Comparison of deep learning software
Tabular comparison of deep learning software From Wikipedia, the free encyclopedia
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 license[a] | Open source |
Platform | Written in | Interface | OpenMP support | OpenCL support | CUDA support | 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 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 | 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 | 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 | 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 | ? | ? | 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 | 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 | 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 | Automatic differentiation[2] | Has pretrained models | Parallel execution (multi node) |
Actively developed |
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
References
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