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/*
* Copyright 2019-2024 Amazon.com, Inc. or its affiliates. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance with
* the License. A copy of the License is located at
*
* http://aws.amazon.com/apache2.0
*
* or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
* CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions
* and limitations under the License.
*/
package com.amazonaws.services.sagemaker.model;
import java.io.Serializable;
import javax.annotation.Generated;
import com.amazonaws.protocol.StructuredPojo;
import com.amazonaws.protocol.ProtocolMarshaller;
/**
*
* A channel is a named input source that training algorithms can consume.
*
*
* @see AWS API
* Documentation
*/
@Generated("com.amazonaws:aws-java-sdk-code-generator")
public class Channel implements Serializable, Cloneable, StructuredPojo {
/**
*
* The name of the channel.
*
*/
private String channelName;
/**
*
* The location of the channel data.
*
*/
private DataSource dataSource;
/**
*
* The MIME type of the data.
*
*/
private String contentType;
/**
*
* If training data is compressed, the compression type. The default value is None.
* CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to
* None.
*
*/
private String compressionType;
/**
*
*
* Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO
* format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is
* already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using
* RecordIO.
*
*
* In File mode, leave this field unset or set it to None.
*
*/
private String recordWrapperType;
/**
*
* (Optional) The input mode to use for the data channel in a training job. If you don't set a value for
* InputMode, SageMaker uses the value set for TrainingInputMode. Use this parameter to
* override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training
* job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML
* storage volume, and mount the directory to a Docker volume, use File input mode. To stream data
* directly from Amazon S3 to the container, choose Pipe input mode.
*
*
* To use a model for incremental training, choose File input model.
*
*/
private String inputMode;
/**
*
* A configuration for a shuffle option for input data in a channel. If you use S3Prefix for
* S3DataType, this shuffles the results of the S3 key prefix matches. If you use
* ManifestFile, the order of the S3 object references in the ManifestFile is shuffled. If
* you use AugmentedManifestFile, the order of the JSON lines in the AugmentedManifestFile
* is shuffled. The shuffling order is determined using the Seed value.
*
*
* For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the
* order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a
* multi-node training job when ShuffleConfig is combined with S3DataDistributionType of
* ShardedByS3Key, the data is shuffled across nodes so that the content sent to a particular node on
* the first epoch might be sent to a different node on the second epoch.
*
*/
private ShuffleConfig shuffleConfig;
/**
*
* The name of the channel.
*
*
* @param channelName
* The name of the channel.
*/
public void setChannelName(String channelName) {
this.channelName = channelName;
}
/**
*
* The name of the channel.
*
*
* @return The name of the channel.
*/
public String getChannelName() {
return this.channelName;
}
/**
*
* The name of the channel.
*
*
* @param channelName
* The name of the channel.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public Channel withChannelName(String channelName) {
setChannelName(channelName);
return this;
}
/**
*
* The location of the channel data.
*
*
* @param dataSource
* The location of the channel data.
*/
public void setDataSource(DataSource dataSource) {
this.dataSource = dataSource;
}
/**
*
* The location of the channel data.
*
*
* @return The location of the channel data.
*/
public DataSource getDataSource() {
return this.dataSource;
}
/**
*
* The location of the channel data.
*
*
* @param dataSource
* The location of the channel data.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public Channel withDataSource(DataSource dataSource) {
setDataSource(dataSource);
return this;
}
/**
*
* The MIME type of the data.
*
*
* @param contentType
* The MIME type of the data.
*/
public void setContentType(String contentType) {
this.contentType = contentType;
}
/**
*
* The MIME type of the data.
*
*
* @return The MIME type of the data.
*/
public String getContentType() {
return this.contentType;
}
/**
*
* The MIME type of the data.
*
*
* @param contentType
* The MIME type of the data.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public Channel withContentType(String contentType) {
setContentType(contentType);
return this;
}
/**
*
* If training data is compressed, the compression type. The default value is None.
* CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to
* None.
*
*
* @param compressionType
* If training data is compressed, the compression type. The default value is None.
* CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set
* it to None.
* @see CompressionType
*/
public void setCompressionType(String compressionType) {
this.compressionType = compressionType;
}
/**
*
* If training data is compressed, the compression type. The default value is None.
* CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to
* None.
*
*
* @return If training data is compressed, the compression type. The default value is None.
* CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set
* it to None.
* @see CompressionType
*/
public String getCompressionType() {
return this.compressionType;
}
/**
*
* If training data is compressed, the compression type. The default value is None.
* CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to
* None.
*
*
* @param compressionType
* If training data is compressed, the compression type. The default value is None.
* CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set
* it to None.
* @return Returns a reference to this object so that method calls can be chained together.
* @see CompressionType
*/
public Channel withCompressionType(String compressionType) {
setCompressionType(compressionType);
return this;
}
/**
*
* If training data is compressed, the compression type. The default value is None.
* CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to
* None.
*
*
* @param compressionType
* If training data is compressed, the compression type. The default value is None.
* CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set
* it to None.
* @return Returns a reference to this object so that method calls can be chained together.
* @see CompressionType
*/
public Channel withCompressionType(CompressionType compressionType) {
this.compressionType = compressionType.toString();
return this;
}
/**
*
*
* Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO
* format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is
* already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using
* RecordIO.
*
*
* In File mode, leave this field unset or set it to None.
*
*
* @param recordWrapperType
*
* Specify RecordIO as the value when input data is in raw format but the training algorithm requires the
* RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the
* input data is already in RecordIO format, you don't need to set this attribute. For more information, see
* Create a Dataset Using
* RecordIO.
*
*
* In File mode, leave this field unset or set it to None.
* @see RecordWrapper
*/
public void setRecordWrapperType(String recordWrapperType) {
this.recordWrapperType = recordWrapperType;
}
/**
*
*
* Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO
* format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is
* already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using
* RecordIO.
*
*
* In File mode, leave this field unset or set it to None.
*
*
* @return
* Specify RecordIO as the value when input data is in raw format but the training algorithm requires the
* RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the
* input data is already in RecordIO format, you don't need to set this attribute. For more information, see
* Create a Dataset Using
* RecordIO.
*
*
* In File mode, leave this field unset or set it to None.
* @see RecordWrapper
*/
public String getRecordWrapperType() {
return this.recordWrapperType;
}
/**
*
*
* Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO
* format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is
* already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using
* RecordIO.
*
*
* In File mode, leave this field unset or set it to None.
*
*
* @param recordWrapperType
*
* Specify RecordIO as the value when input data is in raw format but the training algorithm requires the
* RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the
* input data is already in RecordIO format, you don't need to set this attribute. For more information, see
* Create a Dataset Using
* RecordIO.
*
*
* In File mode, leave this field unset or set it to None.
* @return Returns a reference to this object so that method calls can be chained together.
* @see RecordWrapper
*/
public Channel withRecordWrapperType(String recordWrapperType) {
setRecordWrapperType(recordWrapperType);
return this;
}
/**
*
*
* Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO
* format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is
* already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using
* RecordIO.
*
*
* In File mode, leave this field unset or set it to None.
*
*
* @param recordWrapperType
*
* Specify RecordIO as the value when input data is in raw format but the training algorithm requires the
* RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the
* input data is already in RecordIO format, you don't need to set this attribute. For more information, see
* Create a Dataset Using
* RecordIO.
*
*
* In File mode, leave this field unset or set it to None.
* @return Returns a reference to this object so that method calls can be chained together.
* @see RecordWrapper
*/
public Channel withRecordWrapperType(RecordWrapper recordWrapperType) {
this.recordWrapperType = recordWrapperType.toString();
return this;
}
/**
*
* (Optional) The input mode to use for the data channel in a training job. If you don't set a value for
* InputMode, SageMaker uses the value set for TrainingInputMode. Use this parameter to
* override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training
* job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML
* storage volume, and mount the directory to a Docker volume, use File input mode. To stream data
* directly from Amazon S3 to the container, choose Pipe input mode.
