- Distributed Training on Amazon SageMaker accelerates training times: New Distributed Training on Amazon SageMaker makes it possible to train large, complex deep learning models up to two times faster than current approaches. Today, advanced machine learning use cases—such as natural language processing for intelligent assistants, object detection and classification for autonomous vehicles, and image classification for large-scale content moderation—demand increasingly large datasets and more graphics processing unit (GPU) memory for training.
- However, some of these models are too big to fit in the memory provided by a single GPU. Customers can attempt to split models across multiple GPUs, but finding the best way to split the model and adjusting training code can often take weeks of tedious experimentation. To overcome these challenges, Distributed Training on Amazon SageMaker offers two distributed training capabilities that enable developers to train large models up to two times faster at no additional cost. Distributed Training with Amazon SageMaker’s Data Parallelism engine scales training jobs from one GPU to hundreds or thousands by automatically splitting data across multiple GPUs, improving training time by up to 40%.
- The reduction in training time is possible because Amazon SageMaker’s Data Parallelism engine manages GPUs for optimal synchronisation using algorithms that are purposefully built to fully utilise AWS infrastructure with near-linear scaling efficiency. Distributed Training with Amazon SageMaker’s Model Parallelism engine can efficiently split large, complex models with billions of parameters across multiple GPUs by automatically profiling and identifying the best way to partition models. They do this by using graph partitioning algorithms to optimally balance computation and minimise communication between GPUs, resulting in minimal code changes and fewer errors caused by GPU memory constraints.
- Amazon SageMaker Edge Manager model management for edge devices: Amazon SageMaker Edge Manager allows developers to optimise, secure, monitor, and maintain machine learning models deployed on fleets of edge devices. Today, customers use Amazon SageMaker Neo to create optimised models for edge devices that run up to twice as fast, with less than a tenth of the memory footprint and no loss in accuracy. However, after deployment on edge devices, customers still need to manage and monitor the models to ensure they continue to perform with high accuracy. Amazon SageMaker Edge Manager optimises models to run faster on target devices and provides model management for edge devices, so customers can prepare, run, monitor, and update deployed machine learning models across fleets of devices at the edge.
- Amazon SageMaker Edge Manager gives customers the ability to cryptographically sign their models, upload prediction data from their devices to Amazon SageMaker for monitoring and analysis, and view a dashboard that tracks and visually reports on the operation of the deployed models within the Amazon SageMaker console. Amazon SageMaker Edge Manager extends capabilities that were previously only available in the cloud by sampling data from edge devices and sending it to Amazon SageMaker Model Monitor for analysis, so developers can continuously improve model quality by retraining them when their accuracy declines over time.
- Amazon SageMaker JumpStart enables the machine learning journey: Amazon SageMaker JumpStartprovides developers an easy-to-use, searchable interface to find best-in-class solutions, algorithms, and sample notebooks. Today, some customers that lack experience with machine learning have difficulty getting started with machine learning deployments, while more advanced developers find it difficult to adopt machine learning for all of their use cases. With today’s launch of Amazon SageMaker JumpStart, customers can now quickly find relevant information specific to their machine learning use cases. Developers new to machine learning will be able to select from several complete end-to-end machine learning solutions (e.g. fraud detection, customer churn prediction, or forecasting) and deploy them directly in their Amazon SageMaker Studio environments. And, experienced users will be able to choose from more than a hundred machine learning models to quickly get started on building and training models.
“Hundreds of thousands of everyday developers and data scientists have used our industry-leading machine learning service, Amazon SageMaker, to remove barriers to building, training, and deploying custom machine learning models,” said Swami Sivasubramanian, vice president of Amazon Machine Learning at AWS. “One of the best parts about having such a widely-adopted service like SageMaker is that we get lots of customer suggestions which fuel our next set of deliverables.”
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