com.amazonaws.services.sagemaker.model.ContainerDefinition Maven / Gradle / Ivy
/*
* Copyright 2015-2020 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;
/**
*
* Describes the container, as part of model definition.
*
*
* @see AWS API
* Documentation
*/
@Generated("com.amazonaws:aws-java-sdk-code-generator")
public class ContainerDefinition implements Serializable, Cloneable, StructuredPojo {
/**
*
* 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.
*
*/
private String containerHostname;
/**
*
* 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 Amazon SageMaker, the inference code must meet Amazon
* SageMaker requirements. Amazon SageMaker supports both registry/repository[:tag] and
* registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon
* SageMaker
*
*/
private String image;
/**
*
* 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
*
*/
private ImageConfig imageConfig;
/**
*
* Whether the container hosts a single model or multiple models.
*
*/
private String mode;
/**
*
* 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 Amazon 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, Amazon SageMaker uses AWS Security Token Service to download model
* artifacts from the S3 path you provide. AWS STS is activated in your IAM user account by default. If you
* previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more
* information, see Activating and
* Deactivating AWS STS in an AWS Region in the AWS Identity and Access Management User Guide.
*
*
*
* If you use a built-in algorithm to create a model, Amazon SageMaker requires that you provide a S3 path to the
* model artifacts in ModelDataUrl.
*
*
*/
private String modelDataUrl;
/**
*
* 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.
*
*/
private java.util.Map environment;
/**
*
* The name or Amazon Resource Name (ARN) of the model package to use to create the model.
*
*/
private String modelPackageName;
/**
*
* 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.
*
*
* @param 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.
*/
public void setContainerHostname(String containerHostname) {
this.containerHostname = 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.
*
*
* @return 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.
*/
public String getContainerHostname() {
return this.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.
*
*
* @param 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.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public ContainerDefinition withContainerHostname(String containerHostname) {
setContainerHostname(containerHostname);
return this;
}
/**
*
* 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 Amazon SageMaker, the inference code must meet Amazon
* SageMaker requirements. Amazon SageMaker supports both registry/repository[:tag] and
* registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon
* SageMaker
*
*
* @param 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 Amazon SageMaker, the inference code
* must meet Amazon SageMaker requirements. Amazon SageMaker supports both
* registry/repository[:tag] and registry/repository[@digest] image path formats.
* For more information, see Using Your Own Algorithms with
* Amazon SageMaker
*/
public void setImage(String image) {
this.image = 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 Amazon SageMaker, the inference code must meet Amazon
* SageMaker requirements. Amazon SageMaker supports both registry/repository[:tag] and
* registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon
* SageMaker
*
*
* @return 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 Amazon SageMaker, the inference code
* must meet Amazon SageMaker requirements. Amazon SageMaker supports both
* registry/repository[:tag] and registry/repository[@digest] image path formats.
* For more information, see Using Your Own Algorithms
* with Amazon SageMaker
*/
public String getImage() {
return this.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 Amazon SageMaker, the inference code must meet Amazon
* SageMaker requirements. Amazon SageMaker supports both registry/repository[:tag] and
* registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon
* SageMaker
*
*
* @param 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 Amazon SageMaker, the inference code
* must meet Amazon SageMaker requirements. Amazon SageMaker supports both
* registry/repository[:tag] and registry/repository[@digest] image path formats.
* For more information, see Using Your Own Algorithms with
* Amazon SageMaker
* @return Returns a reference to this object so that method calls can be chained together.
*/
public ContainerDefinition withImage(String image) {
setImage(image);
return this;
}
/**
*
* 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
*
*
* @param 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
*/
public void setImageConfig(ImageConfig imageConfig) {
this.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
*
*
* @return 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
*/
public ImageConfig getImageConfig() {
return this.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
*
*
* @param 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
* @return Returns a reference to this object so that method calls can be chained together.
*/
public ContainerDefinition withImageConfig(ImageConfig imageConfig) {
setImageConfig(imageConfig);
return this;
}
/**
*
* Whether the container hosts a single model or multiple models.
*
*
* @param mode
* Whether the container hosts a single model or multiple models.
* @see ContainerMode
*/
public void setMode(String mode) {
this.mode = mode;
}
/**
*
* Whether the container hosts a single model or multiple models.
*
*
* @return Whether the container hosts a single model or multiple models.
* @see ContainerMode
*/
public String getMode() {
return this.mode;
}
/**
*
* Whether the container hosts a single model or multiple models.
