ModelPackageContainerDefinition (AWS SDK for Java

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AWS SDK for Java 1.x API Reference - 1.12.795

We announced the upcoming end-of-support for AWS SDK for Java (v1). We recommend that you migrate to AWS SDK for Java v2. For dates, additional details, and information on how to migrate, please refer to the linked announcement.

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com.amazonaws.services.sagemaker.model

Class ModelPackageContainerDefinition

java.lang.Object
- com.amazonaws.services.sagemaker.model.ModelPackageContainerDefinition

All Implemented Interfaces:: StructuredPojo, Serializable, Cloneable

@Generated(value="com.amazonaws:aws-java-sdk-code-generator")
public class ModelPackageContainerDefinition
extends Object
implements Serializable, Cloneable, StructuredPojo

Describes the Docker container for the model package.

See Also:: AWS API Documentation, Serialized Form

Constructor Summary

Constructors
Constructor and Description

ModelPackageContainerDefinition ()

Constructors
Constructor and Description
`ModelPackageContainerDefinition ()`

Method Summary

All Methods
Modifier and Type	Method and Description
`ModelPackageContainerDefinition`	`addEnvironmentEntry (String key, String value)` Add a single Environment entry
`ModelPackageContainerDefinition`	`clearEnvironmentEntries ()` Removes all the entries added into Environment.
`ModelPackageContainerDefinition`	`clone ()`
`boolean`	`equals (Object obj)`
`AdditionalS3DataSource`	`getAdditionalS3DataSource ()` The additional data source that is used during inference in the Docker container for your model package.
`String`	`getContainerHostname ()` The DNS host name for the Docker container.
`Map<String,String>`	`getEnvironment ()` The environment variables to set in the Docker container.
`String`	`getFramework ()` The machine learning framework of the model package container image.
`String`	`getFrameworkVersion ()` The framework version of the Model Package Container Image.
`String`	`getImage ()` The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
`String`	`getImageDigest ()` An MD5 hash of the training algorithm that identifies the Docker image used for training.
`ModelDataSource`	`getModelDataSource ()` Specifies the location of ML model data to deploy during endpoint creation.
`String`	`getModelDataUrl ()` The Amazon S3 path where the model artifacts, which result from model training, are stored.
`ModelInput`	`getModelInput ()` A structure with Model Input details.
`String`	`getNearestModelName ()` The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model.
`String`	`getProductId ()` The Amazon Web Services Marketplace product ID of the model package.
`int`	`hashCode ()`
`void`	`marshall (ProtocolMarshaller protocolMarshaller)` Marshalls this structured data using the given `ProtocolMarshaller`.
`void`	`setAdditionalS3DataSource (AdditionalS3DataSource additionalS3DataSource)` The additional data source that is used during inference in the Docker container for your model package.
`void`	`setContainerHostname (String containerHostname)` The DNS host name for the Docker container.
`void`	`setEnvironment (Map<String,String> environment)` The environment variables to set in the Docker container.
`void`	`setFramework (String framework)` The machine learning framework of the model package container image.
`void`	`setFrameworkVersion (String frameworkVersion)` The framework version of the Model Package Container Image.
`void`	`setImage (String image)` The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
`void`	`setImageDigest (String imageDigest)` An MD5 hash of the training algorithm that identifies the Docker image used for training.
`void`	`setModelDataSource (ModelDataSource modelDataSource)` Specifies the location of ML model data to deploy during endpoint creation.
`void`	`setModelDataUrl (String modelDataUrl)` The Amazon S3 path where the model artifacts, which result from model training, are stored.
`void`	`setModelInput (ModelInput modelInput)` A structure with Model Input details.
`void`	`setNearestModelName (String nearestModelName)` The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model.
`void`	`setProductId (String productId)` The Amazon Web Services Marketplace product ID of the model package.
`String`	`toString ()` Returns a string representation of this object.
`ModelPackageContainerDefinition`	`withAdditionalS3DataSource (AdditionalS3DataSource additionalS3DataSource)` The additional data source that is used during inference in the Docker container for your model package.
`ModelPackageContainerDefinition`	`withContainerHostname (String containerHostname)` The DNS host name for the Docker container.
`ModelPackageContainerDefinition`	`withEnvironment (Map<String,String> environment)` The environment variables to set in the Docker container.
`ModelPackageContainerDefinition`	`withFramework (String framework)` The machine learning framework of the model package container image.
`ModelPackageContainerDefinition`	`withFrameworkVersion (String frameworkVersion)` The framework version of the Model Package Container Image.
`ModelPackageContainerDefinition`	`withImage (String image)` The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
`ModelPackageContainerDefinition`	`withImageDigest (String imageDigest)` An MD5 hash of the training algorithm that identifies the Docker image used for training.
`ModelPackageContainerDefinition`	`withModelDataSource (ModelDataSource modelDataSource)` Specifies the location of ML model data to deploy during endpoint creation.
`ModelPackageContainerDefinition`	`withModelDataUrl (String modelDataUrl)` The Amazon S3 path where the model artifacts, which result from model training, are stored.
`ModelPackageContainerDefinition`	`withModelInput (ModelInput modelInput)` A structure with Model Input details.
`ModelPackageContainerDefinition`	`withNearestModelName (String nearestModelName)` The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model.
`ModelPackageContainerDefinition`	`withProductId (String productId)` The Amazon Web Services Marketplace product ID of the model package.

