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pxz-hos-client-cpp-module/support/aws-sdk-cpp-master/aws-cpp-sdk-sagemaker/include/aws/sagemaker/model/S3DataSource.h

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/**
* Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
* SPDX-License-Identifier: Apache-2.0.
*/
#pragma once
#include <aws/sagemaker/SageMaker_EXPORTS.h>
#include <aws/sagemaker/model/S3DataType.h>
#include <aws/core/utils/memory/stl/AWSString.h>
#include <aws/sagemaker/model/S3DataDistribution.h>
#include <aws/core/utils/memory/stl/AWSVector.h>
#include <utility>
namespace Aws
{
namespace Utils
{
namespace Json
{
class JsonValue;
class JsonView;
} // namespace Json
} // namespace Utils
namespace SageMaker
{
namespace Model
{
/**
* <p>Describes the S3 data source.</p><p><h3>See Also:</h3> <a
* href="http://docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/S3DataSource">AWS
* API Reference</a></p>
*/
class AWS_SAGEMAKER_API S3DataSource
{
public:
S3DataSource();
S3DataSource(Aws::Utils::Json::JsonView jsonValue);
S3DataSource& operator=(Aws::Utils::Json::JsonView jsonValue);
Aws::Utils::Json::JsonValue Jsonize() const;
/**
* <p>If you choose <code>S3Prefix</code>, <code>S3Uri</code> identifies a key name
* prefix. Amazon SageMaker uses all objects that match the specified key name
* prefix for model training. </p> <p>If you choose <code>ManifestFile</code>,
* <code>S3Uri</code> identifies an object that is a manifest file containing a
* list of object keys that you want Amazon SageMaker to use for model training.
* </p> <p>If you choose <code>AugmentedManifestFile</code>, 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.
* <code>AugmentedManifestFile</code> can only be used if the Channel's input mode
* is <code>Pipe</code>.</p>
*/
inline const S3DataType& GetS3DataType() const{ return m_s3DataType; }
/**
* <p>If you choose <code>S3Prefix</code>, <code>S3Uri</code> identifies a key name
* prefix. Amazon SageMaker uses all objects that match the specified key name
* prefix for model training. </p> <p>If you choose <code>ManifestFile</code>,
* <code>S3Uri</code> identifies an object that is a manifest file containing a
* list of object keys that you want Amazon SageMaker to use for model training.
* </p> <p>If you choose <code>AugmentedManifestFile</code>, 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.
* <code>AugmentedManifestFile</code> can only be used if the Channel's input mode
* is <code>Pipe</code>.</p>
*/
inline bool S3DataTypeHasBeenSet() const { return m_s3DataTypeHasBeenSet; }
/**
* <p>If you choose <code>S3Prefix</code>, <code>S3Uri</code> identifies a key name
* prefix. Amazon SageMaker uses all objects that match the specified key name
* prefix for model training. </p> <p>If you choose <code>ManifestFile</code>,
* <code>S3Uri</code> identifies an object that is a manifest file containing a
* list of object keys that you want Amazon SageMaker to use for model training.
* </p> <p>If you choose <code>AugmentedManifestFile</code>, 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.
* <code>AugmentedManifestFile</code> can only be used if the Channel's input mode
* is <code>Pipe</code>.</p>
*/
inline void SetS3DataType(const S3DataType& value) { m_s3DataTypeHasBeenSet = true; m_s3DataType = value; }
/**
* <p>If you choose <code>S3Prefix</code>, <code>S3Uri</code> identifies a key name
* prefix. Amazon SageMaker uses all objects that match the specified key name
* prefix for model training. </p> <p>If you choose <code>ManifestFile</code>,
* <code>S3Uri</code> identifies an object that is a manifest file containing a
* list of object keys that you want Amazon SageMaker to use for model training.
* </p> <p>If you choose <code>AugmentedManifestFile</code>, 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.
