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Comparison of deep learning software

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The following table compares some of the most popular software frameworks, libraries and computer programs for deep learning.

Deep learning software by name

Software Creator Software license[a] Open source Platform Written in Interface OpenMP support OpenCL support CUDA support Automatic differentiation[1] Has pretrained models Recurrent Nets Convolutional Nets RBM/DBNs
Caffe Berkeley Vision and Learning Center, community contributors BSD 2-Clause License Yes Ubuntu, OS X, AWS,[2] unofficial Android port,[3] Windows support by Microsoft Research,[4] unofficial Windows port[5] C++, Python[6] C++, command line, Python, MATLAB[7] No Branch,[8] pull request,[9] third party implementation[10] Yes ? Yes[11] Yes Yes No[12]
Chainer[13] PFI/PFN MIT license Yes Linux, Mac OS X Windows Python Python No On roadmap[14] Yes ? Through Caffe's model zoo[15] Yes Yes Yes
CNTK Microsoft Free[16] Yes Windows, Linux[17] C++ Command line,[18] network description language BrainScript[19] (C++, Python and .NET on roadmap[20]) Yes[21] No Yes Yes No[22] Yes[23] Yes[23] No[24]
Deeplearning4j Skymind engineering team; Deeplearning4j community; originally Adam Gibson Apache 2.0 Yes Linux, Ubuntu, Windows, OSX, Android (Cross-platform) Java, Scala, C, C++ Java, Scala, Clojure On roadmap On roadmap[25] Yes[26] Computational Graph Yes[27] Yes Yes Yes
MXNet Distributed (Deep) Machine Learning Community Apache 2.0 Yes Ubuntu, OS X, Windows,[28][29] AWS, Android,[30] iOS, JavaScript[31] C++, Python, Julia, Matlab, Go, R, Scala C++, Python, Julia, Matlab, JavaScript, Go, R, Scala Yes On roadmap[32] Yes Yes[33] Yes[34] Yes Yes Yes
Neural Designer Artelnics Proprietary No Windows, OS X, Linux C++ Graphical user interface Yes No No ? ? No No No
OpenNN Artelnics GNU LGPL Yes Cross platform C++ C++ Yes No No ? ? No No No
SINGA[35] Apache Incubator Apache 2.0 Yes Linux C++, Python Python, C++ No No Yes ? No Yes Yes Yes
TensorFlow Google Brain team Apache 2.0 Yes Linux, Mac OS X (Windows support on roadmap[36][37]) C++, Python Python, C/C++ No On roadmap[37][38] Yes Yes[39] No Yes Yes Yes
Theano Université de Montréal BSD license Yes Cross-platform Python Python Yes Under development[40] Yes Yes[41][42] Through Lasagne's model zoo[43] Yes Yes Yes
Torch Ronan Collobert, Koray Kavukcuoglu, Clement Farabet BSD License Yes Linux, Android,[44] Mac OS X, iOS C, Lua Lua, LuaJIT,[45] C, utility library for C++/OpenCL[46] Yes Third party implementations[47] Yes[48][49] Through Twitter's Autograd[50] Yes[51] Yes Yes Yes
See also: Comparison of deep learning software/Resources § External links
  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

Deep learning software not yet covered

  • adnn – Javascript neural networks
  • Blocks – Theano framework for building and training neural networks
  • CaffeOnSpark – Scalable deep learning package running Caffe on Spark and Hadoop clusters with peer-to-peer communication
  • CNNLab – Deep learning framework using GPU and FPGA-based accelerators
  • ConvNetJS – Javascript library for training deep learning models entirely in a web browser
  • Cortex – Theano-based deep learning toolbox for neuroimaging
  • cuDNN – Highly optimized deep learning computation primitives implemented in CUDA
  • CURRENNT – CUDA-accelerated toolkit for deep Long Short-Term Memory (LSTM) RNN architectures supporting large data sets not fitting into main memory.
  • DeepCL – OpenCL library to train deep convolutional networks, with APIs for C++, Python and the command line
  • DeepLearningKit – Open source deep learning framework for iOS, OS X and tvOS[52]
  • DeepLearnToolbox – Matlab/Octave toolbox for deep learning (deprecated)
  • DeepX – Software accelerator for deep learning execution aimed towards mobile devices
  • DSSTNE (Deep Scalable Sparse Tensor Network Engine) – Amazon developed library for building deep learning models
  • Faster RNNLM (HS/NCE) toolkit – An rnnlm implementation for training on huge datasets and very large vocabularies and usage in real-world ASR and MT problems
  • GNU Gneural Network – GNU package which implements a programmable neural network
  • IDLF – Intel® Deep Learning Framework; supports OpenCL (deprecated)
  • Keras – Deep Learning library for Theano and TensorFlow
  • Lasagne – Lightweight library to build and train neural networks in Theano
  • Leaf – "The Hacker's Machine Learning Engine"; supports OpenCL
  • LightNet – MATLAB-based environment for deep learning
  • MatConvNet – CNNs for MATLAB
  • neon – Nervana's Python based Deep Learning framework
  • Neural Network Toolbox – MATLAB toolbox for neural network creation, training and simulation
  • Pylearn2 – Machine learning library mainly built on top of Theano
  • scikit-neuralnetwork – Multi-layer perceptrons as a wrapper for Pylearn2
  • Tensor Builder – Lightweight extensible library for easy creation of deep neural networks using functions from any Tensor-based library through an API based on the Builder Pattern
  • TensorGraph – Framework for building any models based on TensorFlow
  • Theano-Lights – Deep learning research framework based on Theano
  • tiny-cnn – Header only, dependency-free deep learning framework in C++11
  • torchnet – Torch framework providing a set of abstractions aiming at encouraging code re-use as well as encouraging modular programming[53][54]
  • Veles – Distributed machine learning platform by Samsung

