ↩

  5. "Caffe: a fast open framework for deep learning". July 19, 2019 – via GitHub. https://github.com/BVLC/caffe ↩

  6. "Caffe | Model Zoo". caffe.berkeleyvision.org. http://caffe.berkeleyvision.org/model_zoo.html ↩

  7. GitHub - BVLC/caffe: Caffe: a fast open framework for deep learning., Berkeley Vision and Learning Center, 2019-09-25, retrieved 2019-09-25 https://github.com/BVLC/caffe ↩

  8. Preferred Networks Migrates its Deep Learning Research Platform to PyTorch, 2019-12-05, retrieved 2019-12-27 https://preferred.jp/en/news/pr20191205/ ↩

  9. "Support for Open CL · Issue #27 · deeplearning4j/nd4j". GitHub. https://github.com/deeplearning4j/nd4j/issues/27 ↩

  10. "N-Dimensional Scientific Computing for Java". Archived from the original on 2016-10-16. Retrieved 2016-02-05. https://web.archive.org/web/20161016094035/http://nd4j.org/gpu_native_backends.html ↩

  11. "Comparing Top Deep Learning Frameworks". Deeplearning4j. Archived from the original on 2017-11-07. Retrieved 2017-10-31. https://web.archive.org/web/20171107011631/https://deeplearning4j.org/compare-dl4j-tensorflow-pytorch ↩

  12. Chris Nicholson; Adam Gibson. "Deeplearning4j Models". Archived from the original on 2017-02-11. Retrieved 2016-03-02. https://web.archive.org/web/20170211020819/https://deeplearning4j.org/model-zoo ↩

  13. 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) https://web.archive.org/web/20170713012632/https://deeplearning4j.org/spark ↩

  14. "Metalhead". FluxML. 29 October 2021. https://github.com/FluxML/Metalhead.jl ↩

  15. "Intel® Data Analytics Acceleration Library (Intel® DAAL)". software.intel.com. November 20, 2018. https://software.intel.com/en-us/intel-daal ↩

  16. "Intel® Data Analytics Acceleration Library (Intel® DAAL)". software.intel.com. November 20, 2018. https://software.intel.com/en-us/intel-daal ↩

  17. "Intel® Math Kernel Library Release Notes and New Features". Intel. https://www.intel.com/content/www/us/en/developer/articles/release-notes/intel-math-kernel-library-release-notes-and-new-features.html ↩

  18. "Intel® Math Kernel Library (Intel® MKL)". software.intel.com. September 11, 2018. https://software.intel.com/en-us/mkl ↩

  19. "Deep Neural Network Functions". software.intel.com. May 24, 2019. https://software.intel.com/en-us/mkl-developer-reference-c-deep-neural-network-functions ↩

  20. "Using Intel® MKL with Threaded Applications". software.intel.com. June 1, 2017. https://software.intel.com/en-us/articles/intel-math-kernel-library-intel-mkl-using-intel-mkl-with-threaded-applications ↩

  21. "Intel® Xeon Phi™ Delivers Competitive Performance For Deep Learning—And Getting Better Fast". software.intel.com. March 21, 2019. https://software.intel.com/en-us/articles/intel-xeon-phi-delivers-competitive-performance-for-deep-learning-and-getting-better-fast ↩

  22. "Intel® Xeon Phi™ Delivers Competitive Performance For Deep Learning—And Getting Better Fast". software.intel.com. March 21, 2019. https://software.intel.com/en-us/articles/intel-xeon-phi-delivers-competitive-performance-for-deep-learning-and-getting-better-fast ↩

  23. "Applications - Keras Documentation". keras.io. https://keras.io/applications/ ↩

  24. "Is there RBM in Keras? · Issue #461 · keras-team/keras". GitHub. https://github.com/keras-team/keras/issues/461 ↩

  25. "Does Keras support using multiple GPUs? · Issue #2436 · keras-team/keras". GitHub. https://github.com/keras-team/keras/issues/2436 ↩

  26. "GPU Coder - MATLAB & Simulink". MathWorks. Retrieved 13 November 2017. https://www.mathworks.com/products/gpu-coder.html ↩

  27. "Automatic Differentiation Background - MATLAB & Simulink". MathWorks. September 3, 2019. Retrieved November 19, 2019. https://www.mathworks.com/help/deeplearning/ug/deep-learning-with-automatic-differentiation-in-matlab.html ↩

