AWS Deep Learning AMIs Introduce Chainer, PyTorch 0.3.1 and Apache MXNet 1.1 Support
The AWS Deep Learning AMIs now come with support for Chainer, a flexible and intuitive framework for neural networks. Chainer uses a "define-by-run" approach that enables developers to define their deep learning network architecture on the fly. Such dynamic computational graphs are especially helpful while working with recurrent neural networks (RNNs), and with inputs of variable length like in natural language processing. You can You can quickly get started with Chainer using our step-by-step tutorial.
The AWS Deep Learning AMIs now come with support for Chainer, a flexible and intuitive framework for neural networks. Chainer uses a "define-by-run" approach that enables developers to define their deep learning network architecture on the fly. Such dynamic computational graphs are especially helpful while working with recurrent neural networks (RNNs), and with inputs of variable length like in natural language processing. You can You can quickly get started with Chainer using our step-by-step tutorial.
This release also includes PyTorch 0.3.1 and Apache MXNet 1.1, which provides performance and usability enhancements, and bug fixes. The AWS Deep Learning AMIs are designed to help you build stable, secure and scalable deep learning applications. Visit our documentation guide to select the right AMI for your project and get started with deep learning on AWS.