[][src]Struct rusoto_sagemaker::ResourceConfig

pub struct ResourceConfig {
    pub instance_count: i64,
    pub instance_type: String,
    pub volume_kms_key_id: Option<String>,
    pub volume_size_in_gb: i64,
}

Describes the resources, including ML compute instances and ML storage volumes, to use for model training.

Fields

instance_count: i64

The number of ML compute instances to use. For distributed training, provide a value greater than 1.

instance_type: String

The ML compute instance type.

volume_kms_key_id: Option<String>

The AWS Key Management Service (AWS 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. The VolumeKmsKeyId can be any of the following formats:

  • // KMS Key ID

    "1234abcd-12ab-34cd-56ef-1234567890ab"

  • // Amazon Resource Name (ARN) of a KMS Key

    "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

volume_size_in_gb: i64

The 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.

Trait Implementations

impl Default for ResourceConfig[src]

impl Clone for ResourceConfig[src]

fn clone_from(&mut self, source: &Self)1.0.0[src]

Performs copy-assignment from source. Read more

impl PartialEq<ResourceConfig> for ResourceConfig[src]

impl Debug for ResourceConfig[src]

impl Serialize for ResourceConfig[src]

impl<'de> Deserialize<'de> for ResourceConfig[src]

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