* <code>AugmentedManifestFile</code> can only be used if the Channel's input mode
* is <code>Pipe</code>.</p>
*/
inline void SetS3DataType(S3DataType&& value) { m_s3DataTypeHasBeenSet = true; m_s3DataType = std::move(value); }
/**
* <p>If you choose <code>S3Prefix</code>, <code>S3Uri</code> identifies a key name
* prefix. Amazon SageMaker uses all objects that match the specified key name
* prefix for model training. </p> <p>If you choose <code>ManifestFile</code>,
* <code>S3Uri</code> identifies an object that is a manifest file containing a
* list of object keys that you want Amazon SageMaker to use for model training.
* </p> <p>If you choose <code>AugmentedManifestFile</code>, 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.
* <code>AugmentedManifestFile</code> can only be used if the Channel's input mode
* is <code>Pipe</code>.</p>
*/
inline S3DataSource& WithS3DataType(const S3DataType& value) { SetS3DataType(value); return *this;}
/**
* <p>If you choose <code>S3Prefix</code>, <code>S3Uri</code> identifies a key name
* prefix. Amazon SageMaker uses all objects that match the specified key name
* prefix for model training. </p> <p>If you choose <code>ManifestFile</code>,
* <code>S3Uri</code> identifies an object that is a manifest file containing a
* list of object keys that you want Amazon SageMaker to use for model training.
* </p> <p>If you choose <code>AugmentedManifestFile</code>, 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.
* <code>AugmentedManifestFile</code> can only be used if the Channel's input mode
* is <code>Pipe</code>.</p>
*/
inline S3DataSource& WithS3DataType(S3DataType&& value) { SetS3DataType(std::move(value)); return *this;}
/**
* <p>Depending on the value specified for the <code>S3DataType</code>, identifies
* either a key name prefix or a manifest. For example: </p> <ul> <li> <p> A key
* name prefix might look like this: <code>s3://bucketname/exampleprefix</code>
* </p> </li> <li> <p> A manifest might look like this:
* <code>s3://bucketname/example.manifest</code> </p> <p> 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 <code>S3Uri</code>. Note
* that the prefix must be a valid non-empty <code>S3Uri</code> that precludes
* users from specifying a manifest whose individual <code>S3Uri</code> is sourced
* from different S3 buckets.</p> <p> The following code example shows a valid
* manifest format: </p> <p> <code>[ {"prefix":
* "s3://customer_bucket/some/prefix/"},</code> </p> <p> <code>
* "relative/path/to/custdata-1",</code> </p> <p> <code>
* "relative/path/custdata-2",</code> </p> <p> <code> ...</code> </p> <p> <code>
* "relative/path/custdata-N"</code> </p> <p> <code>]</code> </p> <p> This JSON is
* equivalent to the following <code>S3Uri</code> list:</p> <p>
* <code>s3://customer_bucket/some/prefix/relative/path/to/custdata-1</code> </p>
* <p> <code>s3://customer_bucket/some/prefix/relative/path/custdata-2</code> </p>
* <p> <code>...</code> </p> <p>
* <code>s3://customer_bucket/some/prefix/relative/path/custdata-N</code> </p>
* <p>The complete set of <code>S3Uri</code> in this manifest is the input data for
* the channel for this data source. The object that each <code>S3Uri</code> points
* to must be readable by the IAM role that Amazon SageMaker uses to perform tasks
* on your behalf. </p> </li> </ul>
*/
inline const Aws::String& GetS3Uri() const{ return m_s3Uri; }
/**
* <p>Depending on the value specified for the <code>S3DataType</code>, identifies
* either a key name prefix or a manifest. For example: </p> <ul> <li> <p> A key
* name prefix might look like this: <code>s3://bucketname/exampleprefix</code>
* </p> </li> <li> <p> A manifest might look like this:
* <code>s3://bucketname/example.manifest</code> </p> <p> 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 <code>S3Uri</code>. Note
* that the prefix must be a valid non-empty <code>S3Uri</code> that precludes
* users from specifying a manifest whose individual <code>S3Uri</code> is sourced
* from different S3 buckets.</p> <p> The following code example shows a valid
* manifest format: </p> <p> <code>[ {"prefix":
* "s3://customer_bucket/some/prefix/"},</code> </p> <p> <code>
* "relative/path/to/custdata-1",</code> </p> <p> <code>
* "relative/path/custdata-2",</code> </p> <p> <code> ...</code> </p> <p> <code>
* "relative/path/custdata-N"</code> </p> <p> <code>]</code> </p> <p> This JSON is
* equivalent to the following <code>S3Uri</code> list:</p> <p>
* <code>s3://customer_bucket/some/prefix/relative/path/to/custdata-1</code> </p>
* <p> <code>s3://customer_bucket/some/prefix/relative/path/custdata-2</code> </p>
* <p> <code>...</code> </p> <p>
* <code>s3://customer_bucket/some/prefix/relative/path/custdata-N</code> </p>
* <p>The complete set of <code>S3Uri</code> in this manifest is the input data for
* the channel for this data source. The object that each <code>S3Uri</code> points
* to must be readable by the IAM role that Amazon SageMaker uses to perform tasks
* on your behalf. </p> </li> </ul>
*/
inline bool S3UriHasBeenSet() const { return m_s3UriHasBeenSet; }
/**
* <p>Depending on the value specified for the <code>S3DataType</code>, identifies
* either a key name prefix or a manifest. For example: </p> <ul> <li> <p> A key
* name prefix might look like this: <code>s3://bucketname/exampleprefix</code>
* </p> </li> <li> <p> A manifest might look like this:
* <code>s3://bucketname/example.manifest</code> </p> <p> 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 <code>S3Uri</code>. Note
* that the prefix must be a valid non-empty <code>S3Uri</code> that precludes
* users from specifying a manifest whose individual <code>S3Uri</code> is sourced
* from different S3 buckets.</p> <p> The following code example shows a valid
* manifest format: </p> <p> <code>[ {"prefix":
* "s3://customer_bucket/some/prefix/"},</code> </p> <p> <code>
* "relative/path/to/custdata-1",</code> </p> <p> <code>
* "relative/path/custdata-2",</code> </p> <p> <code> ...</code> </p> <p> <code>
* "relative/path/custdata-N"</code> </p> <p> <code>]</code> </p> <p> This JSON is
* equivalent to the following <code>S3Uri</code> list:</p> <p>
* <code>s3://customer_bucket/some/prefix/relative/path/to/custdata-1</code> </p>
* <p> <code>s3://customer_bucket/some/prefix/relative/path/custdata-2</code> </p>
* <p> <code>...</code> </p> <p>
* <code>s3://customer_bucket/some/prefix/relative/path/custdata-N</code> </p>
* <p>The complete set of <code>S3Uri</code> in this manifest is the input data for
* the channel for this data source. The object that each <code>S3Uri</code> points
* to must be readable by the IAM role that Amazon SageMaker uses to perform tasks
* on your behalf. </p> </li> </ul>
*/
inline void SetS3Uri(const Aws::String& value) { m_s3UriHasBeenSet = true; m_s3Uri = value; }
/**
* <p>Depending on the value specified for the <code>S3DataType</code>, identifies
* either a key name prefix or a manifest. For example: </p> <ul> <li> <p> A key
* name prefix might look like this: <code>s3://bucketname/exampleprefix</code>
* </p> </li> <li> <p> A manifest might look like this:
* <code>s3://bucketname/example.manifest</code> </p> <p> 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 <code>S3Uri</code>. Note
* that the prefix must be a valid non-empty <code>S3Uri</code> that precludes
* users from specifying a manifest whose individual <code>S3Uri</code> is sourced
* from different S3 buckets.</p> <p> The following code example shows a valid
* manifest format: </p> <p> <code>[ {"prefix":
* "s3://customer_bucket/some/prefix/"},</code> </p> <p> <code>
* "relative/path/to/custdata-1",</code> </p> <p> <code>
* "relative/path/custdata-2",</code> </p> <p> <code> ...</code> </p> <p> <code>
* "relative/path/custdata-N"</code> </p> <p> <code>]</code> </p> <p> This JSON is
* equivalent to the following <code>S3Uri</code> list:</p> <p>
* <code>s3://customer_bucket/some/prefix/relative/path/to/custdata-1</code> </p>
* <p> <code>s3://customer_bucket/some/prefix/relative/path/custdata-2</code> </p>
* <p> <code>...</code> </p> <p>
* <code>s3://customer_bucket/some/prefix/relative/path/custdata-N</code> </p>
* <p>The complete set of <code>S3Uri</code> in this manifest is the input data for
