AWS Machine Learning Blog

Category: Amazon EC2

Achieve 12x higher throughput and lowest latency for PyTorch Natural Language Processing applications out-of-the-box on AWS Inferentia

AWS customers like Snap, Alexa, and Autodesk have been using AWS Inferentia to achieve the highest performance and lowest cost on a wide variety of machine learning (ML) deployments. Natural language processing (NLP) models are growing in popularity for real-time and offline batched use cases. Our customers deploy these models in many applications like support […]

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AWS and NVIDIA to bring Arm-based Graviton2 instances with GPUs to the cloud

AWS continues to innovate on behalf of our customers. We’re working with NVIDIA to bring an Arm processor-based, NVIDIA GPU accelerated Amazon Elastic Compute Cloud (Amazon EC2) instance to the cloud in the second half of 2021. This instance will feature the Arm-based AWS Graviton2 processor, which was built from the ground up by AWS […]

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Join AWS and NVIDIA at GTC, October 5–9

Starting Monday, October 5, 2020, the NVIDIA GPU Technology Conference (GTC) is offering online sessions for you to learn AWS best practices to accomplish your machine learning (ML), virtual workstations, high performance computing (HPC), and internet of things (IoT) goals faster and more easily. Amazon Elastic Compute Cloud (Amazon EC2) instances powered by NVIDIA GPUs […]

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AWS Inferentia is now available in 11 AWS Regions, with best-in-class performance for running object detection models at scale

AWS has expanded the availability of Amazon EC2 Inf1 instances to four new AWS Regions, bringing the total number of supported Regions to 11: US East (N. Virginia, Ohio), US West (Oregon), Asia Pacific (Mumbai, Singapore, Sydney, Tokyo), Europe (Frankfurt, Ireland, Paris), and South America (São Paulo). Amazon EC2 Inf1 instances are powered by AWS […]

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How to run distributed training using Horovod and MXNet on AWS DL Containers and AWS  Deep Learning AMIs

Distributed training of large deep learning models has become an indispensable way of model training for computer vision (CV) and natural language processing (NLP) applications. Open source frameworks such as Horovod provide distributed training support to Apache MXNet, PyTorch, and TensorFlow. Converting your non-distributed Apache MXNet training script to use distributed training with Horovod only […]

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Amazon EC2 Inf1 instances featuring AWS Inferentia chips now available in five new Regions and with improved performance

Following strong customer demand, AWS has expanded the availability of Amazon EC2 Inf1 instances to five new Regions: US East (Ohio), Asia Pacific (Sydney, Tokyo), and Europe (Frankfurt, Ireland). Inf1 instances are powered by AWS Inferentia chips, which Amazon custom-designed to provide you with the lowest cost per inference in the cloud and lower barriers […]

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Facebook uses Amazon EC2 to evaluate the Deepfake Detection Challenge

In October 2019, AWS announced that it was working with Facebook, Microsoft, and the Partnership on AI on the first Deepfake Detection Challenge. Deepfake algorithms are the same as the underlying technology that has given us realistic animation effects in movies and video games. Unfortunately, those same algorithms have been used by bad actors to […]

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AWS to offer NVIDIA A100 Tensor Core GPU-based Amazon EC2 instances

Tens of thousands of customers rely on AWS for building machine learning (ML) applications. Customers like Airbnb and Pinterest use AWS to optimize their search recommendations, Lyft and Toyota Research Institute to develop their autonomous vehicle programs, and Capital One and Intuit to build and deploy AI-powered customer assistants. AWS offers the broadest and deepest […]

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Train Deep Learning Models on GPUs using Amazon EC2 Spot Instances

You’ve collected your datasets, designed your deep neural network architecture, and coded your training routines. You are now ready to run training on a large dataset for multiple epochs on a powerful GPU instance. You learn that the Amazon EC2 P3 instances with NVIDIA Tesla V100 GPUs are ideal for compute-intensive deep learning training jobs, […]

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Scalable multi-node deep learning training using GPUs in the AWS Cloud 

A key barrier to the wider adoption of deep neural networks on industrial-size datasets is the time and resources required to train them. AlexNet, which won the 2012 ImageNet Large Scale Visual Recognition Competition (ILSVRC) and kicked off the current boom in deep neural networks, took nearly a week to train across the 1.2-million-image, 1000-category […]

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