So much data, so little time. Machine learning (ML) experts, data scientists, engineers and enthusiasts have encountered this problem the world over. From natural language processing to computer vision, tabular to time series, and everything in-between, the age-old problem of optimizing for speed when running data against as many GPUs as you can get has inspired countless solutions. Today, we’re happy to announce features for PyTorch developers using native open-source frameworks, like PyTorch Lightning and PyTorch DDP, that will streamline their path to the cloud.
Amazon SageMaker is a fully-managed service for ML, and SageMaker model training is an optimized compute environment for high-performance training at scale. SageMaker model training offers a remote training experience with a seamless control plane to easily train and reproduce ML models at high performance and low cost. We’re thrilled to announce new features in the SageMaker training portfolio that make running PyTorch at scale

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