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.
- 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.
- 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.
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