View source for Software:Comparison of deep learning software

{{short description|None}}
The following table compares notable [[Software framework|software framework]]s, libraries and [[Computer program|computer program]]s for [[Deep learning|deep learning]].
<!-- This is a list of software with a corresponding Wikipedia article -->

==Deep-learning software by name==
{| class="wikitable sortable" style="text-align: center; font-size: 85%; width: auto; table-layout: fixed;"
|-
! style="width: 12em" | Software
! Creator
! Initial release
! Software license{{efn|name="license"|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}}
! Open source
! Platform
! Written in
! Interface
! [[Software:OpenMP|OpenMP]] support
! [[OpenCL]] support
! [[Software:CUDA|CUDA]] support
! [[Automatic differentiation]]<ref name="Automatic differentiation in machin">{{cite arXiv |author1=Atilim Gunes Baydin |author2=Barak A. Pearlmutter |author3=Alexey Andreyevich Radul |author4=Jeffrey Mark Siskind |eprint=1502.05767 |title=Automatic differentiation in machine learning: a survey |class=cs.LG |date=20 February 2015}}</ref>
! Has pretrained models
! [[Recurrent neural network|Recurrent net]]s
! [[Convolutional neural network|Convolutional nets]]
! [[Restricted Boltzmann machine|RBM]]/[[Deep belief network|DBNs]]
! Parallel execution (multi node)
! Actively developed
|-
|[[Software:BigDL|BigDL]]
|Jason Dai (Intel)
|2016
|{{free|[[Software:Apache License|Apache 2.0]]}}
|{{Yes}}
|Apache Spark
|Scala
|Scala, Python
|
|
|{{No}}
|
|{{Yes}}
|{{Yes}}
|{{Yes}}
|
|
|
|-
| [[Software:Caffe|Caffe]]
| Berkeley Vision and Learning Center
| 2013
| {{BSD-lic}}
| {{Yes}}
| [[Software:Linux|Linux]], [[Software:MacOS|macOS]], [[Software:Microsoft Windows|Windows]]<ref>{{cite web|url=https://github.com/Microsoft/caffe|title=Microsoft/caffe|work=GitHub|date=30 October 2021}}</ref>
| [[C++]]
| [[Python (programming language)|Python]], [[Software:MATLAB|MATLAB]], [[C++]]
| {{Yes}}
| {{Depends|Under development<ref>{{Cite web|url=https://github.com/BVLC/caffe|title=Caffe: a fast open framework for deep learning.|date=July 19, 2019|via=GitHub}}</ref>}}
| {{Yes}}
| {{Yes}}
| {{Yes}}<ref>{{Cite web|url=http://caffe.berkeleyvision.org/model_zoo.html|title=Caffe &#124; Model Zoo|website=caffe.berkeleyvision.org}}</ref>
| {{Yes}}
| {{Yes}}
| {{No}}
| {{Dunno}}
|{{No}}<ref>{{Citation|title=GitHub - BVLC/caffe: Caffe: a fast open framework for deep learning.|date=2019-09-25|url=https://github.com/BVLC/caffe|publisher=Berkeley Vision and Learning Center|access-date=2019-09-25}}</ref>
|-
| [[Software:Chainer|Chainer]]
| Preferred Networks
| 2015
|  {{BSD-lic}}
| {{Yes}}
| [[Software:Linux|Linux]], [[Software:MacOS|macOS]]
| [[Python (programming language)|Python]]
| [[Python (programming language)|Python]]
| {{No}}
| {{No}}
| {{Yes}}
| {{Yes}}
| {{Yes}}
| {{Yes}}
| {{Yes}}
| {{No}}
| {{Yes}}
|{{No}}<ref>{{Citation|url=https://preferred.jp/en/news/pr20191205/|title=Preferred Networks Migrates its Deep Learning Research Platform to PyTorch|date=2019-12-05|access-date=2019-12-27}}</ref>
|-
| [[Software:Deeplearning4j|Deeplearning4j]] 
| Skymind engineering team; Deeplearning4j community; originally Adam Gibson
| 2014
