Could you explain in more detail why do you want to have sagemaker inside of a lambda please?
Requirement is to train sagemaker model from lambda. So in the trainingconfiguration we have to pass the container for algorithm image.
so container definition can either have hardcoded image uris and regions mapping, or, as below code snipper where you can get the latest image for specified region and algorithm.
container = get_image_uri(boto3.Session().region_name, 'xgboost')
so to access the above api 'get_image_uri' I need to do below import.
from sagemaker.amazon.amazon_estimator import get_image_uri
This is not supported by default in lambda.so I'm trying to package sagemaker dependencies as external dependencies and deploy in lambda.
As s3 allows more size than direct upload, I even tried with uploading from s3 path.
But since sagemaker has numpy dependency and default numpy doesn't work with python containers, I had to add customized numpy packages for aws lambda, which raised the compressed file size to 105MB.
Now that's also not working as it supports only upto 100MB and uncompressed size of less than 250MB.
and reference or suggestion would be of great help.Thank you.
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