*
*
* To use a model for incremental training, choose File input model.
*
*
* @param inputMode
* (Optional) The input mode to use for the data channel in a training job. If you don't set a value for
* InputMode, SageMaker uses the value set for TrainingInputMode. Use this
* parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the
* training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the
* provisioned ML storage volume, and mount the directory to a Docker volume, use File input
* mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
*
* To use a model for incremental training, choose File input model.
* @see TrainingInputMode
*/
public void setInputMode(String inputMode) {
this.inputMode = inputMode;
}
/**
*
* (Optional) The input mode to use for the data channel in a training job. If you don't set a value for
* InputMode, SageMaker uses the value set for TrainingInputMode. Use this parameter to
* override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training
* job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML
* storage volume, and mount the directory to a Docker volume, use File input mode. To stream data
* directly from Amazon S3 to the container, choose Pipe input mode.
*
*
* To use a model for incremental training, choose File input model.
*
*
* @return (Optional) The input mode to use for the data channel in a training job. If you don't set a value for
* InputMode, SageMaker uses the value set for TrainingInputMode. Use this
* parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the
* training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to
* the provisioned ML storage volume, and mount the directory to a Docker volume, use File
* input mode. To stream data directly from Amazon S3 to the container, choose Pipe input
* mode.
*
* To use a model for incremental training, choose File input model.
* @see TrainingInputMode
*/
public String getInputMode() {
return this.inputMode;
}
/**
*
* (Optional) The input mode to use for the data channel in a training job. If you don't set a value for
* InputMode, SageMaker uses the value set for TrainingInputMode. Use this parameter to
* override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training
* job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML
* storage volume, and mount the directory to a Docker volume, use File input mode. To stream data
* directly from Amazon S3 to the container, choose Pipe input mode.
*
*
* To use a model for incremental training, choose File input model.
*
*
* @param inputMode
* (Optional) The input mode to use for the data channel in a training job. If you don't set a value for
* InputMode, SageMaker uses the value set for TrainingInputMode. Use this
* parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the
* training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the
* provisioned ML storage volume, and mount the directory to a Docker volume, use File input
* mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
*
* To use a model for incremental training, choose File input model.
* @return Returns a reference to this object so that method calls can be chained together.
* @see TrainingInputMode
*/
public Channel withInputMode(String inputMode) {
setInputMode(inputMode);
return this;
}
/**
*
* (Optional) The input mode to use for the data channel in a training job. If you don't set a value for
* InputMode, SageMaker uses the value set for TrainingInputMode. Use this parameter to
* override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training
* job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML
* storage volume, and mount the directory to a Docker volume, use File input mode. To stream data
* directly from Amazon S3 to the container, choose Pipe input mode.
*
*
* To use a model for incremental training, choose File input model.
*
*
* @param inputMode
* (Optional) The input mode to use for the data channel in a training job. If you don't set a value for
* InputMode, SageMaker uses the value set for TrainingInputMode. Use this
* parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the
* training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the
* provisioned ML storage volume, and mount the directory to a Docker volume, use File input
* mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
*
* To use a model for incremental training, choose File input model.
* @return Returns a reference to this object so that method calls can be chained together.
* @see TrainingInputMode
*/
public Channel withInputMode(TrainingInputMode inputMode) {
this.inputMode = inputMode.toString();
return this;
}
/**
*
* A configuration for a shuffle option for input data in a channel. If you use S3Prefix for
* S3DataType, this shuffles the results of the S3 key prefix matches. If you use
* ManifestFile, the order of the S3 object references in the ManifestFile is shuffled. If
* you use AugmentedManifestFile, the order of the JSON lines in the AugmentedManifestFile
* is shuffled. The shuffling order is determined using the Seed value.
