Category: PyTorch on AWS

November 2023: On 11/22/2023, AWS announced the Amazon S3 Connector for PyTorch ─ a new connector that delivers high throughput for PyTorch training jobs that access data in Amazon S3. We recommend customers use the new connector for PyTorch training jobs that read and write data in Amazon S3. The Amazon S3 Connector for PyTorch […]

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 […]

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. […]

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 […]

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 […]