| {{Free|Apache 2.0}}
| {{Yes}}
| [[Software:Linux|Linux]], [[Software:MacOS|macOS]], [[Software:Microsoft Windows|Windows]], [[Software:Android (operating system)|Android]] (Cross-platform)
| [[C++]], [[Java (programming language)|Java]]
| [[Java (programming language)|Java]], [[Scala (programming language)|Scala]], Clojure, [[Python (programming language)|Python]] ([[Software:Keras|Keras]]), [[Kotlin (programming language)|Kotlin]]
| {{Yes}}
| {{No}}<ref>{{cite web|url=https://github.com/deeplearning4j/nd4j/issues/27|title=Support for Open CL · Issue #27 · deeplearning4j/nd4j|work=GitHub}}</ref>
| {{Yes}}<ref>{{cite web|url=http://nd4j.org/gpu_native_backends.html|title=N-Dimensional Scientific Computing for Java}}</ref><ref>{{cite web|url=https://deeplearning4j.org/compare-dl4j-tensorflow-pytorch|title=Comparing Top Deep Learning Frameworks|publisher=Deeplearning4j|access-date=2017-10-31|archive-url=https://web.archive.org/web/20171107011631/https://deeplearning4j.org/compare-dl4j-tensorflow-pytorch|archive-date=2017-11-07|url-status=dead}}</ref>
| {{Yes|Computational Graph}}
| {{Yes}}<ref>{{cite web|url=http://deeplearning4j.org/model-zoo|title=Deeplearning4j Models|author1=Chris Nicholson|author2=Adam Gibson|access-date=2016-03-02|archive-url=https://web.archive.org/web/20170211020819/https://deeplearning4j.org/model-zoo|archive-date=2017-02-11|url-status=dead}}</ref>
| {{Yes}}
| {{Yes}}
| {{Yes}}
| {{Yes}}<ref>{{cite web|url=http://deeplearning4j.org/spark|title=Deeplearning4j on Spark|author=Deeplearning4j|publisher=Deeplearning4j|access-date=2016-09-01|archive-url=https://web.archive.org/web/20170713012632/https://deeplearning4j.org/spark|archive-date=2017-07-13|url-status=dead}}</ref>
|Yes
|-
|[[Software:Dlib|Dlib]]
|Davis King
|2002
| {{Free|Boost Software License}}
| {{Yes}}
|Cross-platform
|[[C++]]
|[[C++]], [[Python (programming language)|Python]]
| {{Yes}}
| {{No}}
| {{Yes}}
| {{Yes}}
| {{Yes}}
| {{No}}
| {{Yes}}
| {{Yes}}
| {{Yes}}
|
|-
|[[Software:Flux (machine-learning framework)|Flux]]
|Mike Innes
|2017
| {{Free|MIT license}}
| {{Yes}}
|[[Software:Linux|Linux]], [[Software:MacOS|MacOS]], [[Software:Windows|Windows]] ([[Cross-platform software|Cross-platform]])
|[[Julia (programming language)|Julia]]
|[[Julia (programming language)|Julia]]
|
|| 
| {{Yes}}
| {{Yes}}
| {{Yes}}<ref>{{cite web|url=http://github.com/FluxML/Metalhead.jl|title=Metalhead|date=29 October 2021|publisher=FluxML}}</ref>
| {{Yes}}
| {{Yes}}
| {{No}}
| {{Yes}}
| {{Yes}}
|-
| Intel [[Software:Data Analytics Acceleration Library|Data Analytics Acceleration Library]]
| Intel
| 2015
| {{Free|[[Software:Apache License 2.0|Apache License 2.0]]}}
| {{Yes}}
| [[Software:Linux|Linux]], [[Software:MacOS|macOS]], [[Software:Microsoft Windows|Windows]] on [[Company:Intel|Intel]] [[Central processing unit|CPU]]<ref name="intel-daal">{{Cite web|url=https://software.intel.com/en-us/intel-daal|title=Intel® Data Analytics Acceleration Library (Intel® DAAL)|date=November 20, 2018|website=software.intel.com}}</ref>
| [[C++]], [[Python (programming language)|Python]], [[Java (programming language)|Java]]
| [[C++]], [[Python (programming language)|Python]], [[Java (programming language)|Java]]<ref name="intel-daal"/>
