If you want to be the very best that no one ever was, you should read this tutorial on how to use an AWS Deep Learning AMI to train a Neural Network classifier in Python. The goal of this classifier is to give an image of a Gen 1 Pokemon, to identify it. That was a lot of acronyms and funny words, before we get started on the tutorial, let's cover some background information. AMI stands for Amazon Machine Image and is a template that is used to launch a virtual server (which in AWS is also known as an EC2 instance that you can read more about below). Since it is a template, you can use one AMI to launch multiple EC2 instances with the same configurations.

As machine learning and deep learning models become more sophisticated, hardware acceleration is increasingly required to deliver fast predictions at high throughput. Today, we're very happy to announce that AWS customers can now use the Amazon EC2 Inf1 instances on Amazon ECS, for high performance and the lowest prediction cost in the cloud. For a few weeks now, these instances have also been available on Amazon Elastic Kubernetes Service. They are powered by AWS Inferentia, a custom chip built from the ground up by AWS to accelerate machine learning inference workloads. Inf1 instances are available in multiple sizes, with 1, 4, or 16 AWS Inferentia chips, with up to 100 Gbps network bandwidth and up to 19 Gbps EBS bandwidth.

We've heard from customers that scaling TensorFlow training jobs to multiple nodes and GPUs successfully is hard. TensorFlow has distributed training built-in, but it can be difficult to use. Recently, we made optimizations to TensorFlow and Horovod to help AWS customers scale TensorFlow training jobs to multiple nodes and GPUs. With these improvements, any AWS customer can use an AWS Deep Learning AMI to train ResNet-50 on ImageNet in just under 15 minutes. To achieve this, 32 Amazon EC2 instances, each with 8 GPUs, a total 256 GPUs, were harnessed with TensorFlow. All of the required software and tools for this solution ship with the latest Deep Learning AMIs (DLAMIs), so you can try it out yourself. You can train faster, implement your models faster, and get results faster than ever before. This blog post describes our results and shows you how to try out this easier and faster way to run distributed training with TensorFlow. Figure A. ResNet-50 ImageNet model training with the latest optimized TensorFlow with Horovod on a Deep Learning AMI takes 15 minutes on 256 GPUs.

Welcome to the User Guide for the Deep Learning AMI. The Deep Learning AMI (DLAMI) is your one-stop shop for deep learning in the cloud. This customized machine instance is available in most Amazon EC2 regions for a variety of instance types, from a small CPU-only instance to the latest high-powered multi-GPU instances. It comes preconfigured with NVIDIA CUDA and NVIDIA cuDNN, as well as the latest releases of the most popular deep learning frameworks. This guide will help you launch and use the DLAMI.

The first instances to include NVIDIA Tesla V100 GPUs, P3 instances are the most powerful GPU instances available in the cloud. P3 instances allow customers to build and deploy advanced applications with up to 14 times better performance than previous-generation Amazon EC2 GPU compute instances, and reduce training of machine learning applications from days to hours. With up to eight NVIDIA Tesla V100 GPUs, P3 instances provide up to one petaflop of mixed-precision, 125 teraflops of single-precision, and 62 teraflops of double-precision floating point performance, as well as a 300 GB/s second-generation NVIDIA NVLink interconnect that enables high-speed, low-latency GPU-to-GPU communication. P3 instances also feature up to 64 vCPUs based on custom Intel Xeon E5 (Broadwell) processors, 488 GB of DRAM, and 25 Gbps of dedicated aggregate network bandwidth using the Elastic Network Adapter (ENA). "When we launched our P2 instances last year, we couldn't believe how quickly people adopted them," said Matt Garman, Vice President of Amazon EC2.