aws_sdk_sagemaker::types::builders

Struct S3DataSourceBuilder

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#[non_exhaustive]
pub struct S3DataSourceBuilder { /* private fields */ }
Expand description

A builder for S3DataSource.

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impl S3DataSourceBuilder

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

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.

This field is required.
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pub fn set_s3_data_type(self, input: Option<S3DataType>) -> Self

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.

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pub fn get_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.

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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 SageMaker uses to perform tasks on your behalf.

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

This field is required.
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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 SageMaker uses to perform tasks on your behalf.

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

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pub fn get_s3_uri(&self) -> &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.

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

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.

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

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.

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pub fn get_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.

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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.

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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.

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pub fn get_attribute_names(&self) -> &Option<Vec<String>>

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

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

Appends an item to instance_group_names.

To override the contents of this collection use set_instance_group_names.

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

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

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

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pub fn get_instance_group_names(&self) -> &Option<Vec<String>>

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

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

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.

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

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.

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pub fn get_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.

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

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

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

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

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

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

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pub fn build(self) -> S3DataSource

Consumes the builder and constructs a S3DataSource.

Trait Implementations§

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impl Clone for S3DataSourceBuilder

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fn clone(&self) -> S3DataSourceBuilder

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 S3DataSourceBuilder

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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 S3DataSourceBuilder

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fn default() -> S3DataSourceBuilder

Returns the “default value” for a type. Read more
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impl PartialEq for S3DataSourceBuilder

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fn eq(&self, other: &S3DataSourceBuilder) -> bool

Tests for self and other values to be equal, and is used by ==.
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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 S3DataSourceBuilder

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impl<T> Same for T

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type Output = T

Should always be Self
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impl<T> ToOwned for T
where T: Clone,

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type Owned = T

The resulting type after obtaining ownership.
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fn to_owned(&self) -> T

Creates owned data from borrowed data, usually by cloning. Read more
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fn clone_into(&self, target: &mut T)

Uses borrowed data to replace owned data, usually by cloning. Read more
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impl<T, U> TryFrom<U> for T
where U: Into<T>,

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type Error = Infallible

The type returned in the event of a conversion error.
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fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>

Performs the conversion.
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impl<T, U> TryInto<U> for T
where U: TryFrom<T>,

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type Error = <U as TryFrom<T>>::Error

The type returned in the event of a conversion error.
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fn try_into(self) -> Result<U, <U as TryFrom<T>>::Error>

Performs the conversion.
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impl<T> WithSubscriber for T

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fn with_subscriber<S>(self, subscriber: S) -> WithDispatch<Self>
where S: Into<Dispatch>,

Attaches the provided Subscriber to this type, returning a WithDispatch wrapper. Read more
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fn with_current_subscriber(self) -> WithDispatch<Self>

Attaches the current default Subscriber to this type, returning a WithDispatch wrapper. Read more
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impl<T> ErasedDestructor for T
where T: 'static,