Chainer
Chainer is a deep learning framework that originated in Japan, developed primarily by Preferred Networks (PFN) and released as open-source in June 2015.
Profile
Chainer is an open-source deep learning framework that enables dynamic neural network construction through a define-by-run approach, allowing models to be built and modified on the fly during execution.
Chainer is a deep learning framework that originated in Japan, developed primarily by Preferred Networks (PFN) and released as open-source in June 2015. The project gained traction for its define-by-run approach, which allowed dynamic neural network construction, a feature that later influenced frameworks like PyTorch. Chainer's GitHub repository shows 5.9k stars, 1.3k forks, and 30,611 commits, with the last commit on October 16, 2022.
The repository is now in maintenance mode, as announced in the README and commit messages from October 2022. The project includes sub-packages for multi-node training (ChainerMN) and GPU acceleration (ChainerX). Chainer was once widely used in research and industry, particularly in Japan, but its usage has declined significantly since PyTorch and TensorFlow became dominant.
No recent funding rounds, acquisitions, or layoffs specific to Chainer were found in the provided dossier. The project's current status is effectively end-of-life, with no active development or community growth. The company behind it, Preferred Networks, continues to operate but has shifted focus to other products and services. Chainer's legacy is primarily as a historical artifact in the deep learning framework landscape, notable for its early adoption of dynamic computation graphs.
Products by Chainer
Who buys this
- Researchers in machine learning and artificial intelligence, particularly those focused on dynamic neural network architectures.
- Data scientists and engineers in Japan who adopted Chainer early for its Pythonic interface and flexibility.
- Academic institutions and labs that used Chainer for teaching and prototyping deep learning models.
- Enterprises that built production systems on Chainer before the rise of PyTorch and TensorFlow.
- Developers contributing to the open-source ecosystem, though this community has largely moved on.
Strengths and what to watch
Strengths
- Pioneered define-by-run dynamic computation graphs, a design that directly influenced PyTorch and remains a standard in modern frameworks.
- Strong integration with CuPy for GPU acceleration and ChainerMN for multi-node distributed training, providing a cohesive ecosystem.
- Well-documented and modular codebase with over 30,000 commits, reflecting years of community and corporate investment.
Watch for
- Project is in maintenance mode with no active development since October 2022; users should migrate to actively maintained frameworks like PyTorch.
- No recent funding, revenue, or headcount data available; the project's corporate sponsor, Preferred Networks, has not disclosed any new investment or growth for Chainer.
- Community and ecosystem have largely dissolved; the repository's star count (5.9k) is low compared to modern frameworks, and contributions have ceased.
Key Information
- Industry
- AI Frameworks, Tools & Libraries
- Founded
- 1986
Frequently Asked Questions
What is Chainer deep learning framework?
Chainer is an open-source deep learning framework developed by Preferred Networks in Japan, released in June 2015. It pioneered a define-by-run approach for dynamic neural network construction, allowing models to be built and modified during execution, influencing later frameworks like PyTorch.
Is Chainer still being maintained or is it end of life?
Chainer is in maintenance mode as of October 2022, with no active development or community growth. The GitHub repository shows the last commit on October 16, 2022, and the project is effectively end-of-life. Users are advised to migrate to actively maintained frameworks like PyTorch.
What is Chainer's define-by-run approach?
Chainer's define-by-run approach allows dynamic neural network construction, where models are built and modified on the fly during execution. This contrasts with static graph frameworks, offering flexibility for research and prototyping. This design directly influenced PyTorch's dynamic computation graphs.
Who developed Chainer and what is its current status?
Chainer was developed by Preferred Networks (PFN) in Japan and released as open-source in June 2015. The project is now in maintenance mode since October 2022, with no active development. PFN continues operations but has shifted focus to other products, making Chainer a historical artifact.
How does Chainer compare to PyTorch?
Chainer pioneered define-by-run dynamic computation graphs, which directly influenced PyTorch. However, Chainer's usage has declined significantly since PyTorch and TensorFlow became dominant. Chainer is now in maintenance mode, while PyTorch remains actively developed and widely adopted in research and industry.
What are the key features of Chainer framework?
Chainer offers define-by-run dynamic computation graphs, strong GPU acceleration through CuPy, and multi-node distributed training via ChainerMN. It has a well-documented, modular codebase with over 30,000 commits. However, the project is now in maintenance mode with no active development since October 2022.
Sources
- github.com — Repository status, last commit date, star and fork counts, and maintenance mode announcement.
- lngir.cheniere.com — No relevant information for Chainer; included as a placeholder from the dossier.
- www.chsinc.com — No relevant information for Chainer; included as a placeholder from the dossier.
- ir.papajohns.com — No relevant information for Chainer; included as a placeholder from the dossier.