AWS Cloud Control v1.26.0, Mar 12 25

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AWS Cloud Control v1.26.0 published on Wednesday, Mar 12, 2025 by Pulumi

pulumi/pulumi-aws-native

aws-native.sagemaker.getInferenceComponent

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AWS Cloud Control v1.26.0 published on Wednesday, Mar 12, 2025 by Pulumi

pulumi/pulumi-aws-native

Using getInferenceComponent

Two invocation forms are available. The direct form accepts plain arguments and either blocks until the result value is available, or returns a Promise-wrapped result. The output form accepts Input-wrapped arguments and returns an Output-wrapped result.

function getInferenceComponent(args: GetInferenceComponentArgs, opts?: InvokeOptions): Promise<GetInferenceComponentResult>
function getInferenceComponentOutput(args: GetInferenceComponentOutputArgs, opts?: InvokeOptions): Output<GetInferenceComponentResult>

def get_inference_component(inference_component_arn: Optional[str] = None,
                            opts: Optional[InvokeOptions] = None) -> GetInferenceComponentResult
def get_inference_component_output(inference_component_arn: Optional[pulumi.Input[str]] = None,
                            opts: Optional[InvokeOptions] = None) -> Output[GetInferenceComponentResult]

func LookupInferenceComponent(ctx *Context, args *LookupInferenceComponentArgs, opts ...InvokeOption) (*LookupInferenceComponentResult, error)
func LookupInferenceComponentOutput(ctx *Context, args *LookupInferenceComponentOutputArgs, opts ...InvokeOption) LookupInferenceComponentResultOutput

> Note: This function is named LookupInferenceComponent in the Go SDK.

public static class GetInferenceComponent 
{
    public static Task<GetInferenceComponentResult> InvokeAsync(GetInferenceComponentArgs args, InvokeOptions? opts = null)
    public static Output<GetInferenceComponentResult> Invoke(GetInferenceComponentInvokeArgs args, InvokeOptions? opts = null)
}

public static CompletableFuture<GetInferenceComponentResult> getInferenceComponent(GetInferenceComponentArgs args, InvokeOptions options)
public static Output<GetInferenceComponentResult> getInferenceComponent(GetInferenceComponentArgs args, InvokeOptions options)

fn::invoke:
  function: aws-native:sagemaker:getInferenceComponent
  arguments:
    # arguments dictionary

The following arguments are supported:

InferenceComponentArn string: The Amazon Resource Name (ARN) of the inference component.

InferenceComponentArn string: The Amazon Resource Name (ARN) of the inference component.

inferenceComponentArn String: The Amazon Resource Name (ARN) of the inference component.

inferenceComponentArn string: The Amazon Resource Name (ARN) of the inference component.

inference_component_arn str: The Amazon Resource Name (ARN) of the inference component.

inferenceComponentArn String: The Amazon Resource Name (ARN) of the inference component.

getInferenceComponent Result

The following output properties are available:

CreationTime string: The time when the inference component was created.
EndpointArn string: The Amazon Resource Name (ARN) of the endpoint that hosts the inference component.
EndpointName string: The name of the endpoint that hosts the inference component.
FailureReason string
InferenceComponentArn string: The Amazon Resource Name (ARN) of the inference component.
InferenceComponentName string: The name of the inference component.
InferenceComponentStatus Pulumi.AwsNative.SageMaker.InferenceComponentStatus: The status of the inference component.
LastModifiedTime string: The time when the inference component was last updated.
RuntimeConfig Pulumi.AwsNative.SageMaker.Outputs.InferenceComponentRuntimeConfig
Specification Pulumi.AwsNative.SageMaker.Outputs.InferenceComponentSpecification
Tags List<Pulumi.AwsNative.Outputs.Tag>
VariantName string: The name of the production variant that hosts the inference component.