| {{Yes}}
| {{No}}
| {{No}}
| {{Yes}}
| {{No}}
| 
| {{Yes}}
| 
| {{Yes}}
|
|-
| Intel [[Software:Math Kernel Library|Math Kernel Library]] 2017 <ref>{{cite web|title=Intel® Math Kernel Library Release Notes and New Features|url=https://www.intel.com/content/www/us/en/developer/articles/release-notes/intel-math-kernel-library-release-notes-and-new-features.html|website=Intel}}</ref> and later
| Intel
| 2017
| {{Proprietary}}
| {{No}}
| [[Software:Linux|Linux]], [[Software:MacOS|macOS]], [[Software:Microsoft Windows|Windows]] on [[Company:Intel|Intel]] [[Central processing unit|CPU]]<ref>{{Cite web|url=https://software.intel.com/en-us/mkl|title=Intel® Math Kernel Library (Intel® MKL)|date=September 11, 2018|website=software.intel.com}}</ref>
| 
| [[C (programming language)|C]]<ref>{{Cite web|url=https://software.intel.com/en-us/mkl-developer-reference-c-deep-neural-network-functions|title=Deep Neural Network Functions|date=May 24, 2019|website=software.intel.com}}</ref>
| {{Yes}}<ref>{{Cite web|url=https://software.intel.com/en-us/articles/intel-math-kernel-library-intel-mkl-using-intel-mkl-with-threaded-applications|title=Using Intel® MKL with Threaded Applications|date=June 1, 2017|website=software.intel.com}}</ref>
| {{No}}
| {{No}}
| {{Yes}}
| {{No}}
| {{Yes}}<ref name="intel-benchmark">{{Cite web|url=https://software.intel.com/en-us/articles/intel-xeon-phi-delivers-competitive-performance-for-deep-learning-and-getting-better-fast|title=Intel® Xeon Phi™ Delivers Competitive Performance For Deep Learning—And Getting Better Fast|date=March 21, 2019|website=software.intel.com}}</ref>
| {{Yes}}<ref name="intel-benchmark"/>
| 
| {{No}}
|
|-
| [[Software:Keras|Keras]]
| François Chollet
| 2015
| {{Free|MIT license}}
| {{Yes}}
| [[Software:Linux|Linux]], [[Software:MacOS|macOS]], [[Software:Microsoft Windows|Windows]]
| [[Python (programming language)|Python]]
| [[Python (programming language)|Python]], [[R (programming language)|R]]
| {{Depends|Only if using Theano as backend}}
| {{Depends|Can use Theano, Tensorflow or PlaidML as backends}}
| {{Yes}}
| {{Yes}}
| {{Yes}}<ref>{{Cite web|url=https://keras.io/applications/|title=Applications - Keras Documentation|website=keras.io}}</ref>
| {{Yes}}
| {{Yes}}
| {{No}}<ref>{{Cite web|url=https://github.com/keras-team/keras/issues/461|title=Is there RBM in Keras? · Issue #461 · keras-team/keras|website=GitHub}}</ref>
| {{Yes}}<ref>{{Cite web|url=https://github.com/keras-team/keras/issues/2436|title=Does Keras support using multiple GPUs? · Issue #2436 · keras-team/keras|website=GitHub}}</ref>
| {{Yes}}
|- 
| [[Software:MATLAB|MATLAB]] + Deep Learning Toolbox (formally Neural Network Toolbox)
| [[Company:MathWorks|MathWorks]] 
| 1992
| {{Proprietary}} 
| {{No}} 
| [[Software:Linux|Linux]], [[Software:MacOS|macOS]], [[Software:Microsoft Windows|Windows]] 
| [[C (programming language)|C]], [[C++]], [[Java (programming language)|Java]], [[Software:MATLAB|MATLAB]] 
| [[Software:MATLAB|MATLAB]] 
| {{No}} 
| {{No}} 
| {{Yes|Train with Parallel Computing Toolbox and generate CUDA code with GPU Coder}}<ref>{{cite web|title=GPU Coder - MATLAB & Simulink|url=https://www.mathworks.com/products/gpu-coder.html|website=MathWorks|accessdate=13 November 2017}}</ref> 
