ContainerDefinition (AWS SDK for Java

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

All Implemented Interfaces:

StructuredPojo, Serializable, Cloneable
```
@Generated(value="com.amazonaws:aws-java-sdk-code-generator")
public class ContainerDefinition
extends Object
implements Serializable, Cloneable, StructuredPojo
```
Describes the container, as part of model definition.

See Also:

AWS API Documentation, Serialized Form

Constructor Summary

Constructors
Constructor and Description

ContainerDefinition()

Constructors
Constructor and Description
`ContainerDefinition()`

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`ContainerDefinition`	`addEnvironmentEntry(String key, String value)` Add a single Environment entry
`ContainerDefinition`	`clearEnvironmentEntries()` Removes all the entries added into Environment.
`ContainerDefinition`	`clone()`
`boolean`	`equals(Object obj)`
`String`	`getContainerHostname()` This parameter is ignored for models that contain only a `PrimaryContainer`.
`Map<String,String>`	`getEnvironment()` The environment variables to set in the Docker container.
`String`	`getImage()` The path where inference code is stored.
`ImageConfig`	`getImageConfig()` Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC).
`String`	`getInferenceSpecificationName()` The inference specification name in the model package version.
`String`	`getMode()` Whether the container hosts a single model or multiple models.
`ModelDataSource`	`getModelDataSource()` Specifies the location of ML model data to deploy.
`String`	`getModelDataUrl()` The S3 path where the model artifacts, which result from model training, are stored.
`String`	`getModelPackageName()` The name or Amazon Resource Name (ARN) of the model package to use to create the model.
`MultiModelConfig`	`getMultiModelConfig()` Specifies additional configuration for multi-model endpoints.
`int`	`hashCode()`
`void`	`marshall(ProtocolMarshaller protocolMarshaller)` Marshalls this structured data using the given `ProtocolMarshaller`.
`void`	`setContainerHostname(String containerHostname)` This parameter is ignored for models that contain only a `PrimaryContainer`.
`void`	`setEnvironment(Map<String,String> environment)` The environment variables to set in the Docker container.
`void`	`setImage(String image)` The path where inference code is stored.
`void`	`setImageConfig(ImageConfig imageConfig)` Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC).
`void`	`setInferenceSpecificationName(String inferenceSpecificationName)` The inference specification name in the model package version.
`void`	`setMode(String mode)` Whether the container hosts a single model or multiple models.
`void`	`setModelDataSource(ModelDataSource modelDataSource)` Specifies the location of ML model data to deploy.
`void`	`setModelDataUrl(String modelDataUrl)` The S3 path where the model artifacts, which result from model training, are stored.
`void`	`setModelPackageName(String modelPackageName)` The name or Amazon Resource Name (ARN) of the model package to use to create the model.
`void`	`setMultiModelConfig(MultiModelConfig multiModelConfig)` Specifies additional configuration for multi-model endpoints.
`String`	`toString()` Returns a string representation of this object.
`ContainerDefinition`	`withContainerHostname(String containerHostname)` This parameter is ignored for models that contain only a `PrimaryContainer`.
`ContainerDefinition`	`withEnvironment(Map<String,String> environment)` The environment variables to set in the Docker container.
`ContainerDefinition`	`withImage(String image)` The path where inference code is stored.
`ContainerDefinition`	`withImageConfig(ImageConfig imageConfig)` Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC).
`ContainerDefinition`	`withInferenceSpecificationName(String inferenceSpecificationName)` The inference specification name in the model package version.
`ContainerDefinition`	`withMode(ContainerMode mode)` Whether the container hosts a single model or multiple models.
`ContainerDefinition`	`withMode(String mode)` Whether the container hosts a single model or multiple models.
`ContainerDefinition`	`withModelDataSource(ModelDataSource modelDataSource)` Specifies the location of ML model data to deploy.
`ContainerDefinition`	`withModelDataUrl(String modelDataUrl)` The S3 path where the model artifacts, which result from model training, are stored.
`ContainerDefinition`	`withModelPackageName(String modelPackageName)` The name or Amazon Resource Name (ARN) of the model package to use to create the model.
`ContainerDefinition`	`withMultiModelConfig(MultiModelConfig multiModelConfig)` Specifies additional configuration for multi-model endpoints.

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

- Constructor Detail
  - ContainerDefinition
```
public ContainerDefinition()
```
- Method Detail
  - setContainerHostname
```
public void setContainerHostname(String containerHostname)
```
    This parameter is ignored for models that contain only a PrimaryContainer.
    
    When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don't specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.
    
