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1

SageMaker MXNet Inference Toolkit

Toolkit for allowing inference and serving with MXNet in SageMaker

...Deep Learning Containers provide optimized environments with TensorFlow and MXNet, Nvidia CUDA (for GPU instances), and Intel MKL (for CPU instances) libraries and are available in the Amazon Elastic Container Registry (Amazon ECR). The AWS DLCs are used in Amazon SageMaker as the default vehicles for your SageMaker jobs such as training, inference, transforms etc. They've been tested for machine learning workloads on Amazon EC2, Amazon ECS and Amazon EKS services as well.

Downloads: 0 This Week

Last Update: 2022-07-05
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2

AWS Step Functions Data Science SDK

For building machine learning (ML) workflows and pipelines on AWS

...In Amazon SageMaker, example Jupyter notebooks are available in the example notebooks portion of a notebook instance. To run the AWS Step Functions Data Science SDK example notebooks locally, download the sample notebooks and open them in a working Jupyter instance.

Downloads: 0 This Week

Last Update: 2022-07-07
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3

SageMaker TensorFlow Serving Container

A TensorFlow Serving solution for use in SageMaker

SageMaker TensorFlow Serving Container is an a open source project that builds docker images for running TensorFlow Serving on Amazon SageMaker. Some of the build and tests scripts interact with resources in your AWS account. Be sure to set your default AWS credentials and region using aws configure before using these scripts. Amazon SageMaker uses Docker containers to run all training jobs and inference endpoints. The Docker images are built from the Dockerfiles in docker/. ...

Downloads: 0 This Week

Last Update: 2022-07-05
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