| {{Yes}}<ref>{{cite web | title=Automatic Differentiation Background - MATLAB & Simulink | website=MathWorks | date=September 3, 2019 | url=https://www.mathworks.com/help/deeplearning/ug/deep-learning-with-automatic-differentiation-in-matlab.html | access-date=November 19, 2019}}</ref> 
| {{Yes}}<ref name="NNT">{{cite web|title=Neural Network Toolbox - MATLAB|url=https://www.mathworks.com/products/neural-network.html|website=MathWorks|accessdate=13 November 2017}}</ref><ref>{{cite web|title=Deep Learning Models - MATLAB & Simulink|url=https://www.mathworks.com/solutions/deep-learning/models.html|website=MathWorks|accessdate=13 November 2017}}</ref> 
| {{Yes}}<ref name="NNT"/> 
| {{Yes}}<ref name="NNT"/> 
| {{Yes}} 
| {{Yes|With Parallel Computing Toolbox}}<ref>{{cite web|title=Parallel Computing Toolbox - MATLAB|url=https://www.mathworks.com/products/parallel-computing.html|website=MathWorks|accessdate=13 November 2017}}</ref>
| {{Yes}}
|-
| [[Software:Microsoft Cognitive Toolkit|Microsoft Cognitive Toolkit]] (CNTK)
| [[Organization:Microsoft Research|Microsoft Research]]
| 2016
| {{Free|MIT license}}<ref>{{cite web|url=https://github.com/Microsoft/CNTK/blob/master/LICENSE.md|title=CNTK/LICENSE.md at master · Microsoft/CNTK · GitHub|work=GitHub}}</ref>
| {{Yes}}
| [[Software:Microsoft Windows|Windows]], [[Software:Linux|Linux]]<ref name="Setup CNTK on your machine">{{cite web|url=https://github.com/Microsoft/CNTK/wiki/Setup-CNTK-on-your-machine|title=Setup CNTK on your machine|work=GitHub}}</ref> ([[Software:MacOS|macOS]] via Docker on roadmap)
| [[C++]]
| [[Python (programming language)|Python]] ([[Software:Keras|Keras]]), [[C++]], Command line,<ref>{{cite web|url=https://github.com/Microsoft/CNTK/wiki/CNTK-usage-overview|title=CNTK usage overview|work=GitHub}}</ref> BrainScript<ref>{{cite web|url=https://github.com/Microsoft/CNTK/wiki/BrainScript-Network-Builder|title=BrainScript Network Builder|work=GitHub}}</ref> (.NET on roadmap<ref>{{cite web|url=https://github.com/Microsoft/CNTK/issues/960|title=.NET Support · Issue #960 · Microsoft/CNTK|work=GitHub}}</ref>)
| {{Yes}}<ref>{{cite web|url=https://github.com/Microsoft/CNTK/issues/59#issuecomment-178104505|title=How to train a model using multiple machines? · Issue #59 · Microsoft/CNTK|work=GitHub}}</ref>
| {{No}}
| {{Yes}}
| {{Yes}}
| {{Yes}}<ref>{{Cite web|url=https://github.com/microsoft/CNTK/issues/140|title=Prebuilt models for image classification · Issue #140 · microsoft/CNTK|website=GitHub}}</ref>
| {{Yes}}<ref name="cntk.ai">{{cite web|url=http://www.cntk.ai/|title=CNTK - Computational Network Toolkit|publisher=Microsoft Corporation}}</ref>
| {{Yes}}<ref name="cntk.ai" />
| {{No}}<ref>url=https://github.com/Microsoft/CNTK/issues/534</ref>
| {{Yes}}<ref>{{cite web|url=https://github.com/Microsoft/CNTK/wiki/Multiple-GPUs-and-machines|title=Multiple GPUs and machines|publisher=Microsoft Corporation}}</ref>
| {{No}}<ref>{{cite web|url=https://github.com/Microsoft/CNTK#disclaimer|title=Disclaimer|date=6 November 2021|publisher=CNTK TEAM}}</ref>
|-
| Apache MXNet
| Apache Software Foundation
| 2015
| {{Free|Apache 2.0}}
| {{Yes}}
