According to the Sagemaker pricing, I can use 50 hours of m4.xlarge and m5.xlarge instances for training in the free tier. (I am safely within the two-month limit.) But when I attempt to train an algorithm with the XGBoost container using m5. xlarge, I get the error shown below the code.
Are the ml-type and non-ml-type instances the same with just a fancy prefix for those that one would use with Sagemaker or are they entirely different? The EC2 page doesn't even list the ml instances.
sess = sagemaker.Session() xgb = sagemaker.estimator.Estimator(container, role, instance_count=1, instance_type='m5.xlarge', output_path=output_location, sagemaker_session=sess)
But, however, I get the following ERROR:
ClientError: An error occurred (ValidationException) when calling the CreateTrainingJob operation: 1 validation error detected: Value 'm5.xlarge' at 'resourceConfig.instanceType' failed to satisfy constraint: Member must satisfy enum value set: [ml.p2.xlarge, ml.m5.4xlarge, ml.m4.16xlarge, ml.p4d.24xlarge, ml.c5n.xlarge, ml.p3.16xlarge, ml.m5.large, ml.p2.16xlarge, ml.c4.2xlarge, ml.c5.2xlarge, ml.c4.4xlarge, ml.c5.4xlarge, ml.c5n.18xlarge, ml.g4dn.xlarge, ml.g4dn.12xlarge, ml.c4.8xlarge, ml.g4dn.2xlarge, ml.c5.9xlarge, ml.g4dn.4xlarge, ml.c5.xlarge, ml.g4dn.16xlarge, ml.c4.xlarge, ml.g4dn.8xlarge, ml.c5n.2xlarge, ml.c5n.4xlarge, ml.c5.18xlarge, ml.p3dn.24xlarge, ml.p3.2xlarge, ml.m5.xlarge, ml.m4.10xlarge, ml.c5n.9xlarge, ml.m5.12xlarge, ml.m4.xlarge, ml.m5.24xlarge, ml.m4.2xlarge, ml.p2.8xlarge, ml.m5.2xlarge, ml.p3.8xlarge, ml.m4.4xlarge]
Any help will be appreciated!!