CreationTime string: The time when the inference component was created.
EndpointArn string: The Amazon Resource Name (ARN) of the endpoint that hosts the inference component.
EndpointName string: The name of the endpoint that hosts the inference component.
FailureReason string
InferenceComponentArn string: The Amazon Resource Name (ARN) of the inference component.
InferenceComponentName string: The name of the inference component.
InferenceComponentStatus InferenceComponentStatus: The status of the inference component.
LastModifiedTime string: The time when the inference component was last updated.
RuntimeConfig InferenceComponentRuntimeConfig
Specification InferenceComponentSpecification
Tags Tag
VariantName string: The name of the production variant that hosts the inference component.

creationTime String: The time when the inference component was created.
endpointArn String: The Amazon Resource Name (ARN) of the endpoint that hosts the inference component.
endpointName String: The name of the endpoint that hosts the inference component.
failureReason String
inferenceComponentArn String: The Amazon Resource Name (ARN) of the inference component.
inferenceComponentName String: The name of the inference component.
inferenceComponentStatus InferenceComponentStatus: The status of the inference component.
lastModifiedTime String: The time when the inference component was last updated.
runtimeConfig InferenceComponentRuntimeConfig
specification InferenceComponentSpecification
tags List<Tag>
variantName String: The name of the production variant that hosts the inference component.

creationTime string: The time when the inference component was created.
endpointArn string: The Amazon Resource Name (ARN) of the endpoint that hosts the inference component.
endpointName string: The name of the endpoint that hosts the inference component.
failureReason string
inferenceComponentArn string: The Amazon Resource Name (ARN) of the inference component.
inferenceComponentName string: The name of the inference component.
inferenceComponentStatus InferenceComponentStatus: The status of the inference component.
lastModifiedTime string: The time when the inference component was last updated.
runtimeConfig InferenceComponentRuntimeConfig
specification InferenceComponentSpecification
tags Tag[]
variantName string: The name of the production variant that hosts the inference component.

creation_time str: The time when the inference component was created.
endpoint_arn str: The Amazon Resource Name (ARN) of the endpoint that hosts the inference component.
endpoint_name str: The name of the endpoint that hosts the inference component.
failure_reason str
inference_component_arn str: The Amazon Resource Name (ARN) of the inference component.
inference_component_name str: The name of the inference component.
inference_component_status InferenceComponentStatus: The status of the inference component.
last_modified_time str: The time when the inference component was last updated.
runtime_config InferenceComponentRuntimeConfig
specification InferenceComponentSpecification
tags Sequence[root_Tag]
variant_name str: The name of the production variant that hosts the inference component.

creationTime String: The time when the inference component was created.
endpointArn String: The Amazon Resource Name (ARN) of the endpoint that hosts the inference component.
endpointName String: The name of the endpoint that hosts the inference component.
failureReason String
inferenceComponentArn String: The Amazon Resource Name (ARN) of the inference component.
inferenceComponentName String: The name of the inference component.
inferenceComponentStatus "InService" | "Creating" | "Updating" | "Failed" | "Deleting": The status of the inference component.
lastModifiedTime String: The time when the inference component was last updated.
runtimeConfig Property Map
specification Property Map
tags List<Property Map>
variantName String: The name of the production variant that hosts the inference component.

Supporting Types

InferenceComponentComputeResourceRequirements

MaxMemoryRequiredInMb int: The maximum MB of memory to allocate to run a model that you assign to an inference component.
MinMemoryRequiredInMb int: The minimum MB of memory to allocate to run a model that you assign to an inference component.
NumberOfAcceleratorDevicesRequired double: The number of accelerators to allocate to run a model that you assign to an inference component. Accelerators include GPUs and AWS Inferentia.
NumberOfCpuCoresRequired double: The number of CPU cores to allocate to run a model that you assign to an inference component.