| [[Software:Linux|Linux]], [[Software:MacOS|macOS]], [[Software:Microsoft Windows|Windows]],<ref>{{cite web|url=https://github.com/dmlc/mxnet/releases|title=Releases · dmlc/mxnet|work=Github}}</ref><ref>{{cite web|url=https://mxnet.readthedocs.io/en/latest/how_to/build.html#building-on-windows|title=Installation Guide — mxnet documentation|work=Readthdocs}}</ref> [[Company:Amazon Web Services|AWS]], [[Software:Android (operating system)|Android]],<ref>{{cite web|url=https://mxnet.readthedocs.io/en/latest/how_to/smart_device.html|title=MXNet Smart Device|work=ReadTheDocs|access-date=2016-05-19|archive-url=https://web.archive.org/web/20160921205959/http://mxnet.readthedocs.io/en/latest/how_to/smart_device.html|archive-date=2016-09-21|url-status=dead}}</ref> [[Software:IOS|iOS]], [[File manager|JavaScript]]<ref>{{cite web|url=https://github.com/dmlc/mxnet.js|title=MXNet.js|work=Github|date=28 October 2021}}</ref>
| Small [[C++]] core library
| [[C++]], [[Python (programming language)|Python]], [[Julia (programming language)|Julia]], Matlab, [[JavaScript]], [[Go (programming language)|Go]], [[R (programming language)|R]], [[Scala (programming language)|Scala]], Perl, Clojure
| {{Yes}}
| {{Depends|On roadmap}}<ref>{{cite web|url=https://github.com/dmlc/mxnet/issues/621|title=Support for other Device Types, OpenCL AMD GPU · Issue #621 · dmlc/mxnet|work=GitHub}}</ref>
| {{Yes}}
| {{Yes}}<ref>{{Cite web|url=https://mxnet.readthedocs.io/en/latest/|title=— Redirecting to mxnet.io|website=mxnet.readthedocs.io}}</ref>
| {{Yes}}<ref>{{cite web|url=https://github.com/dmlc/mxnet-model-gallery|title=Model Gallery|work=GitHub}}</ref>
| {{Yes}}
| {{Yes}}
| {{Yes}}
| {{Yes}}<ref>{{cite web|url=https://mxnet.readthedocs.io/en/latest/how_to/multi_devices.html|title=Run MXNet on Multiple CPU/GPUs with Data Parallel|work=GitHub}}</ref>
| {{Yes}}
|-
| [[Software:Neural Designer|Neural Designer]]
| Artelnics
| 2014
| {{Proprietary}}
| {{No}}
| [[Software:Linux|Linux]], [[Software:MacOS|macOS]], [[Software:Microsoft Windows|Windows]]
| [[C++]]
| [[Graphical user interface]]
| {{Yes}}
| {{No}}
| {{Yes}}
| {{No|Analytical differentiation}}
| {{No}}
| {{No}}
| {{No}}
| {{No}}
| {{Yes}}
| {{Yes}}
|-
| [[Software:OpenNN|OpenNN]]
| Artelnics
| 2003
|  {{LGPL-lic}}
| {{Yes}}
| Cross-platform
| [[C++]]
| [[C++]]
| {{Yes}}
| {{No}}
| {{Yes}}
| {{Dunno}}
| {{Dunno}}
| {{No}}
| {{No}}
| {{No}}
| {{Dunno}}
|-
| [[Software:PlaidML|PlaidML]]
| Vertex.AI, [[Company:Intel|Intel]]
| 2017
| {{Free|Apache 2.0}}
| {{Yes}}
| [[Software:Linux|Linux]], [[Software:MacOS|macOS]], [[Software:Windows|Windows]]
| [[Python (programming language)|Python]], [[C++]], [[OpenCL]]
| [[Python (programming language)|Python]], [[C++]]
| {{Dunno}}
| {{Yes|Some OpenCL ICDs are not recognized}}
| {{No}}
| {{Yes}}
| {{Yes}}
| {{Yes}}
| {{Yes}}
|
| {{Yes}}
| {{Yes}}
|-
| [[Software:PyTorch|PyTorch]]
| Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan (Facebook)
| 2016
| {{BSD-lic}}
| {{Yes}}
| [[Software:Linux|Linux]], [[Software:MacOS|macOS]], [[Software:Windows|Windows]], [[Software:Android (operating system)|Android]]<ref>{{Cite web|url=https://pytorch.org/mobile/android/|title=PyTorch|date=Dec 17, 2021}}</ref>