*
*
* For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the
* order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a
* multi-node training job when ShuffleConfig is combined with S3DataDistributionType of
* ShardedByS3Key, the data is shuffled across nodes so that the content sent to a particular node on
* the first epoch might be sent to a different node on the second epoch.
*
*
* @param shuffleConfig
* A configuration for a shuffle option for input data in a channel. If you use S3Prefix for
* S3DataType, this shuffles the results of the S3 key prefix matches. If you use
* ManifestFile, the order of the S3 object references in the ManifestFile is
* shuffled. If you use AugmentedManifestFile, the order of the JSON lines in the
* AugmentedManifestFile is shuffled. The shuffling order is determined using the
* Seed value.
*
* For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that
* the order of the training data is different for each epoch, it helps reduce bias and possible overfitting.
* In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of
* ShardedByS3Key, the data is shuffled across nodes so that the content sent to a particular
* node on the first epoch might be sent to a different node on the second epoch.
*/
public void setShuffleConfig(ShuffleConfig shuffleConfig) {
this.shuffleConfig = shuffleConfig;
}
/**
*
* A configuration for a shuffle option for input data in a channel. If you use S3Prefix for
* S3DataType, this shuffles the results of the S3 key prefix matches. If you use
* ManifestFile, the order of the S3 object references in the ManifestFile is shuffled. If
* you use AugmentedManifestFile, the order of the JSON lines in the AugmentedManifestFile
* is shuffled. The shuffling order is determined using the Seed value.
*
*
* For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the
* order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a
* multi-node training job when ShuffleConfig is combined with S3DataDistributionType of
* ShardedByS3Key, the data is shuffled across nodes so that the content sent to a particular node on
* the first epoch might be sent to a different node on the second epoch.
*
*
* @return A configuration for a shuffle option for input data in a channel. If you use S3Prefix for
* S3DataType, this shuffles the results of the S3 key prefix matches. If you use
* ManifestFile, the order of the S3 object references in the ManifestFile is
* shuffled. If you use AugmentedManifestFile, the order of the JSON lines in the
* AugmentedManifestFile is shuffled. The shuffling order is determined using the
* Seed value.
*
* For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that
* the order of the training data is different for each epoch, it helps reduce bias and possible
* overfitting. In a multi-node training job when ShuffleConfig is combined with
* S3DataDistributionType of ShardedByS3Key, the data is shuffled across nodes so
* that the content sent to a particular node on the first epoch might be sent to a different node on the
* second epoch.
*/
public ShuffleConfig getShuffleConfig() {
return this.shuffleConfig;
}
/**
*
* A configuration for a shuffle option for input data in a channel. If you use S3Prefix for
* S3DataType, this shuffles the results of the S3 key prefix matches. If you use
* ManifestFile, the order of the S3 object references in the ManifestFile is shuffled. If
* you use AugmentedManifestFile, the order of the JSON lines in the AugmentedManifestFile
* is shuffled. The shuffling order is determined using the Seed value.
*
*
* For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the
* order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a
* multi-node training job when ShuffleConfig is combined with S3DataDistributionType of
* ShardedByS3Key, the data is shuffled across nodes so that the content sent to a particular node on
* the first epoch might be sent to a different node on the second epoch.
*
*
* @param shuffleConfig
* A configuration for a shuffle option for input data in a channel. If you use S3Prefix for
* S3DataType, this shuffles the results of the S3 key prefix matches. If you use
* ManifestFile, the order of the S3 object references in the ManifestFile is
* shuffled. If you use AugmentedManifestFile, the order of the JSON lines in the
* AugmentedManifestFile is shuffled. The shuffling order is determined using the
* Seed value.
*
* For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that
* the order of the training data is different for each epoch, it helps reduce bias and possible overfitting.
* In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of
* ShardedByS3Key, the data is shuffled across nodes so that the content sent to a particular
* node on the first epoch might be sent to a different node on the second epoch.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public Channel withShuffleConfig(ShuffleConfig shuffleConfig) {
setShuffleConfig(shuffleConfig);
return this;
}
/**
* Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be
* redacted from this string using a placeholder value.