  28. "Neural Network Toolbox - MATLAB". MathWorks. Retrieved 13 November 2017. https://www.mathworks.com/products/neural-network.html ↩

  29. "Deep Learning Models - MATLAB & Simulink". MathWorks. Retrieved 13 November 2017. https://www.mathworks.com/solutions/deep-learning/models.html ↩

  30. "Neural Network Toolbox - MATLAB". MathWorks. Retrieved 13 November 2017. https://www.mathworks.com/products/neural-network.html ↩

  31. "Neural Network Toolbox - MATLAB". MathWorks. Retrieved 13 November 2017. https://www.mathworks.com/products/neural-network.html ↩

  32. "Parallel Computing Toolbox - MATLAB". MathWorks. Retrieved 13 November 2017. https://www.mathworks.com/products/parallel-computing.html ↩

  33. "CNTK/LICENSE.md at master · Microsoft/CNTK". GitHub. https://github.com/Microsoft/CNTK/blob/master/LICENSE.md ↩

  34. "Setup CNTK on your machine". GitHub. https://github.com/Microsoft/CNTK/wiki/Setup-CNTK-on-your-machine ↩

  35. "CNTK usage overview". GitHub. https://github.com/Microsoft/CNTK/wiki/CNTK-usage-overview ↩

  36. "BrainScript Network Builder". GitHub. https://github.com/Microsoft/CNTK/wiki/BrainScript-Network-Builder ↩

  37. ".NET Support · Issue #960 · Microsoft/CNTK". GitHub. https://github.com/Microsoft/CNTK/issues/960 ↩

  38. "How to train a model using multiple machines? · Issue #59 · Microsoft/CNTK". GitHub. https://github.com/Microsoft/CNTK/issues/59#issuecomment-178104505 ↩

  39. "Prebuilt models for image classification · Issue #140 · microsoft/CNTK". GitHub. https://github.com/microsoft/CNTK/issues/140 ↩

  40. "CNTK - Computational Network Toolkit". Microsoft Corporation. http://www.cntk.ai/ ↩

  41. "CNTK - Computational Network Toolkit". Microsoft Corporation. http://www.cntk.ai/ ↩

  42. "Restricted Boltzmann Machine with CNTK #534". GitHub, Inc. 27 May 2016. Retrieved 30 October 2023. https://github.com/Microsoft/CNTK/issues/534 ↩

  43. "Multiple GPUs and machines". Microsoft Corporation. https://github.com/Microsoft/CNTK/wiki/Multiple-GPUs-and-machines ↩

  44. "Disclaimer". CNTK TEAM. 6 November 2021. https://github.com/Microsoft/CNTK#disclaimer ↩

  45. "Releases · dmlc/mxnet". Github. https://github.com/dmlc/mxnet/releases ↩

  46. "Installation Guide — mxnet documentation". Readthdocs. https://mxnet.readthedocs.io/en/latest/how_to/build.html#building-on-windows ↩

  47. "MXNet Smart Device". ReadTheDocs. Archived from the original on 2016-09-21. Retrieved 2016-05-19. https://web.archive.org/web/20160921205959/http://mxnet.readthedocs.io/en/latest/how_to/smart_device.html ↩

  48. "MXNet.js". Github. 28 October 2021. https://github.com/dmlc/mxnet.js ↩

  49. "— Redirecting to mxnet.io". mxnet.readthedocs.io. https://mxnet.readthedocs.io/en/latest/ ↩

  50. "Model Gallery". GitHub. 29 October 2022. https://github.com/dmlc/mxnet-model-gallery ↩

  51. "Run MXNet on Multiple CPU/GPUs with Data Parallel". GitHub. https://mxnet.readthedocs.io/en/latest/how_to/multi_devices.html ↩

  52. "PyTorch". Dec 17, 2021. https://pytorch.org/mobile/android/ ↩

  53. "Falbel D, Luraschi J (2023). torch: Tensors and Neural Networks with 'GPU' Acceleration". torch.mlverse.org. Retrieved 2023-11-28. https://torch.mlverse.org/ ↩

  54. "OpenCL build of pytorch: (in-progress, not useable) - hughperkins/pytorch-coriander". July 14, 2019 – via GitHub. https://github.com/hughperkins/pytorch-coriander ↩

  55. "DLPrimitives/OpenCL out of tree backend for pytorch - artyom-beilis/pytorch_dlprim". Jan 21, 2022 – via GitHub. https://github.com/artyom-beilis/pytorch_dlprim ↩

