Article · Wikipedia archive · Last revised May 28, 2026

Comparison of deep learning software

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

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May 28, 2026
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The following tables compare notable software frameworks, libraries, and computer programs for deep learning applications.

Deep learning software by name

Software Creator Initial release Software licensea
Open source
 Platform Written in Interface OpenMP support OpenCL support CUDA support
ROCm support1
 Automatic differentiation2 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, Windows3 C++ Python, MATLAB, C++ Yes Under development4 Yes No Yes Yes5 Yes Yes No ? No6
Chainer Preferred Networks 2015 BSD Yes Linux, macOS Python Python No No Yes No Yes Yes Yes Yes No Yes No7
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 No8 Yes910 No Computational Graph Yes11 Yes Yes Yes Yes12 Yes
DeepSpeed Microsoft 2019 Apache 2.0 Yes Linux, macOS, Windows Python, C++, CUDA Python No No Yes No Yes Yes Yes Yes No Yes 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
Fastai fast.ai 2018 Apache 2.0 Yes Linux, macOS, Windows Python, CUDA Python No No Yes No Yes Yes Yes Yes No Yes Yes
Flux Mike Innes 2017 MIT Yes Linux, macOS, Windows (Cross-platform) Julia Julia Yes No Yes Yes13 Yes Yes No Yes Yes
Horovod Uber Technologies 2017 Apache 2.0 Yes Linux, macOS, Windows Python, C++, CUDA Python No No Yes No Yes Yes Yes Yes No Yes Yes
Intel Data Analytics Acceleration Library Intel 2015 Apache 2.0 Yes Linux, macOS, Windows on Intel CPU14 C++, Python, Java C++, Python, Java14 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 CPU16 C/C++, DPC++, Fortran C17 Yes18 No No No Yes No Yes19 Yes19 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 Yes Linux, macOS, Windows Python Python, R Only if using Theano as backend Can use Theano, Tensorflow or PlaidML as backends Yes No Yes Yes20 Yes Yes No21 Yes22 Yes
MATLAB + Deep Learning Toolbox (formerly 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 Coder23 No Yes24 Yes2526 Yes25 Yes25 Yes With Parallel Computing Toolbox27 Yes
Microsoft Cognitive Toolkit (CNTK) Microsoft Research 2016 MIT28 Yes Windows, Linux29 (macOS via Docker on roadmap) C++ Python (Keras), C++, Command line,30 BrainScript31 (.NET on roadmap32) Yes33 No Yes No Yes Yes34 Yes35 Yes35 No36 Yes37 No38
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 Yes Windows, Linux, macOS C#, C++ C#, F# Yes
Apache MXNet Apache Software Foundation 2015 Apache 2.0 Yes Linux, macOS, Windows,3940 AWS, Android,41 iOS, JavaScript42 Small C++ core library C++, Python, Julia, MATLAB, JavaScript, Go, R, Scala, Perl, Clojure Yes No Yes No Yes43 Yes44 Yes Yes Yes Yes45 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 ? Yes46 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, Android47 Python, C, C++, CUDA Python, C++, Julia, R48 Yes Via separately maintained package495051 Yes Yes Yes Yes Yes Yes Yes52 Yes Yes
PyTorch Lightning Lightning-AI (originally William Falcon)53 2019 Apache 2.0 Yes Linux, macOS, Windows Python Python Yes Via PyTorch Yes Yes Yes Yes Yes Yes Yes Yes54 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,5556 Android C++, Python, CUDA Python (Keras), C/C++, Java, Go, JavaScript, R,57 Julia, Swift No On roadmap58 but already with SYCL59 support Yes Yes Yes60 Yes61 Yes Yes Yes Yes Yes
TensorRT Nvidia 2017 Proprietary No Linux, Windows on Nvidia GPUs C++, Python, CUDA C++, Python No No Yes No No No No Yes No No Yes
Theano Université de Montréal 2007 BSD Yes Cross-platform Python Python (Keras) Yes Under development62 Yes No Yes6364 Through Lasagne's model zoo65 Yes Yes Yes Yes66 No
Torch Ronan Collobert, Koray Kavukcuoglu, Clement Farabet 2002 BSD Yes Linux, macOS, Windows,67 Android,68 iOS C, Lua Lua, LuaJIT,69 C, utility library for C++/OpenCL70 Yes Third party implementations7172 Yes7374 No Through Twitter's Autograd75 Yes76 Yes Yes Yes Yes67 No
RANT (Real-time Artificial Neural Tool)77 Douglas Santry 2023 BSD Yes Linux, macOS, Windows C++, Python C++, Python No No No No No Yes No Yes No No Yes
Wolfram Mathematica 1078 and later Wolfram Research 2014 Proprietary No Windows, macOS, Linux, Cloud computing C++, Wolfram Language, CUDA Wolfram Language Yes No Yes No Yes Yes79 Yes Yes Yes Yes80 Yes
Software Creator Initial release Software licensea
Open source
 Platform Written in Interface OpenMP support OpenCL support CUDA support
ROCm support81
 Automatic differentiation2 Has pretrained models
Parallel execution
(multi node)
Actively developed
  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

Comparison of machine learning model compatibility

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

See also

References

References

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