create_model.py

import time
timestamp = time.strftime('-%Y-%m-%d-%H-%M-%S', time.gmtime())
model_name = training_job_name + '-model' + timestamp

training_image = training_info['AlgorithmSpecification']['TrainingImage']
model_data = training_info['ModelArtifacts']['S3ModelArtifacts']

primary_container = {
    'Image': training_image,
    'ModelDataUrl': model_data,
}

from sagemaker import get_execution_role

role = get_execution_role()

create_model_response = client.create_model(
    ModelName = model_name,
    ExecutionRoleArn = role,
    PrimaryContainer = primary_container)

print(create_model_response['ModelArn'])