Methods inherited from class java.lang.Object
getClass, notify, notifyAll, wait, wait, wait

- Constructor Detail
  - ModelPackageContainerDefinition
```
public ModelPackageContainerDefinition()
```
- Method Detail
  - setContainerHostname
```
public void setContainerHostname(String containerHostname)
```
    The DNS host name for the Docker container.
    
    Parameters:
    
    containerHostname - The DNS host name for the Docker container.
  - getContainerHostname
```
public String getContainerHostname()
```
    The DNS host name for the Docker container.
    
    Returns:
    
    The DNS host name for the Docker container.
  - withContainerHostname
```
public ModelPackageContainerDefinition withContainerHostname(String containerHostname)
```
    The DNS host name for the Docker container.
    
    Parameters:
    
    containerHostname - The DNS host name for the Docker container.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setImage
```
public void setImage(String image)
```
    The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
    
    If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.
    
    Parameters:
    
    image - The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
    
    If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.
  - getImage
```
public String getImage()
```
    The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
    
    If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.
    
    Returns:
    
    The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
    
    If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.
  - withImage
```
public ModelPackageContainerDefinition withImage(String image)
```
    The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
    
    If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.
    
    Parameters:
    
    image - The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
    
    If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setImageDigest
```
public void setImageDigest(String imageDigest)
```
    An MD5 hash of the training algorithm that identifies the Docker image used for training.
    
    Parameters:
    
    imageDigest - An MD5 hash of the training algorithm that identifies the Docker image used for training.
  - getImageDigest
```
public String getImageDigest()
```
    An MD5 hash of the training algorithm that identifies the Docker image used for training.
    
    Returns:
    
    An MD5 hash of the training algorithm that identifies the Docker image used for training.
  - withImageDigest
```
public ModelPackageContainerDefinition withImageDigest(String imageDigest)
```
    An MD5 hash of the training algorithm that identifies the Docker image used for training.
    
    Parameters:
    
    imageDigest - An MD5 hash of the training algorithm that identifies the Docker image used for training.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setModelDataUrl
```
public void setModelDataUrl(String modelDataUrl)
```
    The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
    
    The model artifacts must be in an S3 bucket that is in the same region as the model package.
    
    Parameters:
    
    modelDataUrl - The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
    
    The model artifacts must be in an S3 bucket that is in the same region as the model package.
  - getModelDataUrl
```
public String getModelDataUrl()
```
    The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
    
    The model artifacts must be in an S3 bucket that is in the same region as the model package.
    
    Returns:
    
    The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
    
    The model artifacts must be in an S3 bucket that is in the same region as the model package.
  - withModelDataUrl
```
public ModelPackageContainerDefinition withModelDataUrl(String modelDataUrl)
```
    The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
    
    The model artifacts must be in an S3 bucket that is in the same region as the model package.
    
    Parameters:
    
    modelDataUrl - The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
    
    The model artifacts must be in an S3 bucket that is in the same region as the model package.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setModelDataSource
```
public void setModelDataSource(ModelDataSource modelDataSource)
```
    Specifies the location of ML model data to deploy during endpoint creation.
    
    Parameters:
    
    modelDataSource - Specifies the location of ML model data to deploy during endpoint creation.
  - getModelDataSource
```
public ModelDataSource getModelDataSource()
```
    Specifies the location of ML model data to deploy during endpoint creation.
    
    Returns:
    
    Specifies the location of ML model data to deploy during endpoint creation.
  - withModelDataSource
```
public ModelPackageContainerDefinition withModelDataSource(ModelDataSource modelDataSource)
```
    Specifies the location of ML model data to deploy during endpoint creation.
    
    Parameters:
    
    modelDataSource - Specifies the location of ML model data to deploy during endpoint creation.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setProductId
```
public void setProductId(String productId)
```
    The Amazon Web Services Marketplace product ID of the model package.
    
    Parameters:
    
    productId - The Amazon Web Services Marketplace product ID of the model package.
  - getProductId
```
public String getProductId()
```
    The Amazon Web Services Marketplace product ID of the model package.
    
    Returns:
    
    The Amazon Web Services Marketplace product ID of the model package.
  - withProductId
```
public ModelPackageContainerDefinition withProductId(String productId)
```
    The Amazon Web Services Marketplace product ID of the model package.
    
