If you don't want to use SageMaker's built-in capabilities and want to execute your own algorithms, you can do this by packaging your code into a Docker image and uploading it to AWS (specifically, the Elastic Container Registry, or ECR). Additionally, this is assuming SageMaker's "File" input is used as opposed to "Pipe". This guide is covering Training Jobs only but it's likely the code can be altered for Inference and Hosting. SageMaker has a few different ways of executing code, including Training Jobs and Inferences/Hosting. SageMaker is AWS's Machine Learning platform and uses Jupyter Notebooks and Python. AWS Services (AWS Command Line Interface, SageMaker, S3, ECR, etc.)ġ.Amazon has a large number of repos of there own that give a more complete overview on their technologies, with this mostly filling the gap for executing MATLAB code on the platform.
This Repo provides an approach for executing MATLAB stand-alone executables on AWS SageMaker.
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