AWS News Blog

Category: PyTorch on AWS

New – Profile Your Machine Learning Training Jobs With Amazon SageMaker Debugger

Today, I’m extremely happy to announce that Amazon SageMaker Debugger can now profile machine learning models, making it much easier to identify and fix training issues caused by hardware resource usage. Despite its impressive performance on a wide range of business problems, machine learning (ML) remains a bit of a mysterious topic. Getting things right […]

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New – Data Parallelism Library in Amazon SageMaker Simplifies Training on Large Datasets

Today, I’m particularly happy to announce that Amazon SageMaker now supports a new data parallelism library that makes it easier to train models on datasets that may be as large as hundreds or thousands of gigabytes. As data sets and models grow larger and more sophisticated, machine learning (ML) practitioners working on large distributed training […]

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Amazon SageMaker Continues to Lead the Way in Machine Learning and Announces up to 18% Lower Prices on GPU Instances

Since 2006, Amazon Web Services (AWS) has been helping millions of customers build and manage their IT workloads. From startups to large enterprises to public sector, organizations of all sizes use our cloud computing services to reach unprecedented levels of security, resiliency, and scalability. Every day, they’re able to experiment, innovate, and deploy to production […]

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Announcing TorchServe, An Open Source Model Server for PyTorch

PyTorch is one of the most popular open source libraries for deep learning. Developers and researchers particularly enjoy the flexibility it gives them in building and training models. Yet, this is only half the story, and deploying and managing models in production is often the most difficult part of the machine learning process: building bespoke […]

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