MaxMemoryRequiredInMb int: The maximum MB of memory to allocate to run a model that you assign to an inference component.
MinMemoryRequiredInMb int: The minimum MB of memory to allocate to run a model that you assign to an inference component.
NumberOfAcceleratorDevicesRequired float64: The number of accelerators to allocate to run a model that you assign to an inference component. Accelerators include GPUs and AWS Inferentia.
NumberOfCpuCoresRequired float64: The number of CPU cores to allocate to run a model that you assign to an inference component.

maxMemoryRequiredInMb Integer: The maximum MB of memory to allocate to run a model that you assign to an inference component.
minMemoryRequiredInMb Integer: The minimum MB of memory to allocate to run a model that you assign to an inference component.
numberOfAcceleratorDevicesRequired Double: The number of accelerators to allocate to run a model that you assign to an inference component. Accelerators include GPUs and AWS Inferentia.
numberOfCpuCoresRequired Double: The number of CPU cores to allocate to run a model that you assign to an inference component.

maxMemoryRequiredInMb number: The maximum MB of memory to allocate to run a model that you assign to an inference component.
minMemoryRequiredInMb number: The minimum MB of memory to allocate to run a model that you assign to an inference component.
numberOfAcceleratorDevicesRequired number: The number of accelerators to allocate to run a model that you assign to an inference component. Accelerators include GPUs and AWS Inferentia.
numberOfCpuCoresRequired number: The number of CPU cores to allocate to run a model that you assign to an inference component.

max_memory_required_in_mb int: The maximum MB of memory to allocate to run a model that you assign to an inference component.
min_memory_required_in_mb int: The minimum MB of memory to allocate to run a model that you assign to an inference component.
number_of_accelerator_devices_required float: The number of accelerators to allocate to run a model that you assign to an inference component. Accelerators include GPUs and AWS Inferentia.
number_of_cpu_cores_required float: The number of CPU cores to allocate to run a model that you assign to an inference component.

maxMemoryRequiredInMb Number: The maximum MB of memory to allocate to run a model that you assign to an inference component.
minMemoryRequiredInMb Number: The minimum MB of memory to allocate to run a model that you assign to an inference component.
numberOfAcceleratorDevicesRequired Number: The number of accelerators to allocate to run a model that you assign to an inference component. Accelerators include GPUs and AWS Inferentia.
numberOfCpuCoresRequired Number: The number of CPU cores to allocate to run a model that you assign to an inference component.

InferenceComponentContainerSpecification

ArtifactUrl 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).
DeployedImage Pulumi.AwsNative.SageMaker.Inputs.InferenceComponentDeployedImage
Environment Dictionary<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.
Image string: The Amazon Elastic Container Registry (Amazon ECR) path where the Docker image for the model is stored.

ArtifactUrl 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).
DeployedImage InferenceComponentDeployedImage
Environment map[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.
Image string: The Amazon Elastic Container Registry (Amazon ECR) path where the Docker image for the model is stored.

artifactUrl 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).
deployedImage InferenceComponentDeployedImage
environment Map<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.
image String: The Amazon Elastic Container Registry (Amazon ECR) path where the Docker image for the model is stored.

artifactUrl 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).
deployedImage InferenceComponentDeployedImage
environment {[key: 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.
image string: The Amazon Elastic Container Registry (Amazon ECR) path where the Docker image for the model is stored.

artifact_url str: 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).
deployed_image InferenceComponentDeployedImage
environment Mapping[str, str]: 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.
image str: The Amazon Elastic Container Registry (Amazon ECR) path where the Docker image for the model is stored.

artifactUrl 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).
deployedImage Property Map
environment Map<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.
image String: The Amazon Elastic Container Registry (Amazon ECR) path where the Docker image for the model is stored.

InferenceComponentDeployedImage

ResolutionTime string: The date and time when the image path for the model resolved to the ResolvedImage
ResolvedImage string: The specific digest path of the image hosted in this ProductionVariant .
SpecifiedImage string: The image path you specified when you created the model.

ResolutionTime string: The date and time when the image path for the model resolved to the ResolvedImage
ResolvedImage string: The specific digest path of the image hosted in this ProductionVariant .
SpecifiedImage string: The image path you specified when you created the model.

resolutionTime String: The date and time when the image path for the model resolved to the ResolvedImage
resolvedImage String: The specific digest path of the image hosted in this ProductionVariant .
specifiedImage String: The image path you specified when you created the model.

resolutionTime string: The date and time when the image path for the model resolved to the ResolvedImage
resolvedImage string: The specific digest path of the image hosted in this ProductionVariant .
specifiedImage string: The image path you specified when you created the model.

resolution_time str: The date and time when the image path for the model resolved to the ResolvedImage
resolved_image str: The specific digest path of the image hosted in this ProductionVariant .
specified_image str: The image path you specified when you created the model.

resolutionTime String: The date and time when the image path for the model resolved to the ResolvedImage
resolvedImage String: The specific digest path of the image hosted in this ProductionVariant .
specifiedImage String: The image path you specified when you created the model.

