AWS Sagemaker Deploy fails

Question

I am following the sage maker documentation to train and deploy an ML model. I am using the high-level Python library provided by Amazon SageMaker to achieve this.

kmeans_predictor = kmeans.deploy(initial_instance_count=1,
                                 instance_type='ml.m4.xlarge')

The deployment fails with error

ResourceLimitExceeded: An error occurred (ResourceLimitExceeded) when calling the CreateEndpoint operation: The account-level service limit 'ml.c4.8xlarge for endpoint usage' is 0 Instances, with current utilization of 0 Instances and a request delta of 1 Instances.

Where am I going wrong?

Answer 1

I resolved the issue by changing the instance type:

kmeans_predictor = kmeans.deploy(initial_instance_count=1,
                                 instance_type='ml.t2.medium')

Answer 2

Answer

Under free_tier AWS account, use 'InstanceType':'ml.t2.medium' to successfully deploy a machine learning model. By default, if you are following AWS tutorials online, you will end up using 'ml.m4.xlarge' which leads to this error.

Therefore, use 'InstanceType':'ml.t2.medium' instead of 'ml.m4.xlarge' instance in code spinets shown in the image below.

The error is due to account-level service limit. Free_tier account get the error when using EC2 instance type 'ml.m4.xlarge'.Therefore, use 'ml.t2.medium' instead of ml.m4.xlarge'. Usually, while creating AWS endpoint, free_account holders get below error:

ResourceLimitExceeded: An error occurred (ResourceLimitExceeded) when calling the CreateEndpoint operation: The account-level service limit 'ml.m4.xlarge for endpoint usage' is 0 Instances, with current utilization of 0 Instances and a request delta of 1 Instances. Please contact AWS support to request an increase for this limit.

CODE Change to successfully deploy machine learning model on AWS: