New in AWS Deep Learning AMIs: Updated Elastic Inference for TensorFlow, TensorBoard 1.12.1, and MMS 1.0.1
The AWS Deep Learning AMIs for Ubuntu and Amazon Linux now support Amazon Elastic Inference with the latest version of TensorFlow-1.12. With this release, Amazon Elastic Inference (Amazon EI) on Deep Learning AMIs now provides EIPredictor, a new, easy-to-use Python API function for deploying TensorFlow models using EI accelerators to enable easier experimentation. With EIPredictor, developers now have have an alternative to TensorServing when running TensorFlow models on Amazon Elastic Inference. This release also adds a new Conda environment for Amazon Elastic Inference with TensorFlow on Python 3.6, an upgrade to TensorBoard 1.12.1, and an upgrade to MXNet Model Server 1.0.1.
AWS Deep Learning AMIs also support popular deep learning frameworks and interfaces including TensorFlow, MXNet, PyTorch, Chainer, Keras, and Gluon — all pre-installed and fully-configured for you to start developing your deep learning models in minutes while taking full advantage of the computational power of Amazon EC2 instances. When you activate a Conda environment, the Deep Learning AMIs automatically deploy higher-performance builds of frameworks, optimized for the EC2 instance of your choice. For a complete list of frameworks and versions supported by the AWS Deep Learning AMI, see the release notes.
Get started quickly with the AWS Deep Learning AMIs using the getting-started guides and beginner to advanced level tutorials in our developer guide. You can also subscribe to our discussion forum to get launch announcements and post your questions.