*
*
* @param mode
* Whether the container hosts a single model or multiple models.
* @return Returns a reference to this object so that method calls can be chained together.
* @see ContainerMode
*/
public ContainerDefinition withMode(String mode) {
setMode(mode);
return this;
}
/**
*
* Whether the container hosts a single model or multiple models.
*
*
* @param mode
* Whether the container hosts a single model or multiple models.
* @return Returns a reference to this object so that method calls can be chained together.
* @see ContainerMode
*/
public ContainerDefinition withMode(ContainerMode mode) {
this.mode = mode.toString();
return this;
}
/**
*
* 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 Amazon 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, Amazon SageMaker uses AWS Security Token Service to download model
* artifacts from the S3 path you provide. AWS STS is activated in your IAM user account by default. If you
* previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more
* information, see Activating and
* Deactivating AWS STS in an AWS Region in the AWS Identity and Access Management User Guide.
*
*
*
* If you use a built-in algorithm to create a model, Amazon SageMaker requires that you provide a S3 path to the
* model artifacts in ModelDataUrl.
*
*
*
* @param 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 Amazon 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, Amazon SageMaker uses AWS Security Token Service to download
* model artifacts from the S3 path you provide. AWS STS is activated in your IAM user account by default. If
* you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more
* information, see Activating
* and Deactivating AWS STS in an AWS Region in the AWS Identity and Access Management User Guide.
*
*
*
* If you use a built-in algorithm to create a model, Amazon SageMaker requires that you provide a S3 path to
* the model artifacts in ModelDataUrl.
*
*/
public void setModelDataUrl(String modelDataUrl) {
this.modelDataUrl = 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 Amazon 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, Amazon SageMaker uses AWS Security Token Service to download model
* artifacts from the S3 path you provide. AWS STS is activated in your IAM user account by default. If you
* previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more
* information, see Activating and
* Deactivating AWS STS in an AWS Region in the AWS Identity and Access Management User Guide.
*
*
*
* If you use a built-in algorithm to create a model, Amazon SageMaker requires that you provide a S3 path to the
* model artifacts in ModelDataUrl.
*
*
*
* @return 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 Amazon 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, Amazon SageMaker uses AWS Security Token Service to download
* model artifacts from the S3 path you provide. AWS STS is activated in your IAM user account by default.
* If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For
* more information, see Activating and Deactivating AWS STS in an AWS Region in the AWS Identity and Access Management
* User Guide.
*
*
*
* If you use a built-in algorithm to create a model, Amazon SageMaker requires that you provide a S3 path
* to the model artifacts in ModelDataUrl.
*
*/
public String getModelDataUrl() {
return this.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 Amazon 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, Amazon SageMaker uses AWS Security Token Service to download model
* artifacts from the S3 path you provide. AWS STS is activated in your IAM user account by default. If you
* previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more
* information, see Activating and
* Deactivating AWS STS in an AWS Region in the AWS Identity and Access Management User Guide.
*
*
*
* If you use a built-in algorithm to create a model, Amazon SageMaker requires that you provide a S3 path to the
* model artifacts in ModelDataUrl.
*
*
*
* @param 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 Amazon 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, Amazon SageMaker uses AWS Security Token Service to download
* model artifacts from the S3 path you provide. AWS STS is activated in your IAM user account by default. If
* you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more
* information, see Activating
* and Deactivating AWS STS in an AWS Region in the AWS Identity and Access Management User Guide.
*
*
*
* If you use a built-in algorithm to create a model, Amazon SageMaker requires that you provide a S3 path to
* the model artifacts in ModelDataUrl.
*
* @return Returns a reference to this object so that method calls can be chained together.
*/
public ContainerDefinition withModelDataUrl(String modelDataUrl) {
setModelDataUrl(modelDataUrl);
return this;
}
/**
*
* 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.
*
*
* @return 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.
*/
public java.util.Map getEnvironment() {
return 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.
*
*
* @param 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.
*/
public void setEnvironment(java.util.Map environment) {
this.environment = 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.
*
*
* @param 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.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public ContainerDefinition withEnvironment(java.util.Map environment) {
setEnvironment(environment);
return this;
}
/**
* Add a single Environment entry
*
* @see ContainerDefinition#withEnvironment
* @returns a reference to this object so that method calls can be chained together.
*/
public ContainerDefinition addEnvironmentEntry(String key, String value) {
if (null == this.environment) {
this.environment = new java.util.HashMap();
}
if (this.environment.containsKey(key))
throw new IllegalArgumentException("Duplicated keys (" + key.toString() + ") are provided.");
this.environment.put(key, value);
return this;
}
/**
* Removes all the entries added into Environment.
