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.
Key Features
- 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.
Website: chainer.org
Support: Forum, GitHub Code Repository
Developer: Preferred Networks, inc
License: MIT License
Chainer is written in Python. Learn Python with our recommended free books and free tutorials.
Related Software
| Deep Learning with Python | |
|---|---|
| TensorFlow | A very popular Deep Learning framework |
| PyTorch | Tensors and Dynamic neural networks in Python |
| Keras | High-level neural networks API |
| fastai | Simplifies training fast and accurate neural networks |
| tinygrad | Compact, lightweight deep learning framework |
| PyTorch Lightning | Framework for scaling PyTorch training workflows |
| JAX | High-performance numerical computing and ML library |
| PyTensor | Library for fast numerical computation |
| MindSpore | Framework for training and deploying neural networks |
| Elephas | Distributed deep learning with Keras and Spark |
| Chainer | Powerful, flexible, and intuitive framework for neural networks |
| Equinox | Neural network library for building flexible models with JAX |
| Caffe | Convolutional Architecture for Fast Feature Embedding |
| TFlearn | Deep learning library featuring a higher-level API for TensorFlow |
| MXNet | Flexible and efficient library |
| CNTK | Distributed deep learning |
| Neupy | Python library for neural networks and deep learning |
Read our verdict in the software roundup.
 Explore our carefully curated directory of recommended free and open source software, covering every major software category.The directory forms part of our extensive collection of articles for Linux enthusiasts. It includes hundreds of detailed reviews, together with free and open source alternatives to proprietary software from companies such as Google, Microsoft, Apple, Adobe, IBM, Cisco, Oracle, and Autodesk. LinuxLinks also covers interesting projects worth exploring, Linux-compatible hardware, free programming books and tutorials, and much more. Know a useful free and open source Linux application that we haven’t covered? Tell us about it using our submission form. |
Please read our Comment Policy before commenting.