Chainer is a deep learning framework for flexible and intuitive coding of high performance experiments and applications. It is designed to maximize the trial-and-error speed with its Define-by-Run paradigm, which provides Pythonic programming of auto-differentiated neural networks. The framework can accelerate performance with multiple GPUs in distributed environments and add-on packages enable quickly jumping into specific domains. In this talk, we introduce the abstract of Chainer’s API, its capabilities for accelerating the deep learning research and applications, and the future direction of the framework development.