ChainerX Beta Release

Today, we announce ChainerX, a fast, portable, and extensible backend of Chainer. It is aimed at reducing the host-side performance overhead as well as making models much easier to ship for applications. ChainerX is included as an optional feature of Chainer v6.0.0 beta1, and is planned to be officially released as a part of Chainer v6 series next Spring. You can find the official documentation, including a quick tutorial.

Released Chainer/CuPy v5.0.0

We have released Chainer and CuPy v5.0.0 today! This is a major release that introduces several new features.

Released Chainer/CuPy v4.0.0

We have released Chainer and CuPy v4.0.0 today! This is a major release that introduces several new features, especially for accelerating deep learning computations and making the installation process easier. The following is a selected list of updates (full updates can be seen in the release notes: Chainer, CuPy). Note that some of these updates are also backported to v3 series.

Chainer v2.0.0 and our future development plans

We have released Chainer v2.0.0 today! This is the first major update of Chainer. The detailed updates from the beta release can be found in the release notes. You can also find the differences between v1 and v2 in the Upgrade Guide. Note that the repository has been moved to chainer/chainer. Any access to the old URL (including Git operations) will automatically be redirected to the new one.

Plan of v2

We are planning the first major update of Chainer! It is currently scheduled for next March or April.

New release cycle

Starting from the release of v1.18.0, we will have one release every four weeks instead of the current cycle of one release every two weeks.

About Chainer

Chainer is a Python-based, standalone open source framework for deep learning models. Chainer provides a flexible, intuitive, and high performance means of implementing a full range of deep learning models, including state-of-the-art models such as recurrent neural networks and variational autoencoders.

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