Struct aws_sdk_sagemaker::model::s3_data_source::Builder

#[non_exhaustive]
pub struct Builder { /* private fields */ }

A builder for S3DataSource

Implementations

impl Builder

pub fn s3_data_type(self, input: S3DataType) -> Self

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.

pub fn set_s3_data_type(self, input: Option<S3DataType>) -> Self

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.

pub fn s3_uri(self, input: impl Into<String>) -> Self

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

  A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri. Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets.

  The following code example shows a valid manifest format:

  [ {"prefix": "s3://customer_bucket/some/prefix/"},

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

  "relative/path/custdata-2",

  ...

  "relative/path/custdata-N"

  ]

  This JSON is equivalent to the following S3Uri list:

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

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

  ...

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

  The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.

pub fn set_s3_uri(self, input: Option<String>) -> Self

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

  A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri. Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets.

  The following code example shows a valid manifest format:

  [ {"prefix": "s3://customer_bucket/some/prefix/"},

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

  "relative/path/custdata-2",

  ...

  "relative/path/custdata-N"

  ]

  This JSON is equivalent to the following S3Uri list:

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

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

  ...

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

  The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.

pub fn s3_data_distribution_type(self, input: S3DataDistribution) -> Self

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.

pub fn set_s3_data_distribution_type(
    self,
    input: Option<S3DataDistribution>
) -> Self

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.

pub fn attribute_names(self, input: impl Into<String>) -> Self

Appends an item to attribute_names.

To override the contents of this collection use set_attribute_names.

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

pub fn set_attribute_names(self, input: Option<Vec<String>>) -> Self

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

pub fn build(self) -> S3DataSource

Consumes the builder and constructs a S3DataSource

Trait Implementations

impl Clone for Builder

fn clone(&self) -> Builder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for Builder

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

Formats the value using the given formatter.

impl Default for Builder

fn default() -> Builder

Returns the “default value” for a type.

impl PartialEq<Builder> for Builder

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

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

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

This method tests for !=.

impl StructuralPartialEq for Builder