Comparison with Other Frameworks

A table for quick comparison

This table compares Chainer with other popular deep learning frameworks. We hope it helps you to choose an appropriate framework for the demand.


This chart may be out-dated, since the developers of Chainer do not perfectly follow the latest development status of each framework. Please report us if you find an out-dated cell. Requests for new comparison axes are also welcome.

    Chainer Theano-based Torch7 Caffe
Specs Scripting Python Python LuaJIT Python
Net definition language Python Python LuaJIT Protocol Buffers
Define-by-Run scheme Y      
CPU Array backend NumPy NumPy Tensor  
GPU Array backend CuPy CudaNdarray [1] CudaTensor  
NNs Reverse-mode AD Y Y Y Y
Basic RNN support Y Y Y (nnx) #2033
Variable-length loops Y Y (scan)    
Stateful RNNs [2] Y Y Y [6]  
Per-batch architectures Y      
Perf CUDA support Y Y Y Y
cuDNN support Y Y Y (cudnn.torch) Y
FFT-based convolution   Y Y (fbcunn) #544
CPU/GPU generic coding [3] Y [4] Y  
Multi GPU (data parallel) Y Y [7] Y (fbcunn) Y
Multi GPU (model parallel) Y Y [8] Y (fbcunn)  
Misc Type checking Y Y Y N/A
Model serialization Y Y (pickle) Y Y
Caffe reference model Y [5] Y (loadcaffe) Y
[1]They are also developing libgpuarray
[2]Stateful RNN is a type of RNN implementation that maintains states in the loops. It should enable us to use the states arbitrarily to update them.
[3]This row shows whether each array API supports unified codes for CPU and GPU.
[4]The array backend of Theano does not have compatible interface with NumPy, though most users write code on Theano variables, which is generic for CPU and GPU.
[5]Depending on the frameworks.
[6]Also available in the Torch RNN package
[7]Via Platoon
[8]Experimental as May 2016


We are preparing for the benchmarks.