Today, we’d like to introduce you to Chainer — a popular open-source, cross-platform deep learning framework. Developed by Seiya Tokui in collaboration with the Japanese company Preferred Networks, and supported by major tech partners like IBM, Intel, Microsoft, and NVIDIA, Chainer stands out as a robust, flexible, and intuitive framework for deep learning.
Written in Python and first released in 2015, Chainer quickly gained traction, especially in Japan. As its global popularity continues to grow, we’ve put together this updated overview to help you better understand what makes Chainer unique.
But before we dive into its features, let’s take a quick look at the foundation of deep learning — neural networks.
What Is a Neural Network Framework?
Artificial neural networks are at the heart of deep learning. Inspired by the human brain’s structure, they’re designed to help computers recognize patterns, make decisions, and learn from data.
Deep learning models typically rely on five main types of neural networks:
- Multi-layer Perceptron (MLP)
- Radial Basis Network
- Recurrent Neural Network (RNN)
- Generative Adversarial Network (GAN)
- Convolutional Neural Network (CNN)
Neural networks are composed of artificial neurons that pass signals to each other. Each neuron can process multiple inputs and produce a single output — essentially converting raw data into meaningful results.
Between the input and output layers, one or more hidden layers handle the processing. Depending on the architecture, data can flow in one direction (Feed-forward networks) or loop back through previous layers (Feedback or Recurrent networks) to learn from context and memory.
Key Features of Chainer
Chainer offers several standout features that make it a favorite among developers and researchers:
- Define-by-run architecture — Networks are built dynamically as they run, allowing for greater flexibility and easier debugging.
- CUDA and multi-GPU support — Chainer can leverage GPU acceleration with just a few lines of code, thanks to PyCUDA.
- Support for diverse network types — From feed-forward and recurrent networks to recursive and per-batch architectures.
- Flexible computation graph — You can use any Python control flow (loops, conditions, etc.) during forward computation without breaking backpropagation.
- Rich multi-dimensional array support — Offers powerful tools for layer implementation and manipulation.
Let’s take a closer look at one of Chainer’s most defining concepts — the Define-by-run approach.
What Is Define-by-run?
Before Chainer, most deep learning frameworks followed the define-and-run model. In that setup, you first define the entire network structure — the connections between operations like matrix multiplications and activations — and only then run the training process. Frameworks like TensorFlow and Theano traditionally used this method.
Chainer, however, introduced define-by-run, where the computational graph is built on the fly as the code executes. This dynamic approach allows you to modify the network structure at runtime, making experimentation and debugging much easier.
In a define-by-run setup:
- The forward pass is just regular Python code.
- The backward pass (for gradient computation) automatically follows the operations performed during the forward pass.
Advantages of Define-by-run Over Define-and-run
The define-by-run model offers two major advantages:
- Flexibility – You can change the model structure dynamically, which is especially valuable for recurrent neural networks and other complex models.
- Easier Debugging – Since the code runs like a normal Python program, you can use breakpoints, print statements, or the built-in debugger to inspect variables and fix issues instantly.
This makes Chainer more intuitive and developer-friendly compared to static graph frameworks.
Chainer’s Flexibility in Practice
Chainer’s flexibility makes it a go-to choice for researchers who need to prototype and iterate quickly. The current stable release, version 6.3.0, continues to support various architectures, layers, and GPU setups. Its dynamic computation graph and modular structure make it one of the most adaptable deep learning frameworks available.
Extension Libraries
Chainer’s capabilities can be extended even further through several specialized libraries:
- ChainerMN – Enables distributed deep learning and multi-GPU training.
- ChainerRL – Provides state-of-the-art algorithms for reinforcement learning.
- ChainerCV – Designed for computer vision tasks like object detection and image segmentation.
- ChainerUI – A visualization and management tool for monitoring and managing training experiments.
Together, these extensions make Chainer not just flexible, but also incredibly versatile across various deep learning domains.
How to Install and Use Chainer
To get started, set up a Python 2.7 (or later) environment with pip installed. Then, simply run:
pip install chainer
If you plan to use GPU acceleration, install CUDA along with compatible NVIDIA drivers. Once installation is complete, you can explore the included user manual (PDF) for usage examples and advanced configuration options.
Conclusion
Chainer remains a landmark in the evolution of deep learning frameworks, thanks to its define-by-run architecture, flexibility, and extensive library support.
One fascinating example of Chainer in action is PaintsChainer, an AI-powered tool that automatically colorizes black-and-white artwork with minimal user input — a perfect showcase of Chainer’s creative potential.
Whether you’re a researcher, developer, or enthusiast, Chainer offers a powerful and flexible platform for experimenting with deep learning.
