aws-native.sagemaker.getInferenceComponent

AWS Cloud Control v1.81.0, Sep 28 26

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

Viewing docs for AWS Cloud Control v1.81.0
published on Monday, Sep 28, 2026 by Pulumi

Schema (JSON)

pulumi/pulumi-aws-native

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

Viewing docs for AWS Cloud Control v1.81.0
published on Monday, Sep 28, 2026 by Pulumi

Schema (JSON)

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?: InvokeOutputOptions): 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: pulumi.Input[Optional[str]] = None,
                            opts: Optional[InvokeOutputOptions] = 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 Output<GetInferenceComponentResult> Invoke(GetInferenceComponentInvokeArgs args, InvokeOutputOptions opts)
}

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

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

data "aws-native_sagemaker_get_inference_component" "name" {
    # arguments
}

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.

inference_component_arn 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
Specifications List<Pulumi.AwsNative.SageMaker.Outputs.InferenceComponentSpecificationForInstanceType>
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
Specifications []InferenceComponentSpecificationForInstanceType
Tags Tag
VariantName string: The name of the production variant that hosts the inference component.

creation_time string: The time when the inference component was created.
endpoint_arn string: The Amazon Resource Name (ARN) of the endpoint that hosts the inference component.
endpoint_name string: The name of the endpoint that hosts the inference component.
failure_reason string
inference_component_arn string: The Amazon Resource Name (ARN) of the inference component.
inference_component_name string: The name of the inference component.
inference_component_status "InService" | "Creating" | "Updating" | "Failed" | "Deleting": The status of the inference component.
last_modified_time string: The time when the inference component was last updated.
runtime_config object
specification object
specifications list(object)
tags list(object)
variant_name 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
specifications List<InferenceComponentSpecificationForInstanceType>
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
specifications InferenceComponentSpecificationForInstanceType[]
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
specifications Sequence[InferenceComponentSpecificationForInstanceType]
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
specifications List<Property Map>
tags List<Property Map>
variantName String: The name of the production variant that hosts the inference component.

Supporting Types

InferenceComponentAvailabilityZoneBalance

EnforcementMode Pulumi.AwsNative.SageMaker.InferenceComponentAvailabilityZoneBalanceEnforcementMode
MaxImbalance int: The maximum allowed difference in the number of inference component copies between any two Availability Zones

EnforcementMode InferenceComponentAvailabilityZoneBalanceEnforcementMode
MaxImbalance int: The maximum allowed difference in the number of inference component copies between any two Availability Zones

enforcement_mode "PERMISSIVE"
max_imbalance number: The maximum allowed difference in the number of inference component copies between any two Availability Zones

enforcementMode InferenceComponentAvailabilityZoneBalanceEnforcementMode
maxImbalance Integer: The maximum allowed difference in the number of inference component copies between any two Availability Zones

enforcementMode InferenceComponentAvailabilityZoneBalanceEnforcementMode
maxImbalance number: The maximum allowed difference in the number of inference component copies between any two Availability Zones

enforcement_mode InferenceComponentAvailabilityZoneBalanceEnforcementMode
max_imbalance int: The maximum allowed difference in the number of inference component copies between any two Availability Zones

enforcementMode "PERMISSIVE"
maxImbalance Number: The maximum allowed difference in the number of inference component copies between any two Availability Zones

InferenceComponentAvailabilityZoneBalanceEnforcementMode

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.

max_memory_required_in_mb number: The maximum MB of memory to allocate to run a model that you assign to an inference component.
min_memory_required_in_mb number: The minimum MB of memory to allocate to run a model that you assign to an inference component.
number_of_accelerator_devices_required number: 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 number: 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.

InferenceComponentContainerMetricsConfig

MetricsEndpoints List<Pulumi.AwsNative.SageMaker.Inputs.InferenceComponentMetricsEndpoint>

MetricsEndpoints []InferenceComponentMetricsEndpoint

metrics_endpoints list(object)

metricsEndpoints List<InferenceComponentMetricsEndpoint>

metricsEndpoints InferenceComponentMetricsEndpoint[]

metrics_endpoints Sequence[InferenceComponentMetricsEndpoint]

metricsEndpoints List<Property Map>

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).
ContainerMetricsConfig Pulumi.AwsNative.SageMaker.Inputs.InferenceComponentContainerMetricsConfig
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).
ContainerMetricsConfig InferenceComponentContainerMetricsConfig
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.

artifact_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).
container_metrics_config object
deployed_image object
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.

