ChainerX Beta Release
ChainerX Beta Release By Seiya Tokui Dec 3, 2018 In Announcement 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
Released Chainer/CuPy v5.0.0
Released Chainer/CuPy v5.0.0 By Seiya Tokui Oct 25, 2018 In Announcement We have released Chainer and CuPy v5.0.0 today! This is a major release that introduces several new features. The following is a list of selected updates. Full updates can be found in the release notes: Chainer, CuPy. Static subgraph optimization (experimental). By applying the @static_graph decorator to the static part of yo
New ChainerMN functions for improved performance in cloud environments and performance testing results on AWS
New ChainerMN functions for improved performance in cloud environments and performance testing results on AWS By Shuji Suzuki May 25, 2018 In General ChainerMN is a package that adds multi-node distributed learning functionality to Chainer. We have added the following two new functions to v1.2.0 and v1.3.0 of ChainerMN, which are intended to improve the performance on systems whose inter-node comm
ChainerMN on Kubernetes with GPUs
ChainerMN on Kubernetes with GPUs By Shingo Omura May 10, 2018 In General Kubernetes is today the most popular open-source system for automating deployment, scaling, and management of containerized applications. As the rise of Kubernetes, bunch of companies are running Kubernetes as a platform for various workloads including web applications, databases, cronjobs and so on. Machine Learning workloa
Released Chainer/CuPy v4.0.0
Released Chainer/CuPy v4.0.0 By Seiya Tokui Apr 17, 2018 In Announcement 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
ONNX support by Chainer
ONNX support by Chainer By Shunta Saito Jan 17, 2018 In General ONNX support by Chainer Today, we jointly announce ONNX-Chainer, an open source Python package to export Chainer models to the Open Neural Network Exchange (ONNX) format, with Microsoft. Preferred Networks joined the ONNX partner workshop yesterday that was held in Facebook HQ in Menlo Park, and discussed future direction of ONNX. Wha
How to use Chainer for Theano users
How to use Chainer for Theano users By Shunta Saito Oct 6, 2017 In General As we mentioned on our blog, Theano will stop development in a few weeks. Many aspects of Chainer were inspired by Theano’s clean interface design, so we would like to introduce Chainer to users of Theano. We hope this article assists interested Theano users to move to Chainer easily. First, let’s summarize the key similari
Theano's contribution
Theano's contribution By Shunta Saito Sep 29, 2017 In General The Chainer team is saddened to hear about the end of Theano development. Some of us used Theano when we first started studying deep learning and many aspects of Chainer were inspired by Theano’s clean interface design. Like Theano, Chainer is a Python neural network framework with a numpy-like syntax for computation on CPUs or GPUs. Th
Chainer v2.0.0 and our future development plans
Chainer v2.0.0 and our future development plans By Seiya Tokui Jun 1, 2017 In Announcement 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 t
Performance comparison of LSTM with and without cuDNN(v5) in Chainer
Performance comparison of LSTM with and without cuDNN(v5) in Chainer By Motoki Sato Mar 15, 2017 In General We compare the performance of an LSTM network both with and without cuDNN in Chainer. The NVIDIA CUDA® Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as LSTM, CNN.
ChainerRL - Deep Reinforcement Learning Library
ChainerRL - Deep Reinforcement Learning Library By Shohei Hido Feb 22, 2017 In General Chainer-based deep reinforcement learning library, ChainerRL has been released. https://github.com/pfnet/chainerrl (This post is translated from the original post written by Yasuhiro Fujita.) Algorithms ChainerRL contains a set of Chainer implementations of deep reinforcement learning (DRL) algorithms. The follo
Performance of Distributed Deep Learning using ChainerMN
Performance of Distributed Deep Learning using ChainerMN By Takuya Akiba Feb 8, 2017 In General At Deep Learning Summit 2017 in San Francisco on this January, PFN announced advancements on distributed deep learning using Chainer in multi-node environment. In this post, I would like to explain the detail of the announcement. (This post is translated from the original post written by Takuya Akiba.)
Research projects using Chainer | Chainer Blog
Research projects using Chainer By Shunta Saito Dec 1, 2016 In General Recently we found some great research projects that are using Chainer for their algorithm implementations and experiments. We searched for such publicly available projects on arXiv and summarized them here as a table that lists papers along with their URL links: Research projects using Chainer. As a brief overview on the field
Plan of v2 | Chainer Blog
Plan of v2 By Seiya Tokui Nov 15, 2016 In Announcement We are planning the first major update of Chainer! It is currently scheduled for next March or April. The distinction of the release levels (major, minor, and revision) was made in v1.6.0 and is written in the API Compatibility Policy. We are following this policy to decide what can be merged to Chainer in each release. It states that each min
New release cycle | Chainer Blog
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. We had been keeping the biweekly release cycle since the first release in June, 2015. This release cycle worked well at the initial stage of the development when the number of features was small and the user base was not so large. After more than one year of
1
リリース、障害情報などのサービスのお知らせ
最新の人気エントリーの配信
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く
https://chainer.org/annoucement/2016/11/10/new-release-cycle.html
New release cycle | Chainer Blog
Chainer: A flexible framework for neural networks