Amazon Elastic Inference will reduce the costs of in-depth learning by approximately 75% – TechCrunch


Amazon Web Services announced today Amazon Elastic Inference, a new service that allows customers to connect graphical processor-based inference acceleration to any Amazon EC2 instance and reduce the cost Learning in depth up to 75%.

"What we generally see is that the average utilization of these GPUs from P3 instances is about 10 to 30%, which is rather a waste of time with an elastic inference. You do not have to waste all those costs and all that GPU, "said Andy Jassy, ​​general manager of AWS, on stage at the AWS re: Invent conference earlier in the day. "[Amazon Elastic Inference] is a game changer important enough to be able to execute the inference in a much more profitable way. "

Amazon Elastic Inference will also be available for instances and endpoints in the Amazon Sage notebook, "bringing acceleration to embedded algorithms and deep learning environments," the company writes in a blog post. It will support the TensorFlow, Apache MXNet and ONNX machine learning infrastructures.

It is available in three sizes:

  • eia1.medium: 8 TeraFLOP of variable precision performance.
  • eia1.large: 16 TeraFLOP with variable precision performance.
  • eia1.xlarge: 32 TeraFLOP of variable precision performance.

Dive deeper into the new service here.

more coverage AWS re: Invent 2018


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