* the channel for this data source. The object that each <code>S3Uri</code> points
* to must be readable by the IAM role that Amazon SageMaker uses to perform tasks
* on your behalf. </p> </li> </ul>
*/
inline void SetS3Uri(Aws::String&& value) { m_s3UriHasBeenSet = true; m_s3Uri = std::move(value); }
/**
* <p>Depending on the value specified for the <code>S3DataType</code>, identifies
* either a key name prefix or a manifest. For example: </p> <ul> <li> <p> A key
* name prefix might look like this: <code>s3://bucketname/exampleprefix</code>
* </p> </li> <li> <p> A manifest might look like this:
* <code>s3://bucketname/example.manifest</code> </p> <p> 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 <code>S3Uri</code>. Note
* that the prefix must be a valid non-empty <code>S3Uri</code> that precludes
* users from specifying a manifest whose individual <code>S3Uri</code> is sourced
* from different S3 buckets.</p> <p> The following code example shows a valid
* manifest format: </p> <p> <code>[ {"prefix":
* "s3://customer_bucket/some/prefix/"},</code> </p> <p> <code>
* "relative/path/to/custdata-1",</code> </p> <p> <code>
* "relative/path/custdata-2",</code> </p> <p> <code> ...</code> </p> <p> <code>
* "relative/path/custdata-N"</code> </p> <p> <code>]</code> </p> <p> This JSON is
* equivalent to the following <code>S3Uri</code> list:</p> <p>
* <code>s3://customer_bucket/some/prefix/relative/path/to/custdata-1</code> </p>
* <p> <code>s3://customer_bucket/some/prefix/relative/path/custdata-2</code> </p>
* <p> <code>...</code> </p> <p>
* <code>s3://customer_bucket/some/prefix/relative/path/custdata-N</code> </p>
* <p>The complete set of <code>S3Uri</code> in this manifest is the input data for
* the channel for this data source. The object that each <code>S3Uri</code> points
* to must be readable by the IAM role that Amazon SageMaker uses to perform tasks
* on your behalf. </p> </li> </ul>
*/
inline void SetS3Uri(const char* value) { m_s3UriHasBeenSet = true; m_s3Uri.assign(value); }
/**
* <p>Depending on the value specified for the <code>S3DataType</code>, identifies
* either a key name prefix or a manifest. For example: </p> <ul> <li> <p> A key
* name prefix might look like this: <code>s3://bucketname/exampleprefix</code>
* </p> </li> <li> <p> A manifest might look like this:
* <code>s3://bucketname/example.manifest</code> </p> <p> 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 <code>S3Uri</code>. Note
* that the prefix must be a valid non-empty <code>S3Uri</code> that precludes
* users from specifying a manifest whose individual <code>S3Uri</code> is sourced
* from different S3 buckets.</p> <p> The following code example shows a valid
* manifest format: </p> <p> <code>[ {"prefix":
* "s3://customer_bucket/some/prefix/"},</code> </p> <p> <code>
* "relative/path/to/custdata-1",</code> </p> <p> <code>
* "relative/path/custdata-2",</code> </p> <p> <code> ...</code> </p> <p> <code>
* "relative/path/custdata-N"</code> </p> <p> <code>]</code> </p> <p> This JSON is
* equivalent to the following <code>S3Uri</code> list:</p> <p>
* <code>s3://customer_bucket/some/prefix/relative/path/to/custdata-1</code> </p>
* <p> <code>s3://customer_bucket/some/prefix/relative/path/custdata-2</code> </p>
* <p> <code>...</code> </p> <p>
* <code>s3://customer_bucket/some/prefix/relative/path/custdata-N</code> </p>
* <p>The complete set of <code>S3Uri</code> in this manifest is the input data for
* the channel for this data source. The object that each <code>S3Uri</code> points
* to must be readable by the IAM role that Amazon SageMaker uses to perform tasks
* on your behalf. </p> </li> </ul>
*/
inline S3DataSource& WithS3Uri(const Aws::String& value) { SetS3Uri(value); return *this;}
/**
* <p>Depending on the value specified for the <code>S3DataType</code>, identifies
* either a key name prefix or a manifest. For example: </p> <ul> <li> <p> A key
* name prefix might look like this: <code>s3://bucketname/exampleprefix</code>
* </p> </li> <li> <p> A manifest might look like this:
