Comments (5)
Hi @fernando-mc . Yes, you should be able to run the notebook by changing the instance type to a CPU instance (e.g. ml.c4.xlarge). This will lengthen training time though, so I'd suggest reducing the number of epochs, or requesting a service limit increase so that you're able to train with a GPU instance. Thanks.
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@djarpin thanks, sounds good.
I'd still suggest changing the notebook text to recommend one of these options this since it looks like the defaults have changed.
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@djarpin I tried changing the instance type to ml.c4.xlarge
. It's not supporting for image classification. Got below error.
ClientError: An error occurred (ValidationException) when calling the CreateTrainingJob operation: Instance type ml.c4.xlarge is not supported by algorithm image-classification; only GPU instances are supported.
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Hi @DinukaDayarathna - SageMaker's built-in image classification algorithm does require GPU instances. The original post referred to a CIFAR-10 notebook written in MXNet/Gluon, which doesn't impose the same restriction. There are some similarities between the two, but the SageMaker image classification algorithm has additional functionality baked in, which may result in better models, whereas the MXNet example requires custom coding, but may provide greater flexibility. Thanks.
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Hi @djarpin , Noted with thanks !
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