    Parameters:
    
    containerHostname - This parameter is ignored for models that contain only a PrimaryContainer.
    
    When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don't specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.
  - getContainerHostname
```
public String getContainerHostname()
```
    This parameter is ignored for models that contain only a PrimaryContainer.
    
    When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don't specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.
    
    Returns:
    
    This parameter is ignored for models that contain only a PrimaryContainer.
    
    When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don't specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.
  - withContainerHostname
```
public ContainerDefinition withContainerHostname(String containerHostname)
```
    This parameter is ignored for models that contain only a PrimaryContainer.
    
    When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don't specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.
    
    Parameters:
    
    containerHostname - This parameter is ignored for models that contain only a PrimaryContainer.
    
    When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don't specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setImage
```
public void setImage(String image)
```
    The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. 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.
    
    The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
    
    Parameters:
    
    image - The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. 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.
    
    The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
  - getImage
```
public String getImage()
```
    The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. 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.
    
    The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
    
    Returns:
    
    The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. 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.
    
    The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
  - withImage
```
public ContainerDefinition withImage(String image)
```
    The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. 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.
    
    The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
    
    Parameters:
    
    image - The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. 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.
    
    The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setImageConfig
```
public void setImageConfig(ImageConfig imageConfig)
```
    Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers.
    
    The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
    
    Parameters:
    
    imageConfig - Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers.
    
    The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
  - getImageConfig
```
public ImageConfig getImageConfig()
```
    Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers.
    
    The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
    
    Returns:
    
    Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers.
    
    The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
  - withImageConfig
```
public ContainerDefinition withImageConfig(ImageConfig imageConfig)
```
    Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers.
    
    The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
    
    Parameters:
    
    imageConfig - Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers.
    
    The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setMode
```
public void setMode(String mode)
```
    Whether the container hosts a single model or multiple models.
    
    Parameters:
    
    mode - Whether the container hosts a single model or multiple models.
    
    See Also:
    
    ContainerMode
  - getMode
```
public String getMode()
```
    Whether the container hosts a single model or multiple models.
    
    Returns:
    
    Whether the container hosts a single model or multiple models.
    
    See Also:
    
    ContainerMode
  - withMode
```
public ContainerDefinition withMode(String mode)
```
    Whether the container hosts a single model or multiple models.
    
    Parameters:
    
    mode - Whether the container hosts a single model or multiple models.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    ContainerMode
  - withMode
```
public ContainerDefinition withMode(ContainerMode mode)
```
    Whether the container hosts a single model or multiple models.
    
    Parameters:
    
    mode - Whether the container hosts a single model or multiple models.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    ContainerMode
  - setModelDataUrl
```
public void setModelDataUrl(String modelDataUrl)
```
    The 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 S3 path is required for SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.
    
    The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.
    
    If you provide a value for this parameter, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide.
    
    If you use a built-in algorithm to create a model, SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl.
    
    Parameters:
    
    modelDataUrl - The 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 S3 path is required for SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.
    
    The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.
    
    If you provide a value for this parameter, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide.
    
    If you use a built-in algorithm to create a model, SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl.
  - getModelDataUrl
```
public String getModelDataUrl()
```
    The 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 S3 path is required for SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.
    
    The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.
    
    If you provide a value for this parameter, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide.
    
    If you use a built-in algorithm to create a model, SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl.
    
    Returns:
    
    The 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 S3 path is required for SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.
    
    The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.
    
    If you provide a value for this parameter, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide.
    
    If you use a built-in algorithm to create a model, SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl.
  - withModelDataUrl
```
public ContainerDefinition withModelDataUrl(String modelDataUrl)
```
    The 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 S3 path is required for SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.
    
    The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.
    
    If you provide a value for this parameter, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide.
    
    If you use a built-in algorithm to create a model, SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl.
    
    Parameters:
    
    modelDataUrl - The 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 S3 path is required for SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.
    
    The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.
    
    If you provide a value for this parameter, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide.
    
    If you use a built-in algorithm to create a model, SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl.
    
    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.
    
    Currently you cannot use ModelDataSource in conjunction with SageMaker batch transform, SageMaker serverless endpoints, SageMaker multi-model endpoints, and SageMaker Marketplace.
    
    Parameters:
    
    modelDataSource - Specifies the location of ML model data to deploy.
    
    Currently you cannot use ModelDataSource in conjunction with SageMaker batch transform, SageMaker serverless endpoints, SageMaker multi-model endpoints, and SageMaker Marketplace.
  - getModelDataSource
```
public ModelDataSource getModelDataSource()
```
    Specifies the location of ML model data to deploy.
    
