Interface CfnModelPackage.ModelPackageContainerDefinitionProperty
- All Superinterfaces:
software.amazon.jsii.JsiiSerializable
- All Known Implementing Classes:
CfnModelPackage.ModelPackageContainerDefinitionProperty.Jsii$Proxy
- Enclosing class:
CfnModelPackage
Example:
// The code below shows an example of how to instantiate this type.
// The values are placeholders you should change.
import software.amazon.awscdk.services.sagemaker.*;
Object modelInput;
ModelPackageContainerDefinitionProperty modelPackageContainerDefinitionProperty = ModelPackageContainerDefinitionProperty.builder()
.image("image")
// the properties below are optional
.containerHostname("containerHostname")
.environment(Map.of(
"environmentKey", "environment"))
.framework("framework")
.frameworkVersion("frameworkVersion")
.imageDigest("imageDigest")
.modelDataSource(ModelDataSourceProperty.builder()
.s3DataSource(S3ModelDataSourceProperty.builder()
.compressionType("compressionType")
.s3DataType("s3DataType")
.s3Uri("s3Uri")
// the properties below are optional
.modelAccessConfig(ModelAccessConfigProperty.builder()
.acceptEula(false)
.build())
.build())
.build())
.modelDataUrl("modelDataUrl")
.modelInput(modelInput)
.nearestModelName("nearestModelName")
.build();
- See Also:
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Nested Class Summary
Nested ClassesModifier and TypeInterfaceDescriptionstatic final classA builder forCfnModelPackage.ModelPackageContainerDefinitionPropertystatic final classAn implementation forCfnModelPackage.ModelPackageContainerDefinitionProperty -
Method Summary
Modifier and TypeMethodDescriptionbuilder()default StringThe DNS host name for the Docker container.default ObjectThe environment variables to set in the Docker container.default StringThe machine learning framework of the model package container image.default StringThe framework version of the Model Package Container Image.getImage()The Amazon Elastic Container Registry (Amazon ECR) path where inference code is stored.default StringAn MD5 hash of the training algorithm that identifies the Docker image used for training.default ObjectSpecifies the location of ML model data to deploy during endpoint creation.default StringThe Amazon S3 path where the model artifacts, which result from model training, are stored.default ObjectA structure with Model Input details.default StringThe name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model.Methods inherited from interface software.amazon.jsii.JsiiSerializable
$jsii$toJson
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Method Details
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getImage
The Amazon Elastic 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]andregistry/repository[@digest]image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .- See Also:
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getContainerHostname
The DNS host name for the Docker container.- See Also:
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getEnvironment
The environment variables to set in the Docker container.Each key and value in the
Environmentstring to string map can have length of up to 1024. We support up to 16 entries in the map.Returns union: either Mapinvalid input: '<'String,
String> orIResolvable- See Also:
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getFramework
The machine learning framework of the model package container image.- See Also:
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getFrameworkVersion
The framework version of the Model Package Container Image.- See Also:
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getImageDigest
An MD5 hash of the training algorithm that identifies the Docker image used for training.- See Also:
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getModelDataSource
Specifies the location of ML model data to deploy during endpoint creation.Returns union: either
IResolvableorCfnModelPackage.ModelDataSourceProperty- See Also:
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getModelDataUrl
The Amazon S3 path where the model artifacts, which result from model training, are stored.This path must point to a single
gzipcompressed tar archive (.tar.gzsuffix).The model artifacts must be in an S3 bucket that is in the same region as the model package.
- See Also:
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getModelInput
A structure with Model Input details.- See Also:
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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.- See Also:
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builder
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