Struct aws_sdk_sagemaker::types::builders::S3ModelDataSourceBuilder

#[non_exhaustive]
pub struct S3ModelDataSourceBuilder { /* private fields */ }

A builder for S3ModelDataSource.

Implementations

impl S3ModelDataSourceBuilder

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

Specifies the S3 path of ML model data to deploy.

This field is required.

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

Specifies the S3 path of ML model data to deploy.

pub fn get_s3_uri(&self) -> &Option<String>

Specifies the S3 path of ML model data to deploy.

pub fn s3_data_type(self, input: S3ModelDataType) -> Self

Specifies the type of ML model data to deploy.

If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/).

If you choose S3Object, S3Uri identifies an object that is the ML model data to deploy.

This field is required.

pub fn set_s3_data_type(self, input: Option<S3ModelDataType>) -> Self

Specifies the type of ML model data to deploy.

If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/).

If you choose S3Object, S3Uri identifies an object that is the ML model data to deploy.

pub fn get_s3_data_type(&self) -> &Option<S3ModelDataType>

Specifies the type of ML model data to deploy.

If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/).

If you choose S3Object, S3Uri identifies an object that is the ML model data to deploy.

pub fn compression_type(self, input: ModelCompressionType) -> Self

Specifies how the ML model data is prepared.

If you choose Gzip and choose S3Object as the value of S3DataType, S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.

If you choose None and chooose S3Object as the value of S3DataType, S3Uri identifies an object that represents an uncompressed ML model to deploy.

If you choose None and choose S3Prefix as the value of S3DataType, S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.

If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:

• If you choose S3Object as the value of S3DataType, then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object.

• If you choose S3Prefix as the value of S3DataType, then for each S3 object under the key name pefix referenced by S3Uri, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.

• Do not use any of the following as file names or directory names:

  • An empty or blank string

  • A string which contains null bytes

  • A string longer than 255 bytes

  • A single dot (.)

  • A double dot (..)

• Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType, then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory).

• Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.

This field is required.

pub fn set_compression_type(self, input: Option<ModelCompressionType>) -> Self

Specifies how the ML model data is prepared.

If you choose Gzip and choose S3Object as the value of S3DataType, S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.

If you choose None and chooose S3Object as the value of S3DataType, S3Uri identifies an object that represents an uncompressed ML model to deploy.

If you choose None and choose S3Prefix as the value of S3DataType, S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.

If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:

• If you choose S3Object as the value of S3DataType, then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object.

• If you choose S3Prefix as the value of S3DataType, then for each S3 object under the key name pefix referenced by S3Uri, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.

• Do not use any of the following as file names or directory names:

  • An empty or blank string

  • A string which contains null bytes

  • A string longer than 255 bytes

  • A single dot (.)

  • A double dot (..)

• Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType, then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory).

• Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.

pub fn get_compression_type(&self) -> &Option<ModelCompressionType>

Specifies how the ML model data is prepared.

If you choose Gzip and choose S3Object as the value of S3DataType, S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.

If you choose None and chooose S3Object as the value of S3DataType, S3Uri identifies an object that represents an uncompressed ML model to deploy.

If you choose None and choose S3Prefix as the value of S3DataType, S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.

If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:

• If you choose S3Object as the value of S3DataType, then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object.

• If you choose S3Prefix as the value of S3DataType, then for each S3 object under the key name pefix referenced by S3Uri, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.

• Do not use any of the following as file names or directory names:

  • An empty or blank string

  • A string which contains null bytes

  • A string longer than 255 bytes

  • A single dot (.)

  • A double dot (..)

• Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType, then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory).

• Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.

pub fn model_access_config(self, input: ModelAccessConfig) -> Self

Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.

pub fn set_model_access_config(self, input: Option<ModelAccessConfig>) -> Self

Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.

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

Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.

pub fn hub_access_config(self, input: InferenceHubAccessConfig) -> Self

Configuration information for hub access.

pub fn set_hub_access_config( self, input: Option<InferenceHubAccessConfig>, ) -> Self

Configuration information for hub access.

pub fn get_hub_access_config(&self) -> &Option<InferenceHubAccessConfig>

Configuration information for hub access.

pub fn build(self) -> S3ModelDataSource

Consumes the builder and constructs a S3ModelDataSource.

Trait Implementations

impl Clone for S3ModelDataSourceBuilder

fn clone(&self) -> S3ModelDataSourceBuilder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for S3ModelDataSourceBuilder

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

Formats the value using the given formatter.

impl Default for S3ModelDataSourceBuilder

fn default() -> S3ModelDataSourceBuilder

Returns the “default value” for a type.

impl PartialEq for S3ModelDataSourceBuilder

fn eq(&self, other: &S3ModelDataSourceBuilder) -> 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 S3ModelDataSourceBuilder