* <code>s3://bucketname/example.manifest</code> </p> <p> 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 <code>S3Uri</code>. Note
* that the prefix must be a valid non-empty <code>S3Uri</code> that precludes
* users from specifying a manifest whose individual <code>S3Uri</code> is sourced
* from different S3 buckets.</p> <p> The following code example shows a valid
* manifest format: </p> <p> <code>[ {"prefix":
* "s3://customer_bucket/some/prefix/"},</code> </p> <p> <code>
* "relative/path/to/custdata-1",</code> </p> <p> <code>
* "relative/path/custdata-2",</code> </p> <p> <code> ...</code> </p> <p> <code>
* "relative/path/custdata-N"</code> </p> <p> <code>]</code> </p> <p> This JSON is
* equivalent to the following <code>S3Uri</code> list:</p> <p>
* <code>s3://customer_bucket/some/prefix/relative/path/to/custdata-1</code> </p>
* <p> <code>s3://customer_bucket/some/prefix/relative/path/custdata-2</code> </p>
* <p> <code>...</code> </p> <p>
* <code>s3://customer_bucket/some/prefix/relative/path/custdata-N</code> </p>
* <p>The complete set of <code>S3Uri</code> in this manifest is the input data for
* the channel for this data source. The object that each <code>S3Uri</code> points
* to must be readable by the IAM role that Amazon SageMaker uses to perform tasks
* on your behalf. </p> </li> </ul>
*/
inline S3DataSource& WithS3Uri(Aws::String&& value) { SetS3Uri(std::move(value)); return *this;}
/**
* <p>Depending on the value specified for the <code>S3DataType</code>, identifies
* either a key name prefix or a manifest. For example: </p> <ul> <li> <p> A key
* name prefix might look like this: <code>s3://bucketname/exampleprefix</code>
* </p> </li> <li> <p> A manifest might look like this:
* <code>s3://bucketname/example.manifest</code> </p> <p> 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 <code>S3Uri</code>. Note
* that the prefix must be a valid non-empty <code>S3Uri</code> that precludes
* users from specifying a manifest whose individual <code>S3Uri</code> is sourced
* from different S3 buckets.</p> <p> The following code example shows a valid
* manifest format: </p> <p> <code>[ {"prefix":
* "s3://customer_bucket/some/prefix/"},</code> </p> <p> <code>
* "relative/path/to/custdata-1",</code> </p> <p> <code>
* "relative/path/custdata-2",</code> </p> <p> <code> ...</code> </p> <p> <code>
* "relative/path/custdata-N"</code> </p> <p> <code>]</code> </p> <p> This JSON is
* equivalent to the following <code>S3Uri</code> list:</p> <p>
* <code>s3://customer_bucket/some/prefix/relative/path/to/custdata-1</code> </p>
* <p> <code>s3://customer_bucket/some/prefix/relative/path/custdata-2</code> </p>
* <p> <code>...</code> </p> <p>
* <code>s3://customer_bucket/some/prefix/relative/path/custdata-N</code> </p>
* <p>The complete set of <code>S3Uri</code> in this manifest is the input data for
* the channel for this data source. The object that each <code>S3Uri</code> points
* to must be readable by the IAM role that Amazon SageMaker uses to perform tasks
* on your behalf. </p> </li> </ul>
*/
inline S3DataSource& WithS3Uri(const char* value) { SetS3Uri(value); return *this;}
/**
* <p>If you want Amazon SageMaker to replicate the entire dataset on each ML
* compute instance that is launched for model training, specify
* <code>FullyReplicated</code>. </p> <p>If you want Amazon SageMaker to replicate
* a subset of data on each ML compute instance that is launched for model
* training, specify <code>ShardedByS3Key</code>. If there are <i>n</i> ML compute
* instances launched for a training job, each instance gets approximately
* 1/<i>n</i> of the number of S3 objects. In this case, model training on each
* machine uses only the subset of training data. </p> <p>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. </p> <p>In distributed training, where you use multiple
* ML compute EC2 instances, you might choose <code>ShardedByS3Key</code>. If the
* algorithm requires copying training data to the ML storage volume (when
* <code>TrainingInputMode</code> is set to <code>File</code>), this copies
* 1/<i>n</i> of the number of objects. </p>
*/