InferenceComponentRuntimeConfig

CopyCount int: The number of runtime copies of the model container to deploy with the inference component. Each copy can serve inference requests.
CurrentCopyCount int
DesiredCopyCount int

CopyCount int: The number of runtime copies of the model container to deploy with the inference component. Each copy can serve inference requests.
CurrentCopyCount int
DesiredCopyCount int

copyCount Integer: The number of runtime copies of the model container to deploy with the inference component. Each copy can serve inference requests.
currentCopyCount Integer
desiredCopyCount Integer

copyCount number: The number of runtime copies of the model container to deploy with the inference component. Each copy can serve inference requests.
currentCopyCount number
desiredCopyCount number

copy_count int: The number of runtime copies of the model container to deploy with the inference component. Each copy can serve inference requests.
current_copy_count int
desired_copy_count int

copyCount Number: The number of runtime copies of the model container to deploy with the inference component. Each copy can serve inference requests.
currentCopyCount Number
desiredCopyCount Number

InferenceComponentSpecification

BaseInferenceComponentName string

The name of an existing inference component that is to contain the inference component that you're creating with your request.

Specify this parameter only if your request is meant to create an adapter inference component. An adapter inference component contains the path to an adapter model. The purpose of the adapter model is to tailor the inference output of a base foundation model, which is hosted by the base inference component. The adapter inference component uses the compute resources that you assigned to the base inference component.

When you create an adapter inference component, use the Container parameter to specify the location of the adapter artifacts. In the parameter value, use the ArtifactUrl parameter of the InferenceComponentContainerSpecification data type.

Before you can create an adapter inference component, you must have an existing inference component that contains the foundation model that you want to adapt.

ComputeResourceRequirements Pulumi.AwsNative.SageMaker.Inputs.InferenceComponentComputeResourceRequirements

The compute resources allocated to run the model, plus any adapter models, that you assign to the inference component.

Omit this parameter if your request is meant to create an adapter inference component. An adapter inference component is loaded by a base inference component, and it uses the compute resources of the base inference component.

Container Pulumi.AwsNative.SageMaker.Inputs.InferenceComponentContainerSpecification

Defines a container that provides the runtime environment for a model that you deploy with an inference component.

ModelName string

The name of an existing SageMaker AI model object in your account that you want to deploy with the inference component.

StartupParameters Pulumi.AwsNative.SageMaker.Inputs.InferenceComponentStartupParameters

Settings that take effect while the model container starts up.

BaseInferenceComponentName string

The name of an existing inference component that is to contain the inference component that you're creating with your request.

Before you can create an adapter inference component, you must have an existing inference component that contains the foundation model that you want to adapt.

ComputeResourceRequirements InferenceComponentComputeResourceRequirements

The compute resources allocated to run the model, plus any adapter models, that you assign to the inference component.

Container InferenceComponentContainerSpecification

Defines a container that provides the runtime environment for a model that you deploy with an inference component.

ModelName string

The name of an existing SageMaker AI model object in your account that you want to deploy with the inference component.

StartupParameters InferenceComponentStartupParameters

Settings that take effect while the model container starts up.

baseInferenceComponentName String

The name of an existing inference component that is to contain the inference component that you're creating with your request.

Before you can create an adapter inference component, you must have an existing inference component that contains the foundation model that you want to adapt.

computeResourceRequirements InferenceComponentComputeResourceRequirements

The compute resources allocated to run the model, plus any adapter models, that you assign to the inference component.

container InferenceComponentContainerSpecification

Defines a container that provides the runtime environment for a model that you deploy with an inference component.

modelName String

The name of an existing SageMaker AI model object in your account that you want to deploy with the inference component.

startupParameters InferenceComponentStartupParameters

Settings that take effect while the model container starts up.

baseInferenceComponentName string

The name of an existing inference component that is to contain the inference component that you're creating with your request.