    Currently you cannot use ModelDataSource in conjunction with SageMaker batch transform, SageMaker serverless endpoints, SageMaker multi-model endpoints, and SageMaker Marketplace.
    
    Returns:
    
    Specifies the location of ML model data to deploy.
    
    Currently you cannot use ModelDataSource in conjunction with SageMaker batch transform, SageMaker serverless endpoints, SageMaker multi-model endpoints, and SageMaker Marketplace.
  - withModelDataSource
```
public ContainerDefinition withModelDataSource(ModelDataSource modelDataSource)
```
    Specifies the location of ML model data to deploy.
    
    Currently you cannot use ModelDataSource in conjunction with SageMaker batch transform, SageMaker serverless endpoints, SageMaker multi-model endpoints, and SageMaker Marketplace.
    
    Parameters:
    
    modelDataSource - Specifies the location of ML model data to deploy.
    
    Currently you cannot use ModelDataSource in conjunction with SageMaker batch transform, SageMaker serverless endpoints, SageMaker multi-model endpoints, and SageMaker Marketplace.
    
    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 ContainerDefinition 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 ContainerDefinition addEnvironmentEntry(String key,
                                               String value)
```
    Add a single Environment entry
    
    See Also:
    
    withEnvironment(java.util.Map<java.lang.String, java.lang.String>)
  - clearEnvironmentEntries
```
public ContainerDefinition clearEnvironmentEntries()
```
    Removes all the entries added into Environment.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setModelPackageName
```
public void setModelPackageName(String modelPackageName)
```
    The name or Amazon Resource Name (ARN) of the model package to use to create the model.
    
    Parameters:
    
    modelPackageName - The name or Amazon Resource Name (ARN) of the model package to use to create the model.
  - getModelPackageName
```
public String getModelPackageName()
```
    The name or Amazon Resource Name (ARN) of the model package to use to create the model.
    
    Returns:
    
    The name or Amazon Resource Name (ARN) of the model package to use to create the model.
  - withModelPackageName
```
public ContainerDefinition withModelPackageName(String modelPackageName)
```
    The name or Amazon Resource Name (ARN) of the model package to use to create the model.
    
    Parameters:
    
    modelPackageName - The name or Amazon Resource Name (ARN) of the model package to use to create the model.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setInferenceSpecificationName
```
public void setInferenceSpecificationName(String inferenceSpecificationName)
```
    The inference specification name in the model package version.
    
    Parameters:
    
    inferenceSpecificationName - The inference specification name in the model package version.
  - getInferenceSpecificationName
```
public String getInferenceSpecificationName()
```
    The inference specification name in the model package version.
    
    Returns:
    
    The inference specification name in the model package version.
  - withInferenceSpecificationName
```
public ContainerDefinition withInferenceSpecificationName(String inferenceSpecificationName)
```
    The inference specification name in the model package version.
    
    Parameters:
    
    inferenceSpecificationName - The inference specification name in the model package version.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setMultiModelConfig
```
public void setMultiModelConfig(MultiModelConfig multiModelConfig)
```
    Specifies additional configuration for multi-model endpoints.
    
    Parameters:
    
    multiModelConfig - Specifies additional configuration for multi-model endpoints.
  - getMultiModelConfig
```
public MultiModelConfig getMultiModelConfig()
```
    Specifies additional configuration for multi-model endpoints.
    
    Returns:
    
    Specifies additional configuration for multi-model endpoints.
  - withMultiModelConfig
```
public ContainerDefinition withMultiModelConfig(MultiModelConfig multiModelConfig)
```
    Specifies additional configuration for multi-model endpoints.
    
    Parameters:
    
    multiModelConfig - Specifies additional configuration for multi-model endpoints.
    
    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 ContainerDefinition 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.

AWS SDK for Java 1.x API Reference - 1.12.635

Class ContainerDefinition

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

ContainerDefinition

Method Detail

setContainerHostname

getContainerHostname

withContainerHostname

setImage

getImage

withImage

setImageConfig

getImageConfig

withImageConfig

setMode

getMode

withMode

withMode

setModelDataUrl

getModelDataUrl

withModelDataUrl

setModelDataSource

getModelDataSource

withModelDataSource

getEnvironment

setEnvironment

withEnvironment

addEnvironmentEntry

clearEnvironmentEntries

setModelPackageName

getModelPackageName

withModelPackageName

setInferenceSpecificationName

getInferenceSpecificationName

withInferenceSpecificationName

setMultiModelConfig

getMultiModelConfig

withMultiModelConfig

toString

equals

hashCode

clone

marshall