Tag: AWS Deep Learning

Fraud detection is an important problem that has applications in financial services, social media, ecommerce, gaming, and other industries. This post presents an implementation of a fraud detection solution using the Relational Graph Convolutional Network (RGCN) model to predict the probability that a transaction is fraudulent through both the transductive and inductive inference modes. You can deploy our implementation to an Amazon SageMaker endpoint as a real-time fraud detection solution, without requiring external graph storage or orchestration, thereby significantly reducing the deployment cost of the model.

Modern model pre-training often calls for larger cluster deployment to reduce time and cost. At the server level, such training workloads demand faster compute and increased memory allocation. As models grow to hundreds of billions of parameters, they require a distributed training mechanism that spans multiple nodes (instances). In October 2022, we launched Amazon EC2 […]

The AWS Deep Learning AMIs (DLAMI) for Ubuntu and Amazon Linux are now pre-installed and fully configured with Open Neural Network Exchange (ONNX), enabling model portability across deep learning frameworks. In this blog post we’ll introduce ONNX, and demonstrate how ONNX can be used on the DLAMI to port models across frameworks. What is ONNX? ONNX is an open […]

The AWS Deep Learning AMIs for Ubuntu and Amazon Linux now come with an optimized build of TensorFlow 1.9 custom-built directly from source and fine-tuned for high performance training across Amazon EC2 instances. In addition, the AMIs come with the latest Apache MXNet 1.2 with several performance and usability improvements, the new Keras 2-MXNet backend […]

The AWS Deep Learning AMIs (Amazon machine images) are now available in AWS GovCloud (US), Amazon’s isolated cloud region built for sensitive data and regulated workloads. Available in Ubuntu and Amazon Linux, the AMIs provide fully-configured development environments to quickly build AI applications using popular deep learning frameworks including TensorFlow, Apache MXNet and Gluon, PyTorch, Chainer, […]

The AWS Deep Learning AMIs for Ubuntu and Amazon Linux now come pre-installed and fully configured with Horovod, the popular open source distributed training framework to scale TensorFlow training on multiple GPUs. This is an update to the optimized build of TensorFlow 1.8 that we launched in early May. This custom build of TensorFlow 1.8 […]

Whether you’re new to deep learning or want to build advanced deep learning projects in the cloud, it’s easy to get started by using AWS. For users of all levels, AWS recommends Amazon SageMaker, a fully managed machine learning (ML) platform. The platform makes it straightforward to quickly and easily build, train, and deploy ML […]

The AWS Deep Learning AMI lets you build and scale deep learning applications in the cloud, at any scale. The AMI comes pre-installed with popular deep learning frameworks, to let you to train sophisticated, custom AI models, experiment with new algorithms, or to learn new skills and techniques. The latest release of the AWS Deep […]

You can now use Apache MXNet v0.10 and TensorFlow v1.1 with the AWS Deep Learning AMIs for Amazon Linux and Ubuntu. Apache MXNet announced version 0.10, available at http://mxnet.io, with significant improvements to documentation and tutorials including updated installation guides for running MXNet on various operating systems and environments, such as NVIDIA’s Jetson TX2. In […]

In recent years, deep learning has significantly improved several AI applications, such as recommendation engines, voice and speech recognition, and image and video recognition. Many customers process the massive amounts of data that feed these deep neural networks in Apache Spark, only to later feed it into a separate infrastructure to train models using popular […]