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NAMEPaws::SageMaker::ResourceConfig USAGEThis class represents one of two things: Arguments in a call to a service Use the attributes of this class as arguments to methods. You shouldn't make instances of this class. Each attribute should be used as a named argument in the calls that expect this type of object. As an example, if Att1 is expected to be a Paws::SageMaker::ResourceConfig object: $service_obj->Method(Att1 => { InstanceCount => $value, ..., VolumeSizeInGB => $value }); Results returned from an API call Use accessors for each attribute. If Att1 is expected to be an Paws::SageMaker::ResourceConfig object: $result = $service_obj->Method(...); $result->Att1->InstanceCount DESCRIPTIONDescribes the resources, including ML compute instances and ML storage volumes, to use for model training. ATTRIBUTESREQUIRED InstanceCount => IntThe number of ML compute instances to use. For distributed training, provide a value greater than 1. REQUIRED InstanceType => StrThe ML compute instance type. VolumeKmsKeyId => StrThe Amazon Web Services KMS key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job. Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a "VolumeKmsKeyId" when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes (https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/InstanceStorage.html#instance-store-volumes). For more information about local instance storage encryption, see SSD Instance Store Volumes (https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ssd-instance-store.html). The "VolumeKmsKeyId" can be in any of the following formats:
REQUIRED VolumeSizeInGB => IntThe size of the ML storage volume that you want to provision. ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose "File" as the "TrainingInputMode" in the algorithm specification. You must specify sufficient ML storage for your scenario. Amazon SageMaker supports only the General Purpose SSD (gp2) ML storage volume type. Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for training, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request a "VolumeSizeInGB" greater than the total size of the local instance storage. For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes (https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/InstanceStorage.html#instance-store-volumes). SEE ALSOThis class forms part of Paws, describing an object used in Paws::SageMaker BUGS and CONTRIBUTIONSThe source code is located here: <https://github.com/pplu/aws-sdk-perl> Please report bugs to: <https://github.com/pplu/aws-sdk-perl/issues>
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