inline const S3DataDistribution& GetS3DataDistributionType() const{ return m_s3DataDistributionType; }
/**
* <p>If you want Amazon SageMaker to replicate the entire dataset on each ML
* compute instance that is launched for model training, specify
* <code>FullyReplicated</code>. </p> <p>If you want Amazon SageMaker to replicate
* a subset of data on each ML compute instance that is launched for model
* training, specify <code>ShardedByS3Key</code>. If there are <i>n</i> ML compute
* instances launched for a training job, each instance gets approximately
* 1/<i>n</i> of the number of S3 objects. In this case, model training on each
* machine uses only the subset of training data. </p> <p>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. </p> <p>In distributed training, where you use multiple
* ML compute EC2 instances, you might choose <code>ShardedByS3Key</code>. If the
* algorithm requires copying training data to the ML storage volume (when
* <code>TrainingInputMode</code> is set to <code>File</code>), this copies
* 1/<i>n</i> of the number of objects. </p>
*/
inline bool S3DataDistributionTypeHasBeenSet() const { return m_s3DataDistributionTypeHasBeenSet; }
/**
* <p>If you want Amazon SageMaker to replicate the entire dataset on each ML
* compute instance that is launched for model training, specify
* <code>FullyReplicated</code>. </p> <p>If you want Amazon SageMaker to replicate
* a subset of data on each ML compute instance that is launched for model
* training, specify <code>ShardedByS3Key</code>. If there are <i>n</i> ML compute
* instances launched for a training job, each instance gets approximately
* 1/<i>n</i> of the number of S3 objects. In this case, model training on each
* machine uses only the subset of training data. </p> <p>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. </p> <p>In distributed training, where you use multiple
* ML compute EC2 instances, you might choose <code>ShardedByS3Key</code>. If the
* algorithm requires copying training data to the ML storage volume (when
* <code>TrainingInputMode</code> is set to <code>File</code>), this copies
* 1/<i>n</i> of the number of objects. </p>
*/
inline void SetS3DataDistributionType(const S3DataDistribution& value) { m_s3DataDistributionTypeHasBeenSet = true; m_s3DataDistributionType = value; }
/**
* <p>If you want Amazon SageMaker to replicate the entire dataset on each ML
* compute instance that is launched for model training, specify
* <code>FullyReplicated</code>. </p> <p>If you want Amazon SageMaker to replicate
* a subset of data on each ML compute instance that is launched for model
* training, specify <code>ShardedByS3Key</code>. If there are <i>n</i> ML compute
* instances launched for a training job, each instance gets approximately
* 1/<i>n</i> of the number of S3 objects. In this case, model training on each
* machine uses only the subset of training data. </p> <p>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. </p> <p>In distributed training, where you use multiple
* ML compute EC2 instances, you might choose <code>ShardedByS3Key</code>. If the
* algorithm requires copying training data to the ML storage volume (when
* <code>TrainingInputMode</code> is set to <code>File</code>), this copies
* 1/<i>n</i> of the number of objects. </p>
*/
inline void SetS3DataDistributionType(S3DataDistribution&& value) { m_s3DataDistributionTypeHasBeenSet = true; m_s3DataDistributionType = std::move(value); }
/**
* <p>If you want Amazon SageMaker to replicate the entire dataset on each ML
* compute instance that is launched for model training, specify
* <code>FullyReplicated</code>. </p> <p>If you want Amazon SageMaker to replicate
* a subset of data on each ML compute instance that is launched for model
* training, specify <code>ShardedByS3Key</code>. If there are <i>n</i> ML compute
* instances launched for a training job, each instance gets approximately
* 1/<i>n</i> of the number of S3 objects. In this case, model training on each
* machine uses only the subset of training data. </p> <p>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. </p> <p>In distributed training, where you use multiple
