Class: Aws::SageMaker::Types::ContainerDefinition
- Inherits:
-
Struct
- Object
- Struct
- Aws::SageMaker::Types::ContainerDefinition
- Defined in:
- gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb
Overview
When making an API call, you may pass ContainerDefinition data as a hash:
{
container_hostname: "ContainerHostname",
image: "ContainerImage",
image_config: {
repository_access_mode: "Platform", # required, accepts Platform, Vpc
repository_auth_config: {
repository_credentials_provider_arn: "RepositoryCredentialsProviderArn", # required
},
},
mode: "SingleModel", # accepts SingleModel, MultiModel
model_data_url: "Url",
environment: {
"EnvironmentKey" => "EnvironmentValue",
},
model_package_name: "VersionedArnOrName",
inference_specification_name: "InferenceSpecificationName",
multi_model_config: {
model_cache_setting: "Enabled", # accepts Enabled, Disabled
},
}
Describes the container, as part of model definition.
Constant Summary collapse
- SENSITIVE =
[]
Instance Attribute Summary collapse
-
#container_hostname ⇒ String
This parameter is ignored for models that contain only a
PrimaryContainer
. -
#environment ⇒ Hash<String,String>
The environment variables to set in the Docker container.
-
#image ⇒ String
The path where inference code is stored.
-
#image_config ⇒ Types::ImageConfig
Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC).
-
#inference_specification_name ⇒ String
The inference specification name in the model package version.
-
#mode ⇒ String
Whether the container hosts a single model or multiple models.
-
#model_data_url ⇒ String
The S3 path where the model artifacts, which result from model training, are stored.
-
#model_package_name ⇒ String
The name or Amazon Resource Name (ARN) of the model package to use to create the model.
-
#multi_model_config ⇒ Types::MultiModelConfig
Specifies additional configuration for multi-model endpoints.
Instance Attribute Details
#container_hostname ⇒ String
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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 3762 class ContainerDefinition < Struct.new( :container_hostname, :image, :image_config, :mode, :model_data_url, :environment, :model_package_name, :inference_specification_name, :multi_model_config) 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 3762 class ContainerDefinition < Struct.new( :container_hostname, :image, :image_config, :mode, :model_data_url, :environment, :model_package_name, :inference_specification_name, :multi_model_config) SENSITIVE = [] include Aws::Structure end |
#image ⇒ String
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
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 3762 class ContainerDefinition < Struct.new( :container_hostname, :image, :image_config, :mode, :model_data_url, :environment, :model_package_name, :inference_specification_name, :multi_model_config) SENSITIVE = [] include Aws::Structure end |
#image_config ⇒ Types::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
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 3762 class ContainerDefinition < Struct.new( :container_hostname, :image, :image_config, :mode, :model_data_url, :environment, :model_package_name, :inference_specification_name, :multi_model_config) SENSITIVE = [] include Aws::Structure end |
#inference_specification_name ⇒ String
The inference specification name in the model package version.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 3762 class ContainerDefinition < Struct.new( :container_hostname, :image, :image_config, :mode, :model_data_url, :environment, :model_package_name, :inference_specification_name, :multi_model_config) SENSITIVE = [] include Aws::Structure end |
#mode ⇒ String
Whether the container hosts a single model or multiple models.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 3762 class ContainerDefinition < Struct.new( :container_hostname, :image, :image_config, :mode, :model_data_url, :environment, :model_package_name, :inference_specification_name, :multi_model_config) SENSITIVE = [] include Aws::Structure end |
#model_data_url ⇒ String
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.
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 IAM user 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
.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 3762 class ContainerDefinition < Struct.new( :container_hostname, :image, :image_config, :mode, :model_data_url, :environment, :model_package_name, :inference_specification_name, :multi_model_config) SENSITIVE = [] include Aws::Structure end |
#model_package_name ⇒ String
The name or Amazon Resource Name (ARN) of the model package to use to create the model.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 3762 class ContainerDefinition < Struct.new( :container_hostname, :image, :image_config, :mode, :model_data_url, :environment, :model_package_name, :inference_specification_name, :multi_model_config) SENSITIVE = [] include Aws::Structure end |
#multi_model_config ⇒ Types::MultiModelConfig
Specifies additional configuration for multi-model endpoints.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 3762 class ContainerDefinition < Struct.new( :container_hostname, :image, :image_config, :mode, :model_data_url, :environment, :model_package_name, :inference_specification_name, :multi_model_config) SENSITIVE = [] include Aws::Structure end |