Posted On: Sep 20, 2018

The AWS Deep Learning AMIs for Ubuntu and Amazon Linux now come with newer versions of the following deep learning frameworks and interfaces in the AWS China (Beijing) Region, operated by Sinnet: TensorFlow 1.10 optimized for AWS for higher performance, Horovod 0.13.11 with OpenMPI 3.1.0 optimized for distributed multi-GPU TensorFlow training on Amazon EC2 P3 instances, PyTorch with CUDA 9.2 optimized for model training on Amazon EC2 P3 instances, Chainer 4.3.1, and Keras 2.2.2.

Faster training with optimized TensorFlow 1.10

The Deep Learning AMIs come with an optimized build of TensorFlow 1.10, custom built to accelerate deep learning applications on Amazon EC2 C5 and P3 instances. Deep Learning AMIs automatically deploy the TensorFlow build optimized for the EC2 instance of your choice when you activate the TensorFlow virtual environment for the first time.

For developers looking to scale their TensorFlow training from a single GPU to multiple GPUs, the AWS Deep Learning AMIs come with Horovod, optimized for distributed training using Amazon EC2 P3 instances.

Latest in framework updates

Deep Learning AMIs now support the latest PyTorch 0.4.1 pre-configured with NVidia CUDA 9.2, cuDNN 7.1.4, and NCCL 2.2.13 for accelerated deep learning on Amazon EC2 P3 instances. Also Chainer is now upgraded to version 4.3.1, optimized for high performance across Amazon EC2 instance families.

AWS Deep Learning AMIs also support Apache MXNet 1.2.1 with Gluon, Microsoft Cognitive Toolkit (CNTK) 2.5.1, Caffe 1.0, Caffe2 0.8.1 and Theano 1.0.1 —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.

Getting started with the Deep Learning AMIs

You can quickly get started with the AWS Deep Learning AMIs by using the tutorials in the developer guide. You can find the Deep Learning AMI of your choice in the Quick Start section of the Step 1: Choose an Amazon Machine Image (AMI) in the EC2 instance launch wizard (see the following image).
 

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