aws_sdk_sagemaker::types::builders

Struct AutoMls3DataSourceBuilder

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#[non_exhaustive]
pub struct AutoMls3DataSourceBuilder { /* private fields */ }
Expand description

A builder for AutoMls3DataSource.

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impl AutoMls3DataSourceBuilder

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pub fn s3_data_type(self, input: AutoMls3DataType) -> Self

The data type.

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

    The S3Prefix should have the following format:

    s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER-OR-FILE

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

    A ManifestFile should have the format shown below:

    \[ {"prefix": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/DOC-EXAMPLE-PREFIX/"},

    "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-1",

    "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-2",

    ... "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-N" \]

  • 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 is available for V2 API jobs only (for example, for jobs created by calling CreateAutoMLJobV2).

    Here is a minimal, single-record example of an AugmentedManifestFile:

    {"source-ref": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/cats/cat.jpg",

    "label-metadata": {"class-name": "cat" }

    For more information on AugmentedManifestFile, see Provide Dataset Metadata to Training Jobs with an Augmented Manifest File.

This field is required.
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pub fn set_s3_data_type(self, input: Option<AutoMls3DataType>) -> Self

The data type.

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

    The S3Prefix should have the following format:

    s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER-OR-FILE

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

    A ManifestFile should have the format shown below:

    \[ {"prefix": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/DOC-EXAMPLE-PREFIX/"},

    "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-1",

    "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-2",

    ... "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-N" \]

  • 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 is available for V2 API jobs only (for example, for jobs created by calling CreateAutoMLJobV2).

    Here is a minimal, single-record example of an AugmentedManifestFile:

    {"source-ref": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/cats/cat.jpg",

    "label-metadata": {"class-name": "cat" }

    For more information on AugmentedManifestFile, see Provide Dataset Metadata to Training Jobs with an Augmented Manifest File.

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pub fn get_s3_data_type(&self) -> &Option<AutoMls3DataType>

The data type.

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

    The S3Prefix should have the following format:

    s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER-OR-FILE

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

    A ManifestFile should have the format shown below:

    \[ {"prefix": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/DOC-EXAMPLE-PREFIX/"},

    "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-1",

    "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-2",

    ... "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-N" \]

  • 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 is available for V2 API jobs only (for example, for jobs created by calling CreateAutoMLJobV2).

    Here is a minimal, single-record example of an AugmentedManifestFile:

    {"source-ref": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/cats/cat.jpg",

    "label-metadata": {"class-name": "cat" }

    For more information on AugmentedManifestFile, see Provide Dataset Metadata to Training Jobs with an Augmented Manifest File.

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

The URL to the Amazon S3 data source. The Uri refers to the Amazon S3 prefix or ManifestFile depending on the data type.

This field is required.
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pub fn set_s3_uri(self, input: Option<String>) -> Self

The URL to the Amazon S3 data source. The Uri refers to the Amazon S3 prefix or ManifestFile depending on the data type.

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

The URL to the Amazon S3 data source. The Uri refers to the Amazon S3 prefix or ManifestFile depending on the data type.

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pub fn build(self) -> AutoMls3DataSource

Consumes the builder and constructs a AutoMls3DataSource.

Trait Implementations§

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impl Clone for AutoMls3DataSourceBuilder

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fn clone(&self) -> AutoMls3DataSourceBuilder

Returns a duplicate of the value. Read more
1.0.0 · Source§

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

Performs copy-assignment from source. Read more
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impl Debug for AutoMls3DataSourceBuilder

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

Formats the value using the given formatter. Read more
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impl Default for AutoMls3DataSourceBuilder

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fn default() -> AutoMls3DataSourceBuilder

Returns the “default value” for a type. Read more
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impl PartialEq for AutoMls3DataSourceBuilder

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fn eq(&self, other: &AutoMls3DataSourceBuilder) -> bool

Tests for self and other values to be equal, and is used by ==.
1.0.0 · Source§

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

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.
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impl StructuralPartialEq for AutoMls3DataSourceBuilder

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