Struct S3DataSource

#[non_exhaustive]
pub struct S3DataSource {
    pub s3_data_type: Option<S3DataType>,
    pub s3_uri: Option<String>,
    pub s3_data_distribution_type: Option<S3DataDistribution>,
    pub attribute_names: Option<Vec<String>>,
    pub instance_group_names: Option<Vec<String>>,
    pub model_access_config: Option<ModelAccessConfig>,
    pub hub_access_config: Option<HubAccessConfig>,
}

Describes the S3 data source.

Your input bucket must be in the same Amazon Web Services region as your training job.

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

s3_data_type: Option<S3DataType>

If you choose S3Prefix, S3Uri identifies a key name prefix. 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 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.

If you choose Converse, S3Uri identifies an Amazon S3 location that contains data formatted according to Converse format. This format structures conversational messages with specific roles and content types used for training and fine-tuning foundational models.

s3_uri: Option<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

  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 SageMaker uses to perform tasks on your behalf.

Your input bucket must be located in same Amazon Web Services region as your training job.

s3_data_distribution_type: Option<S3DataDistribution>

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

If you want 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.

attribute_names: Option<Vec<String>>

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

instance_group_names: Option<Vec<String>>

A list of names of instance groups that get data from the S3 data source.

model_access_config: Option<ModelAccessConfig>

The access configuration file to control access to the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig.

• If you are a Jumpstart user, see the End-user license agreements section for more details on accepting the EULA.

• If you are an AutoML user, see the Optional Parameters section of Create an AutoML job to fine-tune text generation models using the API for details on How to set the EULA acceptance when fine-tuning a model using the AutoML API.

hub_access_config: Option<HubAccessConfig>

The configuration for a private hub model reference that points to a SageMaker JumpStart public hub model.

Implementations

impl S3DataSource

pub fn s3_data_type(&self) -> Option<&S3DataType>

If you choose S3Prefix, S3Uri identifies a key name prefix. 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 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.

If you choose Converse, S3Uri identifies an Amazon S3 location that contains data formatted according to Converse format. This format structures conversational messages with specific roles and content types used for training and fine-tuning foundational models.

pub fn s3_uri(&self) -> Option<&str>

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 SageMaker uses to perform tasks on your behalf.

Your input bucket must be located in same Amazon Web Services region as your training job.

pub fn s3_data_distribution_type(&self) -> Option<&S3DataDistribution>

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

If you want 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) -> &[String]

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

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .attribute_names.is_none().

pub fn instance_group_names(&self) -> &[String]

A list of names of instance groups that get data from the S3 data source.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .instance_group_names.is_none().

pub fn model_access_config(&self) -> Option<&ModelAccessConfig>

The access configuration file to control access to the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig.

• If you are a Jumpstart user, see the End-user license agreements section for more details on accepting the EULA.

• If you are an AutoML user, see the Optional Parameters section of Create an AutoML job to fine-tune text generation models using the API for details on How to set the EULA acceptance when fine-tuning a model using the AutoML API.

pub fn hub_access_config(&self) -> Option<&HubAccessConfig>

The configuration for a private hub model reference that points to a SageMaker JumpStart public hub model.

impl S3DataSource

pub fn builder() -> S3DataSourceBuilder

Creates a new builder-style object to manufacture S3DataSource.

Trait Implementations

impl Clone for S3DataSource

fn clone(&self) -> S3DataSource

Returns a duplicate 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 PartialEq for S3DataSource

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

Tests for self and other values to be equal, and is used by ==.

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

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for S3DataSource