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
},
mode: "SingleModel", # accepts SingleModel, MultiModel
model_data_url: "Url",
environment: {
"EnvironmentKey" => "EnvironmentValue",
},
model_package_name: "VersionedArnOrName",
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).
-
#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 3001 class ContainerDefinition < Struct.new( :container_hostname, :image, :image_config, :mode, :model_data_url, :environment, :model_package_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 3001 class ContainerDefinition < Struct.new( :container_hostname, :image, :image_config, :mode, :model_data_url, :environment, :model_package_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 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
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 3001 class ContainerDefinition < Struct.new( :container_hostname, :image, :image_config, :mode, :model_data_url, :environment, :model_package_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 3001 class ContainerDefinition < Struct.new( :container_hostname, :image, :image_config, :mode, :model_data_url, :environment, :model_package_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 3001 class ContainerDefinition < Struct.new( :container_hostname, :image, :image_config, :mode, :model_data_url, :environment, :model_package_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 Amazon 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, 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
.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 3001 class ContainerDefinition < Struct.new( :container_hostname, :image, :image_config, :mode, :model_data_url, :environment, :model_package_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 3001 class ContainerDefinition < Struct.new( :container_hostname, :image, :image_config, :mode, :model_data_url, :environment, :model_package_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 3001 class ContainerDefinition < Struct.new( :container_hostname, :image, :image_config, :mode, :model_data_url, :environment, :model_package_name, :multi_model_config) SENSITIVE = [] include Aws::Structure end |