| [[Python (programming language)|Python]], [[C (programming language)|C]], [[C++]], [[Software:CUDA|CUDA]]
| [[Python (programming language)|Python]], [[C++]], [[Julia (programming language)|Julia]]
| {{Yes}}
| {{Depends|Via separately maintained package}}<ref>{{Cite web|url=https://github.com/hughperkins/pytorch-coriander|title=OpenCL build of pytorch: (in-progress, not useable) - hughperkins/pytorch-coriander|date=July 14, 2019|via=GitHub}}</ref><ref>{{Cite web|url=https://github.com/artyom-beilis/pytorch_dlprim|title=DLPrimitives/OpenCL out of tree backend for pytorch -  artyom-beilis/pytorch_dlprim|date=Jan 21, 2022|via=GitHub}}</ref><ref name="auto">{{Cite web|url=https://github.com/pytorch/pytorch/issues/488|title=OpenCL Support · Issue #488 · pytorch/pytorch|website=GitHub}}</ref>
| {{Yes}}
| {{Yes}}
| {{Yes}}
| {{Yes}}
| {{Yes}}
| {{Yes}}<ref>{{Cite web|url=https://github.com/GabrielBianconi/pytorch-rbm/blob/master/rbm.py|title=Restricted Boltzmann Machines (RBMs) in PyTorch|website=GitHub}}</ref>
| {{Yes}}
| {{Yes}}
|-
| [[Software:Apache SINGA|Apache SINGA]]
| Apache Software Foundation
| 2015
| {{Free|Apache 2.0}}
| {{Yes}}
| [[Software:Linux|Linux]], [[Software:MacOS|macOS]], [[Software:Microsoft Windows|Windows]]
| [[C++]]
| [[Python (programming language)|Python]], [[C++]], [[Java (programming language)|Java]]
| {{No}}
| {{Depends|Supported in V1.0}}
| {{Yes}}
| {{Dunno}}
| {{Yes}}
| {{Yes}}
| {{Yes}}
| {{Yes}}
| {{Yes}}
|
|-
| [[Software:TensorFlow|TensorFlow]]
| [[Company:Google Brain|Google Brain]]
| 2015
| {{Free|Apache 2.0}}
| {{Yes}}
| [[Software:Linux|Linux]], [[Software:MacOS|macOS]], [[Software:Microsoft Windows|Windows]],<ref name="TensorFlow.org">{{Cite web|url=https://www.tensorflow.org/install/pip|title=Install TensorFlow with pip}}</ref><ref name="developers.googleblog.com">{{Cite web|url=https://developers.googleblog.com/2016/11/tensorflow-0-12-adds-support-for-windows.html|title=TensorFlow 0.12 adds support for Windows}}</ref> [[Software:Android (operating system)|Android]]
| [[C++]], [[Python (programming language)|Python]], [[Software:CUDA|CUDA]]
| [[Python (programming language)|Python]] ([[Software:Keras|Keras]]), [[C (programming language)|C]]/[[C++]], [[Java (programming language)|Java]], [[Go (programming language)|Go]], [[JavaScript]], [[R (programming language)|R]],<ref>{{Citation|last1=interface)|first1=JJ Allaire (R|title=tensorflow: R Interface to TensorFlow|date=2017-05-26|url=https://cran.r-project.org/web/packages/tensorflow/index.html|last2=RStudio|last3=Eddelbuettel|last4=Golding|last5=Tang|last6=Tutorials)|first3=Dirk|first4=Nick|first5=Yuan|first6=Google Inc (Examples and|accessdate=2017-06-14}}</ref> [[Julia (programming language)|Julia]], [[Swift (programming language)|Swift]]
| {{No}}
| {{Depends|On roadmap}}<ref name="tensorflow-roadmap">{{cite web | url=https://github.com/tensorflow/tensorflow/blob/master/tensorflow/docs_src/about/roadmap.md | title=tensorflow/roadmap.md at master · tensorflow/tensorflow · GitHub | work=GitHub | date=January 23, 2017 | access-date=May 21, 2017 }}</ref> but already with [[Software:SYCL|SYCL]]<ref name="GitHub">{{cite web|url=https://github.com/tensorflow/tensorflow/issues/22|title=OpenCL support · Issue #22 · tensorflow/tensorflow|work=GitHub}}</ref> support