*
* @return A string representation of this object.
*
* @see java.lang.Object#toString()
*/
@Override
public String toString() {
StringBuilder sb = new StringBuilder();
sb.append("{");
if (getChannelName() != null)
sb.append("ChannelName: ").append(getChannelName()).append(",");
if (getDataSource() != null)
sb.append("DataSource: ").append(getDataSource()).append(",");
if (getContentType() != null)
sb.append("ContentType: ").append(getContentType()).append(",");
if (getCompressionType() != null)
sb.append("CompressionType: ").append(getCompressionType()).append(",");
if (getRecordWrapperType() != null)
sb.append("RecordWrapperType: ").append(getRecordWrapperType()).append(",");
if (getInputMode() != null)
sb.append("InputMode: ").append(getInputMode()).append(",");
if (getShuffleConfig() != null)
sb.append("ShuffleConfig: ").append(getShuffleConfig());
sb.append("}");
return sb.toString();
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (obj instanceof Channel == false)
return false;
Channel other = (Channel) obj;
if (other.getChannelName() == null ^ this.getChannelName() == null)
return false;
if (other.getChannelName() != null && other.getChannelName().equals(this.getChannelName()) == false)
return false;
if (other.getDataSource() == null ^ this.getDataSource() == null)
return false;
if (other.getDataSource() != null && other.getDataSource().equals(this.getDataSource()) == false)
return false;
if (other.getContentType() == null ^ this.getContentType() == null)
return false;
if (other.getContentType() != null && other.getContentType().equals(this.getContentType()) == false)
return false;
if (other.getCompressionType() == null ^ this.getCompressionType() == null)
return false;
if (other.getCompressionType() != null && other.getCompressionType().equals(this.getCompressionType()) == false)
return false;
if (other.getRecordWrapperType() == null ^ this.getRecordWrapperType() == null)
return false;
if (other.getRecordWrapperType() != null && other.getRecordWrapperType().equals(this.getRecordWrapperType()) == false)
return false;
if (other.getInputMode() == null ^ this.getInputMode() == null)
return false;
if (other.getInputMode() != null && other.getInputMode().equals(this.getInputMode()) == false)
return false;
if (other.getShuffleConfig() == null ^ this.getShuffleConfig() == null)
return false;
if (other.getShuffleConfig() != null && other.getShuffleConfig().equals(this.getShuffleConfig()) == false)
return false;
return true;
}
@Override
public int hashCode() {
final int prime = 31;
int hashCode = 1;
hashCode = prime * hashCode + ((getChannelName() == null) ? 0 : getChannelName().hashCode());
hashCode = prime * hashCode + ((getDataSource() == null) ? 0 : getDataSource().hashCode());
hashCode = prime * hashCode + ((getContentType() == null) ? 0 : getContentType().hashCode());
hashCode = prime * hashCode + ((getCompressionType() == null) ? 0 : getCompressionType().hashCode());
hashCode = prime * hashCode + ((getRecordWrapperType() == null) ? 0 : getRecordWrapperType().hashCode());
hashCode = prime * hashCode + ((getInputMode() == null) ? 0 : getInputMode().hashCode());
hashCode = prime * hashCode + ((getShuffleConfig() == null) ? 0 : getShuffleConfig().hashCode());
return hashCode;
}
@Override
public Channel clone() {
try {
return (Channel) super.clone();
} catch (CloneNotSupportedException e) {
throw new IllegalStateException("Got a CloneNotSupportedException from Object.clone() " + "even though we're Cloneable!", e);
}
}
@com.amazonaws.annotation.SdkInternalApi
@Override
public void marshall(ProtocolMarshaller protocolMarshaller) {
com.amazonaws.services.sagemaker.model.transform.ChannelMarshaller.getInstance().marshall(this, protocolMarshaller);
}
}