AWS Machine Learning Blog

Category: PyTorch on AWS

Reduce deep learning training time and cost with MosaicML Composer on AWS

In the past decade, we have seen Deep learning (DL) science adopted at a tremendous pace by AWS customers. The plentiful and jointly trained parameters of DL models have a large representational capacity that brought improvements in numerous customer use cases, including image and speech analysis, natural language processing (NLP), time series processing, and more. […]

Solution overview

Build flexible and scalable distributed training architectures using Kubeflow on AWS and Amazon SageMaker

In this post, we demonstrate how Kubeflow on AWS (an AWS-specific distribution of Kubeflow) used with AWS Deep Learning Containers and Amazon Elastic File System (Amazon EFS) simplifies collaboration and provides flexibility in training deep learning models at scale on both Amazon Elastic Kubernetes Service (Amazon EKS) and Amazon SageMaker utilizing a hybrid architecture approach. […]

Build custom Amazon SageMaker PyTorch models for real-time handwriting text recognition

In many industries, including financial services, banking, healthcare, legal, and real estate, automating document handling is an essential part of the business and customer service. In addition, strict compliance regulations make it necessary for businesses to handle sensitive documents, especially customer data, properly. Documents can come in a variety of formats, including digital forms or […]

Announcing the Amazon S3 plugin for PyTorch

Amazon S3 plugin for PyTorch is an open-source library which is built to be used with the deep learning framework PyTorch for streaming data from Amazon Simple Storage Service (Amazon S3). With this feature available in PyTorch Deep Learning Containers, you can take advantage of using data from S3 buckets directly with PyTorch dataset and […]

Object detection with Detectron2 on Amazon SageMaker

Deep learning is at the forefront of most machine learning (ML) implementations across a broad set of business verticals. Driven by the highly flexible nature of neural networks, the boundary of what is possible has been pushed to a point where neural networks can outperform humans in a variety of tasks, such as object detection […]

Using container images to run PyTorch models in AWS Lambda

PyTorch is an open-source machine learning (ML) library widely used to develop neural networks and ML models. Those models are usually trained on multiple GPU instances to speed up training, resulting in expensive training time and model sizes up to a few gigabytes. After they’re trained, these models are deployed in production to produce inferences. […]

AWS and NVIDIA achieve the fastest training times for Mask R-CNN and T5-3B

Note: At the AWS re:Invent Machine Learning Keynote we announced performance records for T5-3B and Mask-RCNN. This blog post includes updated numbers with additional optimizations since the keynote aired live on 12/8. At re:Invent 2019, we demonstrated the fastest training times on the cloud for Mask R-CNN, a popular instance segmentation model, and BERT, a […]

Deploying PyTorch models for inference at scale using TorchServe

Many services you interact with today rely on machine learning (ML). From online search and product recommendations to speech recognition and language translation, these services need ML models to serve predictions. As ML finds its way into even more services, you face the challenge of taking the results of your hard work and deploying the […]