What is Chainer?
It is an open source deep learning framework written purely in Python on top of Numpy and CuPy Python libraries aiming at flexibility. It supports CUDA computation. It only requires a few lines of code to leverage a GPU. It also runs on multiple GPUs with little effort.
Chainer is a tool in the Machine Learning Tools category of a tech stack.
Chainer is an open source tool with 5.6K GitHub stars and 1.4K GitHub forks. Here’s a link to Chainer's open source repository on GitHub
Who uses Chainer?
3 companies reportedly use Chainer in their tech stacks, including cotobox, AI, and Engineering.
11 developers on StackShare have stated that they use Chainer.
- Supports CUDA computation
- Runs on multiple GPUs
- Supports various network architectures
- Supports per-batch architectures
Chainer Alternatives & Comparisons
What are some alternatives to Chainer?
See all alternatives
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
Theano is a Python library that lets you to define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray).
It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.