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Amazon Elastic Inference

  • Allows attaching low-cost GPU-powered inference acceleration to EC2 instances, SageMaker instances, or ECS tasks.
  • Reduce machine learning inference costs by up to 75%.

Common use cases

  • Computer vision
  • Natural language processing
  • Speech recognition
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Concepts

  • Accelerator
    • A GPU-powered hardware device provisioned.
    • It is not a part of the hardware where your instance is hosted.
    • Uses AWS PrivateLink endpoint service to attach to the instance over the network.
  • Only a single endpoint service is required in every Availability Zone to connect Elastic Inference accelerator to instances.

Features

  • Supports TensorFlow, Apache MXNet, PyTorch, and ONNX models.
  • Can provide 1 to 32 trillion floating-point operations per second (TFLOPS) per accelerator.
  • The accelerator attached to each instance in an auto-scaling group scales accordingly to your application’s compute demand.

Pricing

  • You are charged for the accelerator hours you consume.

Note: If you are studying for the AWS Certified Machine Learning Specialty exam, we highly recommend that you take our AWS Certified Machine Learning – Specialty Practice Exams and read our Machine Learning Specialty exam study guide.

AWS Certified Machine Learning Specialty Practice Exams

References:
https://aws.amazon.com/machine-learning/elastic-inference/features/
https://docs.aws.amazon.com/elastic-inference/latest/developerguide/basics.html
https://aws.amazon.com/machine-learning/elastic-inference/pricing/

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Written by: Carlo Acebedo

Carlo is a cloud engineer and a content creator at Tutorials Dojo. He's also a member of the AWS Community builder and holds 5 AWS Certifications. Carlo specializes in building and automating solutions in the Amazon Web Services Cloud.

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