Deep Learning

Chainer – framework for neural networks

Chainer is a Python-based open source deep learning framework aiming at flexibility.

It provides automatic differentiation APIs based on the define-by-run approach (a.k.a. dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks. It also supports CUDA/cuDNN using CuPy for high performance training and inference.

Chainer’s seeks to provide a different perspective – it lets you build the computational graph “on-the-fly” during training.

Chainer is cited in many academic papers not only for computer vision, but also speech processing, natural language processing, and robotics.

Features include:

  • Chainer is developed in Python, allowing for inspection and customization of all code in Python and understandable python messages at run time.
  • Supports CUDA computation. It only requires a few lines of code to leverage a GPU. It also runs on multiple GPUs. It includes a GPU-based numerical computation library named CuPy. CuPy is a NumPy-equivalent array backend for GPUs included in Chainer, which enables CPU/GPU-agnostic coding, just like NumPy-based operations. NumPy based syntax for working with arrays, thanks to CuPy implementation
  • Broad and deep support – Chainer is actively used for most of the current approaches for neural nets (CNN, RNN, RL, etc.), adds new approaches as they’re developed.
  • Provides imperative ways of declaring neural networks by supporting Numpy-compatible operations between arrays.
  • Supports various network architectures including feed-forward nets, convnets, recurrent nets and recursive nets. It also supports per-batch architectures.
  • Hardware acceleration support:
    • NVIDIA CUDA / cuDNN.
    • Intel CPU (experimental).
    • Multi-GPU data parallelism.
    • Multi-GPU model parallelism.
  • OOP like programming style.
  • Native trainer abstraction. Chainer’s training framework aims at maximal flexibility, while keeps the simplicity for the typical usages. Most components are pluggable, and users can overwrite the definition.
  • Native reporter abstraction.
  • Extension libraries.
  • Fully customizable.

Support: Forum, GitHub Code Repository
Developer: Preferred Networks, inc
License: MIT License


  • Python – supported versions 2.7.6+, 3.4.3+, 3.5.1+ and 3.6.0+.
  • NumPy – supported versions: 1.9, 1.10, 1.11, 1.12 and 1.13.

Chainer is written in Python. Learn Python with our recommended free books and free tutorials.

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