software.amazon.awssdk.services.sagemaker.model

Class S3DataSource

java.lang.Object

software.amazon.awssdk.services.sagemaker.model.S3DataSource

All Implemented Interfaces:

StructuredPojo, ToCopyableBuilder<S3DataSource.Builder,S3DataSource>

@Generated(value="software.amazon.awssdk:codegen")
public final class S3DataSource
extends Object
implements StructuredPojo, ToCopyableBuilder<S3DataSource.Builder,S3DataSource>

Describes the S3 data source.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static interface	S3DataSource.Builder

Method Summary

Modifier and Type	Method and Description
static S3DataSource.Builder	builder()
boolean	equals(Object obj)
<T> Optional<T>	getValueForField(String fieldName, Class<T> clazz)
int	hashCode()
void	marshall(ProtocolMarshaller protocolMarshaller) Marshalls this structured data using the given ProtocolMarshaller.
S3DataDistribution	s3DataDistributionType() If you want Amazon SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated.
String	s3DataDistributionTypeAsString() If you want Amazon SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated.
S3DataType	s3DataType() If you choose S3Prefix, S3Uri identifies a key name prefix.
String	s3DataTypeAsString() If you choose S3Prefix, S3Uri identifies a key name prefix.
String	s3Uri() Depending on the value specified for the S3DataType, identifies either a key name prefix or a manifest.
static Class<? extends S3DataSource.Builder>	serializableBuilderClass()
S3DataSource.Builder	toBuilder() Take this object and create a builder that contains all of the current property values of this object.
String	toString()

Methods inherited from class java.lang.Object

getClass, notify, notifyAll, wait, wait, wait

Methods inherited from interface software.amazon.awssdk.utils.builder.ToCopyableBuilder

copy

Method Detail

s3DataType

public S3DataType s3DataType()

If you choose S3Prefix, S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with 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 the service returns an enum value that is not available in the current SDK version, s3DataType will return S3DataType.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from s3DataTypeAsString().

Returns:

If you choose S3Prefix, S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with 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.

See Also:

S3DataType

s3DataTypeAsString

public String s3DataTypeAsString()

If you choose S3Prefix, S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with 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 the service returns an enum value that is not available in the current SDK version, s3DataType will return S3DataType.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from s3DataTypeAsString().

Returns:

If you choose S3Prefix, S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with 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.

See Also:

S3DataType

s3Uri

public String s3Uri()

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

  The manifest is an S3 object which is a JSON file with the following format:

  [

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

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

  "relative/path/custdata-2",

  ...

  ]

  The preceding JSON matches the following s3Uris:

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

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

  ...

  The complete set of s3uris in this manifest constitutes the input data for the channel for this datasource. The object that each s3uris points to must readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.

Returns:

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

  The manifest is an S3 object which is a JSON file with the following format:

  [

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

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

  "relative/path/custdata-2",

  ...

  ]

  The preceding JSON matches the following s3Uris:

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

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

  ...

  The complete set of s3uris in this manifest constitutes the input data for the channel for this datasource. The object that each s3uris points to must readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.

s3DataDistributionType

public S3DataDistribution s3DataDistributionType()

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.

If the service returns an enum value that is not available in the current SDK version, s3DataDistributionType will return S3DataDistribution.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from s3DataDistributionTypeAsString().

Returns:

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.

See Also:

S3DataDistribution

s3DataDistributionTypeAsString

public String s3DataDistributionTypeAsString()

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.

If the service returns an enum value that is not available in the current SDK version, s3DataDistributionType will return S3DataDistribution.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from s3DataDistributionTypeAsString().

Returns:

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.

See Also:

S3DataDistribution

toBuilder

public S3DataSource.Builder toBuilder()

Description copied from interface: ToCopyableBuilder

Take this object and create a builder that contains all of the current property values of this object.

Specified by:

toBuilder in interface ToCopyableBuilder<S3DataSource.Builder,S3DataSource>

Returns:

a builder for type T

builder

public static S3DataSource.Builder builder()

serializableBuilderClass

public static Class<? extends S3DataSource.Builder> serializableBuilderClass()

hashCode

public int hashCode()

Overrides:

hashCode in class Object

equals

public boolean equals(Object obj)

Overrides:

equals in class Object

toString

public String toString()

Overrides:

toString in class Object

getValueForField

public <T> Optional<T> getValueForField(String fieldName,
                                        Class<T> clazz)

marshall

public void marshall(ProtocolMarshaller protocolMarshaller)

Description copied from interface: StructuredPojo

Marshalls this structured data using the given ProtocolMarshaller.

Specified by:

marshall in interface StructuredPojo

Parameters:

protocolMarshaller - Implementation of ProtocolMarshaller used to marshall this object's data.