| {{Yes}}
| {{Yes}}<ref name="tensorflow.org">{{Cite web|url=https://www.tensorflow.org/|title=TensorFlow|website=TensorFlow}}</ref>
| {{Yes}}<ref name="github.com">{{Cite web|url=https://github.com/tensorflow/models|title=Models and examples built with TensorFlow.|date=July 19, 2019|via=GitHub}}</ref>
| {{Yes}}
| {{Yes}}
| {{Yes}}
| {{Yes}}
| {{Yes}}
|-
| [[Software:Theano|Theano]]
| Université de Montréal
| 2007
| {{BSD-lic}}
| {{Yes}}
| Cross-platform
| [[Python (programming language)|Python]]
| [[Python (programming language)|Python]] ([[Software:Keras|Keras]])
| {{Yes}}
| {{Depends|Under development<ref>{{cite web|url=http://deeplearning.net/software/theano/tutorial/using_gpu.html|title=Using the GPU — Theano 0.8.2 documentation}}</ref>}}
| {{Yes}}
| {{Yes}}<ref>{{Cite web|url=http://deeplearning.net/software/theano/library/gradient.html|title=gradient – Symbolic Differentiation — Theano 1.0.0 documentation|website=deeplearning.net}}</ref><ref>{{Cite web|url=https://groups.google.com/d/msg/theano-users/mln5g2IuBSU/gespG36Lf_QJ|title=Automatic vs. Symbolic differentiation}}</ref>
| {{Depends|Through Lasagne's model zoo<ref>{{cite web|url=https://github.com/Lasagne/Recipes/tree/master/modelzoo|title=Recipes/modelzoo at master · Lasagne/Recipes · GitHub|work=GitHub}}</ref>}}
| {{Yes}}
| {{Yes}}
| {{Yes}}
| {{Yes}}<ref>{{Cite web|url=http://deeplearning.net/software/theano/tutorial/using_multi_gpu.html|title=Using multiple GPUs — Theano 1.0.0 documentation|website=deeplearning.net}}</ref>
| {{No}}
|-
| [[Software:Torch (machine learning)|Torch]]
| Ronan Collobert, Koray Kavukcuoglu, Clement Farabet
| 2002
| {{BSD-lic}}
| {{Yes}}
| [[Software:Linux|Linux]], [[Software:MacOS|macOS]], [[Software:Microsoft Windows|Windows]],<ref name="auto1">{{Cite web|url=https://github.com/torch/torch7|title=torch/torch7|date=July 18, 2019|via=GitHub}}</ref> [[Software:Android (operating system)|Android]],<ref>{{cite web|url=https://github.com/soumith/torch-android|title=GitHub - soumith/torch-android: Torch-7 for Android|work=GitHub|date=13 October 2021}}</ref> [[Software:IOS|iOS]]
| [[C (programming language)|C]], [[Lua (programming language)|Lua]]
| [[Lua (programming language)|Lua]], [[Lua (programming language)|LuaJIT]],<ref>{{Cite web|url=http://ronan.collobert.com/pub/matos/2011_torch7_nipsw.pdf|title=Torch7: A Matlab-like Environment for Machine Learning}}</ref> [[C (programming language)|C]], utility library for [[C++]]/[[OpenCL]]<ref name=jtorch>{{cite web|url=https://github.com/jonathantompson/jtorch|title=GitHub - jonathantompson/jtorch: An OpenCL Torch Utility Library|work=GitHub|date=18 November 2020}}</ref>
| {{Yes}}
| {{Depends|Third party implementations<ref>{{cite web|url=https://github.com/torch/torch7/wiki/Cheatsheet#opencl|title=Cheatsheet|work=GitHub}}</ref><ref>{{cite web|url=https://github.com/hughperkins/distro-cl|title=cltorch|work=GitHub}}</ref>}}
| {{Yes}}<ref>{{cite web|url=https://github.com/torch/cutorch|title=Torch CUDA backend|work=GitHub}}</ref><ref>{{cite web|url=https://github.com/torch/cunn|title=Torch CUDA backend for nn|work=GitHub}}</ref>
