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.

ONNX support by Chainer

  • By Shunta Saito
  • Jan 17, 2018
  • In General

ONNX support by Chainer

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.

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.

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.

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

  • By Shohei Hido
  • Feb 22, 2017
  • In General

Chainer-based deep reinforcement learning library, ChainerRL has been released. https://github.com/pfnet/chainerrl

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.

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.

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 Blog

This is the official blog of Chainer, a framework for neural networks. In this blog, we will provide information about Chainer and its development, including:

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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