* ML compute EC2 instances, you might choose <code>ShardedByS3Key</code>. If the
* algorithm requires copying training data to the ML storage volume (when
* <code>TrainingInputMode</code> is set to <code>File</code>), this copies
* 1/<i>n</i> of the number of objects. </p>
*/
inline S3DataSource& WithS3DataDistributionType(const S3DataDistribution& value) { SetS3DataDistributionType(value); return *this;}
/**
* <p>If you want Amazon SageMaker to replicate the entire dataset on each ML
* compute instance that is launched for model training, specify
* <code>FullyReplicated</code>. </p> <p>If you want Amazon SageMaker to replicate
* a subset of data on each ML compute instance that is launched for model
* training, specify <code>ShardedByS3Key</code>. If there are <i>n</i> ML compute
* instances launched for a training job, each instance gets approximately
* 1/<i>n</i> of the number of S3 objects. In this case, model training on each
* machine uses only the subset of training data. </p> <p>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. </p> <p>In distributed training, where you use multiple
* ML compute EC2 instances, you might choose <code>ShardedByS3Key</code>. If the
* algorithm requires copying training data to the ML storage volume (when
* <code>TrainingInputMode</code> is set to <code>File</code>), this copies
* 1/<i>n</i> of the number of objects. </p>
*/
inline S3DataSource& WithS3DataDistributionType(S3DataDistribution&& value) { SetS3DataDistributionType(std::move(value)); return *this;}
/**
* <p>A list of one or more attribute names to use that are found in a specified
* augmented manifest file.</p>
*/
inline const Aws::Vector<Aws::String>& GetAttributeNames() const{ return m_attributeNames; }
/**
* <p>A list of one or more attribute names to use that are found in a specified
* augmented manifest file.</p>
*/
inline bool AttributeNamesHasBeenSet() const { return m_attributeNamesHasBeenSet; }
/**
* <p>A list of one or more attribute names to use that are found in a specified
* augmented manifest file.</p>
*/
inline void SetAttributeNames(const Aws::Vector<Aws::String>& value) { m_attributeNamesHasBeenSet = true; m_attributeNames = value; }
/**
* <p>A list of one or more attribute names to use that are found in a specified
* augmented manifest file.</p>
*/
inline void SetAttributeNames(Aws::Vector<Aws::String>&& value) { m_attributeNamesHasBeenSet = true; m_attributeNames = std::move(value); }
/**
* <p>A list of one or more attribute names to use that are found in a specified
* augmented manifest file.</p>
*/
inline S3DataSource& WithAttributeNames(const Aws::Vector<Aws::String>& value) { SetAttributeNames(value); return *this;}
/**
* <p>A list of one or more attribute names to use that are found in a specified
* augmented manifest file.</p>
*/
inline S3DataSource& WithAttributeNames(Aws::Vector<Aws::String>&& value) { SetAttributeNames(std::move(value)); return *this;}
/**
* <p>A list of one or more attribute names to use that are found in a specified
* augmented manifest file.</p>
*/
inline S3DataSource& AddAttributeNames(const Aws::String& value) { m_attributeNamesHasBeenSet = true; m_attributeNames.push_back(value); return *this; }
/**
* <p>A list of one or more attribute names to use that are found in a specified
* augmented manifest file.</p>
*/
inline S3DataSource& AddAttributeNames(Aws::String&& value) { m_attributeNamesHasBeenSet = true; m_attributeNames.push_back(std::move(value)); return *this; }
/**
* <p>A list of one or more attribute names to use that are found in a specified
* augmented manifest file.</p>
*/
inline S3DataSource& AddAttributeNames(const char* value) { m_attributeNamesHasBeenSet = true; m_attributeNames.push_back(value); return *this; }
private:
S3DataType m_s3DataType;
bool m_s3DataTypeHasBeenSet;
Aws::String m_s3Uri;
bool m_s3UriHasBeenSet;
S3DataDistribution m_s3DataDistributionType;
bool m_s3DataDistributionTypeHasBeenSet;
Aws::Vector<Aws::String> m_attributeNames;
bool m_attributeNamesHasBeenSet;
};
} // namespace Model
} // namespace SageMaker
} // namespace Aws