Before you can create an adapter inference component, you must have an existing inference component that contains the foundation model that you want to adapt.

computeResourceRequirements InferenceComponentComputeResourceRequirements

The compute resources allocated to run the model, plus any adapter models, that you assign to the inference component.

container InferenceComponentContainerSpecification

Defines a container that provides the runtime environment for a model that you deploy with an inference component.

modelName string

The name of an existing SageMaker AI model object in your account that you want to deploy with the inference component.

startupParameters InferenceComponentStartupParameters

Settings that take effect while the model container starts up.

base_inference_component_name str

The name of an existing inference component that is to contain the inference component that you're creating with your request.

Before you can create an adapter inference component, you must have an existing inference component that contains the foundation model that you want to adapt.

compute_resource_requirements InferenceComponentComputeResourceRequirements

The compute resources allocated to run the model, plus any adapter models, that you assign to the inference component.

container InferenceComponentContainerSpecification

Defines a container that provides the runtime environment for a model that you deploy with an inference component.

model_name str

The name of an existing SageMaker AI model object in your account that you want to deploy with the inference component.

startup_parameters InferenceComponentStartupParameters

Settings that take effect while the model container starts up.

baseInferenceComponentName String

The name of an existing inference component that is to contain the inference component that you're creating with your request.

Before you can create an adapter inference component, you must have an existing inference component that contains the foundation model that you want to adapt.

computeResourceRequirements Property Map

The compute resources allocated to run the model, plus any adapter models, that you assign to the inference component.

container Property Map

Defines a container that provides the runtime environment for a model that you deploy with an inference component.

modelName String

The name of an existing SageMaker AI model object in your account that you want to deploy with the inference component.

startupParameters Property Map

Settings that take effect while the model container starts up.

InferenceComponentStartupParameters

ContainerStartupHealthCheckTimeoutInSeconds int: The timeout value, in seconds, for your inference container to pass health check by Amazon S3 Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests .
ModelDataDownloadTimeoutInSeconds int: The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this inference component.

ContainerStartupHealthCheckTimeoutInSeconds int: The timeout value, in seconds, for your inference container to pass health check by Amazon S3 Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests .
ModelDataDownloadTimeoutInSeconds int: The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this inference component.

containerStartupHealthCheckTimeoutInSeconds Integer: The timeout value, in seconds, for your inference container to pass health check by Amazon S3 Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests .
modelDataDownloadTimeoutInSeconds Integer: The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this inference component.

containerStartupHealthCheckTimeoutInSeconds number: The timeout value, in seconds, for your inference container to pass health check by Amazon S3 Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests .
modelDataDownloadTimeoutInSeconds number: The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this inference component.

container_startup_health_check_timeout_in_seconds int: The timeout value, in seconds, for your inference container to pass health check by Amazon S3 Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests .
model_data_download_timeout_in_seconds int: The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this inference component.

containerStartupHealthCheckTimeoutInSeconds Number: The timeout value, in seconds, for your inference container to pass health check by Amazon S3 Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests .
modelDataDownloadTimeoutInSeconds Number: The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this inference component.

InferenceComponentStatus

Package Details

Repository: AWS Native pulumi/pulumi-aws-native
License: Apache-2.0

We recommend new projects start with resources from the AWS provider.

AWS Cloud Control v1.26.0 published on Wednesday, Mar 12, 2025 by Pulumi

pulumi/pulumi-aws-native

aws-native.sagemaker.getInferenceComponent

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Using getInferenceComponent

getInferenceComponent Result

Supporting Types

InferenceComponentComputeResourceRequirements

InferenceComponentContainerSpecification

InferenceComponentDeployedImage

InferenceComponentRuntimeConfig

InferenceComponentSpecification

InferenceComponentStartupParameters

InferenceComponentStatus

Tag

Package Details

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