Skip to content

emkessler/sagemaker

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

19 Commits
 
 
 
 
 
 

Repository files navigation

SageMaker for Scientific Computation

Amazon SageMaker is a fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. But besides streamlining the machine learning workflow, SageMaker also provides a powerful, flexible and easy to use compute environment to execute and parallelize a large spectrum of scientific computing tasks. In this notebook we demonstrate how to simulate a simple quantum system using Tensorflow together with Amazon SageMaker's Bring You Own Algorithm (BYOA) functionality.

To get started, navigate to the sagemaker_for_sci_comp folder, open the notebook Superradiance on Sagemaker.ipynb and follow along the steps.

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published