Struct rusoto_sagemaker::S3DataSource

pub struct S3DataSource {
    pub attribute_names: Option<Vec<String>>,
    pub s3_data_distribution_type: Option<String>,
    pub s3_data_type: String,
    pub s3_uri: String,
}

Describes the S3 data source.

Fields

attribute_names: Option<Vec<String>>

A list of one or more attribute names to use that are found in a specified augmented manifest file.

s3_data_distribution_type: Option<String>

If you want Amazon SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated.

If you want Amazon SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key. If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.

Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.

In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key. If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File), this copies 1/n of the number of objects.

s3_data_type: String

If you choose S3Prefix, S3Uri identifies a key name prefix. Amazon SageMaker uses all objects that match the specified key name prefix for model training.

If you choose ManifestFile, S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for model training.

If you choose AugmentedManifestFile, S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe.

s3_uri: String

Depending on the value specified for the S3DataType, identifies either a key name prefix or a manifest. For example:

• A key name prefix might look like this: s3://bucketname/exampleprefix.

• A manifest might look like this: s3://bucketname/example.manifest

  The manifest is an S3 object which is a JSON file with the following format:

  [

  {"prefix": "s3://customerbucket/some/prefix/"},

  "relative/path/to/custdata-1",

  "relative/path/custdata-2",

  ...

  ]

  The preceding JSON matches the following s3Uris:

  s3://customerbucket/some/prefix/relative/path/to/custdata-1

  s3://customer_bucket/some/prefix/relative/path/custdata-2

  ...

  The complete set of s3uris in this manifest is the input data for the channel for this datasource. The object that each s3uris points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.

Trait Implementations

impl PartialEq<S3DataSource> for S3DataSource

fn eq(&self, other: &S3DataSource) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &S3DataSource) -> bool

This method tests for !=.

impl Default for S3DataSource

fn default() -> S3DataSource

Returns the "default value" for a type.

impl Clone for S3DataSource

fn clone(&self) -> S3DataSource

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for S3DataSource

fn fmt(&self, f: &mut Formatter) -> Result

Formats the value using the given formatter.

impl Serialize for S3DataSource

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error> where
    __S: Serializer, 

Serialize this value into the given Serde serializer.

impl<'de> Deserialize<'de> for S3DataSource

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
    __D: Deserializer<'de>, 

Deserialize this value from the given Serde deserializer.