*
* @return Returns a reference to this object so that method calls can be chained together.
*/
public ContainerDefinition clearEnvironmentEntries() {
this.environment = null;
return this;
}
/**
*
* The name or Amazon Resource Name (ARN) of the model package to use to create the model.
*
*
* @param modelPackageName
* The name or Amazon Resource Name (ARN) of the model package to use to create the model.
*/
public void setModelPackageName(String modelPackageName) {
this.modelPackageName = modelPackageName;
}
/**
*
* The name or Amazon Resource Name (ARN) of the model package to use to create the model.
*
*
* @return The name or Amazon Resource Name (ARN) of the model package to use to create the model.
*/
public String getModelPackageName() {
return this.modelPackageName;
}
/**
*
* The name or Amazon Resource Name (ARN) of the model package to use to create the model.
*
*
* @param modelPackageName
* The name or Amazon Resource Name (ARN) of the model package to use to create the model.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public ContainerDefinition withModelPackageName(String modelPackageName) {
setModelPackageName(modelPackageName);
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 (getContainerHostname() != null)
sb.append("ContainerHostname: ").append(getContainerHostname()).append(",");
if (getImage() != null)
sb.append("Image: ").append(getImage()).append(",");
if (getImageConfig() != null)
sb.append("ImageConfig: ").append(getImageConfig()).append(",");
if (getMode() != null)
sb.append("Mode: ").append(getMode()).append(",");
if (getModelDataUrl() != null)
sb.append("ModelDataUrl: ").append(getModelDataUrl()).append(",");
if (getEnvironment() != null)
sb.append("Environment: ").append(getEnvironment()).append(",");
if (getModelPackageName() != null)
sb.append("ModelPackageName: ").append(getModelPackageName());
sb.append("}");
return sb.toString();
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (obj instanceof ContainerDefinition == false)
return false;
ContainerDefinition other = (ContainerDefinition) obj;
if (other.getContainerHostname() == null ^ this.getContainerHostname() == null)
return false;
if (other.getContainerHostname() != null && other.getContainerHostname().equals(this.getContainerHostname()) == false)
return false;
if (other.getImage() == null ^ this.getImage() == null)
return false;
if (other.getImage() != null && other.getImage().equals(this.getImage()) == false)
return false;
if (other.getImageConfig() == null ^ this.getImageConfig() == null)
return false;
if (other.getImageConfig() != null && other.getImageConfig().equals(this.getImageConfig()) == false)
return false;
if (other.getMode() == null ^ this.getMode() == null)
return false;
if (other.getMode() != null && other.getMode().equals(this.getMode()) == false)
return false;
if (other.getModelDataUrl() == null ^ this.getModelDataUrl() == null)
return false;
if (other.getModelDataUrl() != null && other.getModelDataUrl().equals(this.getModelDataUrl()) == false)
return false;
if (other.getEnvironment() == null ^ this.getEnvironment() == null)
return false;
if (other.getEnvironment() != null && other.getEnvironment().equals(this.getEnvironment()) == false)
return false;
if (other.getModelPackageName() == null ^ this.getModelPackageName() == null)
return false;
if (other.getModelPackageName() != null && other.getModelPackageName().equals(this.getModelPackageName()) == false)
return false;
return true;
}
@Override
public int hashCode() {
final int prime = 31;
int hashCode = 1;
hashCode = prime * hashCode + ((getContainerHostname() == null) ? 0 : getContainerHostname().hashCode());
hashCode = prime * hashCode + ((getImage() == null) ? 0 : getImage().hashCode());
hashCode = prime * hashCode + ((getImageConfig() == null) ? 0 : getImageConfig().hashCode());
hashCode = prime * hashCode + ((getMode() == null) ? 0 : getMode().hashCode());
hashCode = prime * hashCode + ((getModelDataUrl() == null) ? 0 : getModelDataUrl().hashCode());
hashCode = prime * hashCode + ((getEnvironment() == null) ? 0 : getEnvironment().hashCode());
hashCode = prime * hashCode + ((getModelPackageName() == null) ? 0 : getModelPackageName().hashCode());
return hashCode;
}
@Override
public ContainerDefinition clone() {
try {
return (ContainerDefinition) 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.ContainerDefinitionMarshaller.getInstance().marshall(this, protocolMarshaller);
}
}