  56. "OpenCL Support · Issue #488 · pytorch/pytorch". GitHub. https://github.com/pytorch/pytorch/issues/488 ↩

  57. "Restricted Boltzmann Machines (RBMs) in PyTorch". GitHub. 14 November 2022. https://github.com/GabrielBianconi/pytorch-rbm/blob/master/rbm.py ↩

  58. "Install TensorFlow with pip". https://www.tensorflow.org/install/pip ↩

  59. "TensorFlow 0.12 adds support for Windows". https://developers.googleblog.com/2016/11/tensorflow-0-12-adds-support-for-windows.html ↩

  60. 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. https://cran.r-project.org/web/packages/tensorflow/ ↩

  61. "tensorflow/roadmap.md at master". GitHub. January 23, 2017. Retrieved May 21, 2017. https://github.com/tensorflow/tensorflow/blob/master/tensorflow/docs_src/about/roadmap.md ↩

  62. "OpenCL support". GitHub. https://github.com/tensorflow/tensorflow/issues/22 ↩

  63. "TensorFlow". TensorFlow. https://www.tensorflow.org/ ↩

  64. "Models and examples built with TensorFlow". July 19, 2019 – via GitHub. https://github.com/tensorflow/models ↩

  65. "Using the GPU: Theano 0.8.2 documentation". Archived from the original on 2017-04-01. Retrieved 2016-01-21. https://web.archive.org/web/20170401163303/http://deeplearning.net/software/theano/tutorial/using_gpu.html ↩

  66. "gradient – Symbolic Differentiation — Theano 1.0.0 documentation". deeplearning.net. http://deeplearning.net/software/theano/library/gradient.html ↩

  67. "Automatic vs. Symbolic differentiation". https://groups.google.com/d/msg/theano-users/mln5g2IuBSU/gespG36Lf_QJ ↩

  68. "Recipes/modelzoo at master · Lasagne/Recipes". GitHub. https://github.com/Lasagne/Recipes/tree/master/modelzoo ↩

  69. "Using multiple GPUs — Theano 1.0.0 documentation". deeplearning.net. http://deeplearning.net/software/theano/tutorial/using_multi_gpu.html ↩

  70. "torch/torch7". July 18, 2019 – via GitHub. https://github.com/torch/torch7 ↩

  71. "GitHub - soumith/torch-android: Torch-7 for Android". GitHub. 13 October 2021. https://github.com/soumith/torch-android ↩

  72. "Torch7: A MATLAB-like Environment for Machine Learning" (PDF). http://ronan.collobert.com/pub/matos/2011_torch7_nipsw.pdf ↩

  73. "GitHub - jonathantompson/jtorch: An OpenCL Torch Utility Library". GitHub. 18 November 2020. https://github.com/jonathantompson/jtorch ↩

  74. "Cheatsheet". GitHub. https://github.com/torch/torch7/wiki/Cheatsheet#opencl ↩

  75. "cltorch". GitHub. https://github.com/hughperkins/distro-cl ↩

  76. "Torch CUDA backend". GitHub. https://github.com/torch/cutorch ↩

  77. "Torch CUDA backend for nn". GitHub. https://github.com/torch/cunn ↩

  78. "Autograd automatically differentiates native Torch code: twitter/torch-autograd". July 9, 2019 – via GitHub. https://github.com/twitter/torch-autograd ↩

  79. "ModelZoo". GitHub. https://github.com/torch/torch7/wiki/ModelZoo ↩

  80. "torch/torch7". July 18, 2019 – via GitHub. https://github.com/torch/torch7 ↩

  81. "Launching Mathematica 10". Wolfram. https://blog.wolfram.com/2014/07/09/launching-mathematica-10-with-700-new-functions-and-a-crazy-amount-of-rd ↩

  82. "Wolfram Neural Net Repository of Neural Network Models". resources.wolframcloud.com. http://resources.wolframcloud.com/NeuralNetRepository ↩

  83. "Parallel Computing—Wolfram Language Documentation". reference.wolfram.com. https://reference.wolfram.com/language/guide/ParallelComputing.html.en ↩

  84. 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 ↩

  85. "Deep Learning — ROCm 4.5.0 documentation". Archived from the original on 2022-12-05. Retrieved 2022-09-27. https://web.archive.org/web/20221205102733/https://rocmdocs.amd.com/en/latest/Deep_learning/Deep-learning.html ↩

  86. 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]. /wiki/ArXiv_(identifier) ↩