Class: Aws::SageMaker::Types::ModelPackageContainerDefinition
- Inherits:
-
Struct
- Object
- Struct
- Aws::SageMaker::Types::ModelPackageContainerDefinition
- Defined in:
- gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb
Overview
Describes the Docker container for the model package.
Constant Summary collapse
- SENSITIVE =
[]
Instance Attribute Summary collapse
-
#additional_s3_data_source ⇒ Types::AdditionalS3DataSource
The additional data source that is used during inference in the Docker container for your model package.
-
#container_hostname ⇒ String
The DNS host name for the Docker container.
-
#environment ⇒ Hash<String,String>
The environment variables to set in the Docker container.
-
#framework ⇒ String
The machine learning framework of the model package container image.
-
#framework_version ⇒ String
The framework version of the Model Package Container Image.
-
#image ⇒ String
The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
-
#image_digest ⇒ String
An MD5 hash of the training algorithm that identifies the Docker image used for training.
-
#model_data_source ⇒ Types::ModelDataSource
Specifies the location of ML model data to deploy during endpoint creation.
-
#model_data_url ⇒ String
The Amazon S3 path where the model artifacts, which result from model training, are stored.
-
#model_input ⇒ Types::ModelInput
A structure with Model Input details.
-
#nearest_model_name ⇒ String
The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model.
-
#product_id ⇒ String
The Amazon Web Services Marketplace product ID of the model package.
Instance Attribute Details
#additional_s3_data_source ⇒ Types::AdditionalS3DataSource
The additional data source that is used during inference in the Docker container for your model package.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 33124 class ModelPackageContainerDefinition < Struct.new( :container_hostname, :image, :image_digest, :model_data_url, :model_data_source, :product_id, :environment, :model_input, :framework, :framework_version, :nearest_model_name, :additional_s3_data_source) SENSITIVE = [] include Aws::Structure end |
#container_hostname ⇒ String
The DNS host name for the Docker container.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 33124 class ModelPackageContainerDefinition < Struct.new( :container_hostname, :image, :image_digest, :model_data_url, :model_data_source, :product_id, :environment, :model_input, :framework, :framework_version, :nearest_model_name, :additional_s3_data_source) SENSITIVE = [] include Aws::Structure end |
#environment ⇒ Hash<String,String>
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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 33124 class ModelPackageContainerDefinition < Struct.new( :container_hostname, :image, :image_digest, :model_data_url, :model_data_source, :product_id, :environment, :model_input, :framework, :framework_version, :nearest_model_name, :additional_s3_data_source) SENSITIVE = [] include Aws::Structure end |
#framework ⇒ String
The machine learning framework of the model package container image.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 33124 class ModelPackageContainerDefinition < Struct.new( :container_hostname, :image, :image_digest, :model_data_url, :model_data_source, :product_id, :environment, :model_input, :framework, :framework_version, :nearest_model_name, :additional_s3_data_source) SENSITIVE = [] include Aws::Structure end |
#framework_version ⇒ String
The framework version of the Model Package Container Image.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 33124 class ModelPackageContainerDefinition < Struct.new( :container_hostname, :image, :image_digest, :model_data_url, :model_data_source, :product_id, :environment, :model_input, :framework, :framework_version, :nearest_model_name, :additional_s3_data_source) SENSITIVE = [] include Aws::Structure end |
#image ⇒ String
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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 33124 class ModelPackageContainerDefinition < Struct.new( :container_hostname, :image, :image_digest, :model_data_url, :model_data_source, :product_id, :environment, :model_input, :framework, :framework_version, :nearest_model_name, :additional_s3_data_source) SENSITIVE = [] include Aws::Structure end |
#image_digest ⇒ String
An MD5 hash of the training algorithm that identifies the Docker image used for training.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 33124 class ModelPackageContainerDefinition < Struct.new( :container_hostname, :image, :image_digest, :model_data_url, :model_data_source, :product_id, :environment, :model_input, :framework, :framework_version, :nearest_model_name, :additional_s3_data_source) SENSITIVE = [] include Aws::Structure end |
#model_data_source ⇒ Types::ModelDataSource
Specifies the location of ML model data to deploy during endpoint creation.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 33124 class ModelPackageContainerDefinition < Struct.new( :container_hostname, :image, :image_digest, :model_data_url, :model_data_source, :product_id, :environment, :model_input, :framework, :framework_version, :nearest_model_name, :additional_s3_data_source) SENSITIVE = [] include Aws::Structure end |
#model_data_url ⇒ String
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).
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 33124 class ModelPackageContainerDefinition < Struct.new( :container_hostname, :image, :image_digest, :model_data_url, :model_data_source, :product_id, :environment, :model_input, :framework, :framework_version, :nearest_model_name, :additional_s3_data_source) SENSITIVE = [] include Aws::Structure end |
#model_input ⇒ Types::ModelInput
A structure with Model Input details.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 33124 class ModelPackageContainerDefinition < Struct.new( :container_hostname, :image, :image_digest, :model_data_url, :model_data_source, :product_id, :environment, :model_input, :framework, :framework_version, :nearest_model_name, :additional_s3_data_source) SENSITIVE = [] include Aws::Structure end |
#nearest_model_name ⇒ String
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
.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 33124 class ModelPackageContainerDefinition < Struct.new( :container_hostname, :image, :image_digest, :model_data_url, :model_data_source, :product_id, :environment, :model_input, :framework, :framework_version, :nearest_model_name, :additional_s3_data_source) SENSITIVE = [] include Aws::Structure end |
#product_id ⇒ String
The Amazon Web Services Marketplace product ID of the model package.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 33124 class ModelPackageContainerDefinition < Struct.new( :container_hostname, :image, :image_digest, :model_data_url, :model_data_source, :product_id, :environment, :model_input, :framework, :framework_version, :nearest_model_name, :additional_s3_data_source) SENSITIVE = [] include Aws::Structure end |