Released Chainer/CuPy v6.0.0

We have released Chainer and CuPy v6.0.0 today! This is a major release that introduces several new features. Full updates can be found in the release notes: Chainer, CuPy.

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.

ChainerMN on AWS with CloudFormation

  • By Shingo Omura
  • Jun 1, 2018
  • In General

Japanese version is here

Open source deep learning framework Chainer officially supported by Amazon Web Services

Chainer has worked with Amazon Web Services (AWS) to provide access to the Chainer deep learning framework as a listed choice across many of AWS applications. Chainer provides straightforward calculation of deep neural networks in Python. The combination with AWS leverages Chainer’s exceptional abilities in multi-GPU and multi-server scaling, as demonstrated when PFN trained ResNet50 on ImageNet-1K using Chainer in 15 minutes, four times faster than the previous record held by Facebook.

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 communication bandwidth is low.

  • Double buffering to conceal communication time
  • All-Reduce function in half-precision floats (FP16)

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 workloads, including Deep Learning workloads, are not an exception even though such workloads require special hardwares like GPUs.

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.

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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