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).
containerMetricsConfig InferenceComponentContainerMetricsConfig
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).
containerMetricsConfig InferenceComponentContainerMetricsConfig
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).
container_metrics_config InferenceComponentContainerMetricsConfig
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).
containerMetricsConfig Property Map
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.

InferenceComponentContainerSpecificationForInstanceType

ArtifactUrl string
ContainerMetricsConfig Pulumi.AwsNative.SageMaker.Inputs.InferenceComponentContainerMetricsConfig
Environment Dictionary<string, string>
Image string

ArtifactUrl string
ContainerMetricsConfig InferenceComponentContainerMetricsConfig
Environment map[string]string
Image string

artifact_url string
container_metrics_config object
environment map(string)
image string

artifactUrl String
containerMetricsConfig InferenceComponentContainerMetricsConfig
environment Map<String,String>
image String

artifactUrl string
containerMetricsConfig InferenceComponentContainerMetricsConfig
environment {[key: string]: string}
image string

artifact_url str
container_metrics_config InferenceComponentContainerMetricsConfig
environment Mapping[str, str]
image str

artifactUrl String
containerMetricsConfig Property Map
environment Map<String>
image String

InferenceComponentDataCacheConfig

EnableCaching bool: Whether the endpoint caches the model artifacts and container image on each instance it provisions for the inference component

EnableCaching bool: Whether the endpoint caches the model artifacts and container image on each instance it provisions for the inference component

enable_caching bool: Whether the endpoint caches the model artifacts and container image on each instance it provisions for the inference component

enableCaching Boolean: Whether the endpoint caches the model artifacts and container image on each instance it provisions for the inference component

enableCaching boolean: Whether the endpoint caches the model artifacts and container image on each instance it provisions for the inference component

enable_caching bool: Whether the endpoint caches the model artifacts and container image on each instance it provisions for the inference component

enableCaching Boolean: Whether the endpoint caches the model artifacts and container image on each instance it provisions for the inference component

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.

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

InferenceComponentMetricsEndpoint

MetricsEndpointPath string: The path to the Prometheus formatted metrics endpoint exposed by the container
MetricPublishFrequencyInSeconds int: The interval, in seconds, at which container metrics scraped from the endpoint are published to Amazon CloudWatch. Valid values per the SageMaker API Reference are 10, 30, 60, 120, 180, 240 and 300; the service validates the value.

MetricsEndpointPath string: The path to the Prometheus formatted metrics endpoint exposed by the container
MetricPublishFrequencyInSeconds int: The interval, in seconds, at which container metrics scraped from the endpoint are published to Amazon CloudWatch. Valid values per the SageMaker API Reference are 10, 30, 60, 120, 180, 240 and 300; the service validates the value.

metrics_endpoint_path string: The path to the Prometheus formatted metrics endpoint exposed by the container
metric_publish_frequency_in_seconds number: The interval, in seconds, at which container metrics scraped from the endpoint are published to Amazon CloudWatch. Valid values per the SageMaker API Reference are 10, 30, 60, 120, 180, 240 and 300; the service validates the value.

metricsEndpointPath String: The path to the Prometheus formatted metrics endpoint exposed by the container
metricPublishFrequencyInSeconds Integer: The interval, in seconds, at which container metrics scraped from the endpoint are published to Amazon CloudWatch. Valid values per the SageMaker API Reference are 10, 30, 60, 120, 180, 240 and 300; the service validates the value.

metricsEndpointPath string: The path to the Prometheus formatted metrics endpoint exposed by the container
metricPublishFrequencyInSeconds number: The interval, in seconds, at which container metrics scraped from the endpoint are published to Amazon CloudWatch. Valid values per the SageMaker API Reference are 10, 30, 60, 120, 180, 240 and 300; the service validates the value.

metrics_endpoint_path str: The path to the Prometheus formatted metrics endpoint exposed by the container
metric_publish_frequency_in_seconds int: The interval, in seconds, at which container metrics scraped from the endpoint are published to Amazon CloudWatch. Valid values per the SageMaker API Reference are 10, 30, 60, 120, 180, 240 and 300; the service validates the value.

metricsEndpointPath String: The path to the Prometheus formatted metrics endpoint exposed by the container
metricPublishFrequencyInSeconds Number: The interval, in seconds, at which container metrics scraped from the endpoint are published to Amazon CloudWatch. Valid values per the SageMaker API Reference are 10, 30, 60, 120, 180, 240 and 300; the service validates the value.

