Image-classification-transfer-learning Sagemaker issue

Question

Im getting an error like this while trying image classification with Sagemaker:

ClientError: An error occurred (ValidationException) when calling the CreateTrainingJob operation: 1 validation error detected: Value 'ml.t2.medium' at 'resourceConfig.instanceType' failed to satisfy constraint: Member must satisfy enum value set: [ml.p2.xlarge, ml.m5.4xlarge, ml.m4.16xlarge, ml.p3.16xlarge, ml.m5.large, ml.p2.16xlarge, ml.c4.2xlarge, ml.c5.2xlarge, ml.c4.4xlarge, ml.c5.4xlarge, ml.c4.8xlarge, ml.c5.9xlarge, ml.c5.xlarge, ml.c4.xlarge, ml.c5.18xlarge, ml.p3.2xlarge, ml.m5.xlarge, ml.m4.10xlarge, ml.m5.12xlarge, ml.m4.xlarge, ml.m5.24xlarge, ml.m4.2xlarge, ml.p2.8xlarge, ml.m5.2xlarge, ml.p3.8xlarge, ml.m4.4xlarge]

Answer 1

The ml.t2.medium instance type is not available on SageMaker Training as of this writing.

You can refer to https://aws.amazon.com/sagemaker/pricing/ to see the supported instance types in the component and region you are using.

You should also check if the algorithm you are running has additional hardware constraints. For instance, the Image Classification Algorithm doc calls out that it requires GPU instances for training:

For image classification, we support the following GPU instances for training: ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge and ml.p3.16xlarge. We recommend using GPU instances with more memory for training with large batch sizes. However, both CPU (such as C4) and GPU (such as P2 and P3) instances can be used for the inference. You can also run the algorithm on multi-GPU and multi-machine settings for distributed training.

Both P2 and P3 instances are supported in the image classification algorithm.