See also

References

  1. ^ Atilim Gunes Baydin; Barak A. Pearlmutter; Alexey Andreyevich Radul; Jeffrey Mark Siskind (20 Feb 2015). "Automatic differentiation in machine learning: a survey". arXiv:1502.05767 [cs.LG].
  2. ^ "Caffe".
  3. ^ "GitHub - sh1r0/caffe-android-lib: Porting caffe to android platform". GitHub.
  4. ^ "GitHub - Microsoft/caffe: Caffe on both Linux and Windows". GitHub.
  5. ^ "GitHub - niuzhiheng/caffe: Caffe". GitHub.
  6. ^ "Caffe".
  7. ^ "Caffe".
  8. ^ "GitHub - BVLC/caffe at opencl". GitHub.
  9. ^ "OpenCL Backend by lunochod · Pull Request #2195 · BVLC/caffe". GitHub.
  10. ^ "GitHub - amd/OpenCL-caffe: OpenCL version of caffe developed by AMD research lab". GitHub.
  11. ^ "Model Zoo". GitHub.
  12. ^ "RBM layer ? (+DBN) · Issue #1207 · BVLC/caffe". GitHub.
  13. ^ "Chainer Official Site". GitHub.
  14. ^ "Chainer OpenCL support".
  15. ^ "Chainer Caffe Reference Model Support". GitHub.
  16. ^ "CNTK/LICENSE.md at master · Microsoft/CNTK · GitHub". GitHub.
  17. ^ "Setup CNTK on your machine". GitHub.
  18. ^ "CNTK usage overview". GitHub.
  19. ^ "BrainScript Network Builder". GitHub.
  20. ^ "CNTK as a library with C++ APIs · Issue #175 · Microsoft/CNTK". GitHub.
  21. ^ "How to train a model using multiple machines? · Issue #59 · Microsoft/CNTK". GitHub.
  22. ^ https://github.com/Microsoft/CNTK/issues/140#issuecomment-186466820
  23. ^ a b Microsoft Corporation. "CNTK - Computational Network Toolkit". Microsoft Corporation.
  24. ^ https://github.com/Microsoft/CNTK/issues/534. {{cite web}}: Missing or empty |title= (help)
  25. ^ "Support for Open CL · Issue #27 · deeplearning4j/nd4j". GitHub.
  26. ^ "N-Dimensional Scientific Computing for Java".
  27. ^ Chris Nicholson; Adam Gibson. "Deeplearning4j Models".
  28. ^ "Releases · dmlc/mxnet". Github.
  29. ^ "Installation Guide — mxnet documentation". Readthdocs.
  30. ^ "MXNet Smart Device". ReadTheDocs.
  31. ^ "MXNet.js". Github.
  32. ^ "Support for other Device Types, OpenCL AMD GPU · Issue #621 · dmlc/mxnet". GitHub.
  33. ^ http://mxnet.readthedocs.io/
  34. ^ "Model Gallery". GitHub.
  35. ^ Apache SINGA. "Apache SINGA – A Distributed Deep Learning Platform".
  36. ^ "Windows Support and Documentation · Issue #17 · tensorflow/tensorflow". GitHub.
  37. ^ a b "tensorflow/roadmap.md at master · tensorflow/tensorflow · GitHub". GitHub.
  38. ^ "OpenCL support · Issue #22 · tensorflow/tensorflow". GitHub.
  39. ^ https://www.tensorflow.org/
  40. ^ "Using the GPU — Theano 0.8.2 documentation".
  41. ^ http://deeplearning.net/software/theano/library/gradient.html
  42. ^ https://groups.google.com/d/msg/theano-users/mln5g2IuBSU/gespG36Lf_QJ
  43. ^ "Recipes/modelzoo at master · Lasagne/Recipes · GitHub". GitHub.
  44. ^ "GitHub - soumith/torch-android: Torch-7 for Android". GitHub.
  45. ^ "Torch7: A Matlab-like Environment for Machine Learning" (PDF).
  46. ^ "GitHub - jonathantompson/jtorch: An OpenCL Torch Utility Library". GitHub.
  47. ^ "Cheatsheet". GitHub.
  48. ^ "Torch CUDA backend". GitHub.
  49. ^ "Torch CUDA backend for nn". GitHub.
  50. ^ https://github.com/twitter/torch-autograd
  51. ^ "ModelZoo". GitHub.
  52. ^ http://arxiv.org/pdf/1605.04614v1.pdf
  53. ^ https://code.facebook.com/posts/580706092103929
  54. ^ Ronan Collobert; Laurens van der Maaten; Armand Joulin. "Torchnet: An Open-Source Platform for (Deep) Learning Research" (PDF). Facebook AI Research. Retrieved 24 June 2016.
  55. ^ http://arxiv.org/abs/1506.06579
  56. ^ http://yosinski.com/deepvis
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