    Parameters:
    
    productId - The Amazon Web Services Marketplace product ID of the model package.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - getEnvironment
```
public Map<String,String> getEnvironment()
```
    The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
    
    Returns:
    
    The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
  - setEnvironment
```
public void setEnvironment(Map<String,String> environment)
```
    The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
    
    Parameters:
    
    environment - The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
  - withEnvironment
```
public ModelPackageContainerDefinition withEnvironment(Map<String,String> environment)
```
    The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
    
    Parameters:
    
    environment - The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - addEnvironmentEntry
```
public ModelPackageContainerDefinition addEnvironmentEntry(String key,
 String value)
```
    Add a single Environment entry
    
    See Also:
    
    withEnvironment(java.util.Map<java.lang.String, java.lang.String>)
  - clearEnvironmentEntries
```
public ModelPackageContainerDefinition clearEnvironmentEntries()
```
    Removes all the entries added into Environment.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setModelInput
```
public void setModelInput(ModelInput modelInput)
```
    A structure with Model Input details.
    
    Parameters:
    
    modelInput - A structure with Model Input details.
  - getModelInput
```
public ModelInput getModelInput()
```
    A structure with Model Input details.
    
    Returns:
    
    A structure with Model Input details.
  - withModelInput
```
public ModelPackageContainerDefinition withModelInput(ModelInput modelInput)
```
    A structure with Model Input details.
    
    Parameters:
    
    modelInput - A structure with Model Input details.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setFramework
```
public void setFramework(String framework)
```
    The machine learning framework of the model package container image.
    
    Parameters:
    
    framework - The machine learning framework of the model package container image.
  - getFramework
```
public String getFramework()
```
    The machine learning framework of the model package container image.
    
    Returns:
    
    The machine learning framework of the model package container image.
  - withFramework
```
public ModelPackageContainerDefinition withFramework(String framework)
```
    The machine learning framework of the model package container image.
    
    Parameters:
    
    framework - The machine learning framework of the model package container image.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setFrameworkVersion
```
public void setFrameworkVersion(String frameworkVersion)
```
    The framework version of the Model Package Container Image.
    
    Parameters:
    
    frameworkVersion - The framework version of the Model Package Container Image.
  - getFrameworkVersion
```
public String getFrameworkVersion()
```
    The framework version of the Model Package Container Image.
    
    Returns:
    
    The framework version of the Model Package Container Image.
  - withFrameworkVersion
```
public ModelPackageContainerDefinition withFrameworkVersion(String frameworkVersion)
```
    The framework version of the Model Package Container Image.
    
    Parameters:
    
    frameworkVersion - The framework version of the Model Package Container Image.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setNearestModelName
```
public void setNearestModelName(String nearestModelName)
```
    The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata.
    
    Parameters:
    
    nearestModelName - The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata.
  - getNearestModelName
```
public String getNearestModelName()
```
    The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata.
    
    Returns:
    
    The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata.
  - withNearestModelName
```
public ModelPackageContainerDefinition withNearestModelName(String nearestModelName)
```
    The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata.
    
    Parameters:
    
    nearestModelName - The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setAdditionalS3DataSource
```
public void setAdditionalS3DataSource(AdditionalS3DataSource additionalS3DataSource)
```
    The additional data source that is used during inference in the Docker container for your model package.
    
    Parameters:
    
    additionalS3DataSource - The additional data source that is used during inference in the Docker container for your model package.
  - getAdditionalS3DataSource
```
public AdditionalS3DataSource getAdditionalS3DataSource()
```
    The additional data source that is used during inference in the Docker container for your model package.
    
    Returns:
    
    The additional data source that is used during inference in the Docker container for your model package.
  - withAdditionalS3DataSource
```
public ModelPackageContainerDefinition withAdditionalS3DataSource(AdditionalS3DataSource additionalS3DataSource)
```
    The additional data source that is used during inference in the Docker container for your model package.
    
    Parameters:
    
    additionalS3DataSource - The additional data source that is used during inference in the Docker container for your model package.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - toString
```
public String toString()
```
    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.
    
    Overrides:
    
    toString in class Object
    
    Returns:
    
    A string representation of this object.
    
    See Also:
    
    Object.toString()
  - equals
```
public boolean equals(Object obj)
```
    Overrides:
    
    equals in class Object
  - hashCode
```
public int hashCode()
```
    Overrides:
    
    hashCode in class Object
  - clone
```
public ModelPackageContainerDefinition clone()
```
    Overrides:
    
    clone in class Object
  - 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.

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Constr |
Method

AWS SDK for Java 1.x API Reference - 1.12.795

Class ModelPackageContainerDefinition

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

ModelPackageContainerDefinition

Method Detail

setContainerHostname

getContainerHostname

withContainerHostname

setImage

getImage

withImage

setImageDigest

getImageDigest

withImageDigest

setModelDataUrl

getModelDataUrl

withModelDataUrl

setModelDataSource

getModelDataSource

withModelDataSource

setProductId

getProductId

withProductId

getEnvironment

setEnvironment

withEnvironment

addEnvironmentEntry

clearEnvironmentEntries

setModelInput

getModelInput

withModelInput

setFramework

getFramework

withFramework

setFrameworkVersion

getFrameworkVersion

withFrameworkVersion

setNearestModelName

getNearestModelName

withNearestModelName

setAdditionalS3DataSource

getAdditionalS3DataSource

withAdditionalS3DataSource

toString

equals

hashCode

clone

marshall