New ChainerMN functions for improved performance in cloud environments and performance testing results on AWS
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)
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.
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
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.
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.
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.
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.
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.
- Chainer/CuPy v7 release and Future of Chainer
- Chainer/CuPy v7のリリースと今後の開発体制について
- Sunsetting Python 2 Support
- Released Chainer/CuPy v6.0.0
- ChainerX Beta Release
- Released Chainer/CuPy v5.0.0
- ChainerMN on AWS with CloudFormation
- Open source deep learning framework Chainer officially supported by Amazon Web Services