Amazon SageMaker now Provides Chainer integration, Support for AWS CloudFormation, and Availability in the Asia Pacific (Tokyo) AWS Region
Amazon SageMaker now comes pre-configured to run Chainer in a Docker container, adding to the existing integrated Tensorflow and Apache MXNet deep learning framework containers currently available. Chainer is a popular deep learning framework that supports a variety of neural architectures where the network is defined dynamically in a “define-by-run” scheme. This means you can fully leverage Python constructs and control flows in your network. To quickly get started with Chainer, Amazon SageMaker provides sample notebooks for common workflows such as sentiment analysis and MNIST, by example, easily accessible within the Jupyter Dashboard Interface from Amazon SageMaker.
Amazon SageMaker now comes pre-configured to run Chainer in a Docker container, adding to the existing integrated Tensorflow and Apache MXNet deep learning framework containers currently available. Chainer is a popular deep learning framework that supports a variety of neural architectures where the network is defined dynamically in a “define-by-run” scheme. This means you can fully leverage Python constructs and control flows in your network. To quickly get started with Chainer, Amazon SageMaker provides sample notebooks for common workflows such as sentiment analysis and MNIST, by example, easily accessible within the Jupyter Dashboard Interface from Amazon SageMaker.
Amazon SageMaker also now supports AWS CloudFormation, so you can describe and provision all of your infrastructure resources through a template, to standardize configuration across your organization and accounts in an automated and secure manner.
Lastly, Amazon SageMaker is now available in the Asia Pacific (Tokyo) AWS Region.
Chainer integration and support for AWS CloudFormation are available in Amazon SageMaker today in the U.S. East (N. Virginia), U.S. East (Ohio), U.S West (Oregon), EU (Ireland), and Asia Pacific (Tokyo) AWS regions. Visit the documentation for more information on using Chainer in Amazon SageMaker algorithms, and visit the blog post.