| {{Yes|Through [[Company:Twitter|Twitter]]'s Autograd<ref>{{Cite web|url=https://github.com/twitter/torch-autograd|title=Autograd automatically differentiates native Torch code: twitter/torch-autograd|date=July 9, 2019|via=GitHub}}</ref>}}
| {{Yes}}<ref>{{cite web|url=https://github.com/torch/torch7/wiki/ModelZoo|title=ModelZoo|work=GitHub}}</ref>
| {{Yes}}
| {{Yes}}
| {{Yes}}
| {{Yes}}<ref name="auto1"/>
| {{No}}
|-
| [[Software:Wolfram Mathematica|Wolfram Mathematica]] 10<ref>{{cite web|title=Launching Mathematica 10|url=https://blog.wolfram.com/2014/07/09/launching-mathematica-10-with-700-new-functions-and-a-crazy-amount-of-rd|website=Wolfram}}</ref> and later
| [[Company:Wolfram Research|Wolfram Research]]
| 2014
| {{Proprietary}}
| {{No}}
| [[Software:Microsoft Windows|Windows]], [[Software:MacOS|macOS]], [[Software:Linux|Linux]], [[Cloud computing]]
| [[C++]], [[Wolfram Language]], [[Software:CUDA|CUDA]]
| [[Wolfram Language]]
| {{Yes}}
| {{No}}
| {{Yes}}
| {{Yes}}
| {{Yes}}<ref>{{Cite web|url=http://resources.wolframcloud.com/NeuralNetRepository|title=Wolfram Neural Net Repository of Neural Network Models|website=resources.wolframcloud.com}}</ref>
| {{Yes}}
| {{Yes}}
| {{Yes}}
| {{Yes}}<ref>{{Cite web|url=https://reference.wolfram.com/language/guide/ParallelComputing.html.en|title=Parallel Computing—Wolfram Language Documentation|website=reference.wolfram.com}}</ref>
| {{Yes}}
|-
! style="width: 12em" | Software
! Creator
! Initial release
! Software license{{efn|name="license"|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}}
! Open source
! Platform
! Written in
! Interface
! [[Software:OpenMP|OpenMP]] support
! [[OpenCL]] support
! [[Software:CUDA|CUDA]] support
! [[Automatic differentiation]]<ref name="Automatic differentiation in machin"/>
! Has pretrained models
! [[Recurrent neural network|Recurrent net]]s
! [[Convolutional neural network|Convolutional nets]]
! [[Restricted Boltzmann machine|RBM]]/[[Deep belief network|DBNs]]
! Parallel execution (multi node)
! Actively developed
|}

{{notelist}}

== Comparison of compatibility of machine learning models ==
{| class="wikitable"
|+
!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
|-
|[[Software:TensorFlow|TensorFlow]], [[Software:Keras|Keras]], [[Software:Caffe|Caffe]], [[Software:Torch (machine learning)|Torch]], [[Software:Open Neural Network Exchange|ONNX]],
|Algorithm training
| {{No}}
| {{No}} / Separate files in most formats
| {{No}}
| {{No}}
| {{No}}
| {{Yes}}
|-
|[[Software:Open Neural Network Exchange|ONNX]]
|Algorithm training
| {{Yes}}
| {{No}} / Separate files in most formats
| {{No}}
| {{No}}
| {{No}}
| {{Yes}}
|}

==See also==
*[[Software:Comparison of numerical-analysis software|Comparison of numerical-analysis software]]
*[[Software:Comparison of statistical packages|Comparison of statistical packages]]
*[[List of datasets for machine-learning research]]
*[[Software:List of numerical-analysis software|List of numerical-analysis software]]

==References==
{{reflist|33em}}

[[Category:Applied machine learning]]
[[Category:Comparisons of mathematical software|Deep learning frameworks]]
[[Category:Data mining and machine learning software]]
[[Category:Deep learning]]

{{Sourceattribution|Comparison of deep learning software}}