CurrentCopyCount int
InstanceType string

current_copy_count number
instance_type string

currentCopyCount Integer
instanceType String

currentCopyCount number
instanceType string

current_copy_count int
instance_type str

currentCopyCount Number
instanceType String

InferenceComponentPlacementStrategy

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
PlacementStatus List<Pulumi.AwsNative.SageMaker.Inputs.InferenceComponentPlacementStatus>: The placement status of the inference component across instance types

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
PlacementStatus []InferenceComponentPlacementStatus: The placement status of the inference component across instance types

copy_count number: The number of runtime copies of the model container to deploy with the inference component. Each copy can serve inference requests.
current_copy_count number
desired_copy_count number
placement_status list(object): The placement status of the inference component across instance types

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
placementStatus List<InferenceComponentPlacementStatus>: The placement status of the inference component across instance types

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
placementStatus InferenceComponentPlacementStatus[]: The placement status of the inference component across instance types

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
placement_status Sequence[InferenceComponentPlacementStatus]: The placement status of the inference component across instance types

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
placementStatus List<Property Map>: The placement status of the inference component across instance types

InferenceComponentSchedulingConfig

AvailabilityZoneBalance Pulumi.AwsNative.SageMaker.Inputs.InferenceComponentAvailabilityZoneBalance
PlacementStrategy Pulumi.AwsNative.SageMaker.InferenceComponentPlacementStrategy

AvailabilityZoneBalance InferenceComponentAvailabilityZoneBalance
PlacementStrategy InferenceComponentPlacementStrategy

availability_zone_balance object
placement_strategy "SPREAD" | "BINPACK"

availabilityZoneBalance InferenceComponentAvailabilityZoneBalance
placementStrategy InferenceComponentPlacementStrategy

availabilityZoneBalance InferenceComponentAvailabilityZoneBalance
placementStrategy InferenceComponentPlacementStrategy

availability_zone_balance InferenceComponentAvailabilityZoneBalance
placement_strategy InferenceComponentPlacementStrategy

availabilityZoneBalance Property Map
placementStrategy "SPREAD" | "BINPACK"

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.

CurrentDataCacheConfig Pulumi.AwsNative.SageMaker.Inputs.InferenceComponentDataCacheConfig

The data caching configuration actually in effect, including a value the service chose rather than the template: SageMaker enables caching automatically on instance types with more than 232 GiB of local NVMe storage, whether or not DataCacheConfig was set. Returned by Describe and not settable; set DataCacheConfig instead.

DataCacheConfig Pulumi.AwsNative.SageMaker.Inputs.InferenceComponentDataCacheConfig

ModelName string

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

SchedulingConfig Pulumi.AwsNative.SageMaker.Inputs.InferenceComponentSchedulingConfig

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.

CurrentDataCacheConfig InferenceComponentDataCacheConfig

DataCacheConfig InferenceComponentDataCacheConfig

ModelName string

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

SchedulingConfig InferenceComponentSchedulingConfig

StartupParameters InferenceComponentStartupParameters

Settings that take effect while the model container starts up.

base_inference_component_name 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.

compute_resource_requirements object

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

container object

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

current_data_cache_config object

data_cache_config object

model_name string

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

scheduling_config object

startup_parameters object

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.

currentDataCacheConfig InferenceComponentDataCacheConfig

dataCacheConfig InferenceComponentDataCacheConfig

modelName String

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

schedulingConfig InferenceComponentSchedulingConfig

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.

currentDataCacheConfig InferenceComponentDataCacheConfig

dataCacheConfig InferenceComponentDataCacheConfig

modelName string

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

schedulingConfig InferenceComponentSchedulingConfig

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.

current_data_cache_config InferenceComponentDataCacheConfig

data_cache_config InferenceComponentDataCacheConfig

model_name str

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

scheduling_config InferenceComponentSchedulingConfig

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.

currentDataCacheConfig Property Map

dataCacheConfig Property Map

modelName String

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

schedulingConfig Property Map

startupParameters Property Map

Settings that take effect while the model container starts up.

InferenceComponentSpecificationForInstanceType

InstanceType string
ComputeResourceRequirements Pulumi.AwsNative.SageMaker.Inputs.InferenceComponentComputeResourceRequirements
Container Pulumi.AwsNative.SageMaker.Inputs.InferenceComponentContainerSpecificationForInstanceType
CurrentDataCacheConfig Pulumi.AwsNative.SageMaker.Inputs.InferenceComponentDataCacheConfig: The data caching configuration actually in effect for this instance type, including a value the service chose rather than the template: SageMaker enables caching automatically on instance types with more than 232 GiB of local NVMe storage, whether or not DataCacheConfig was set. Returned by Describe and not settable; set DataCacheConfig instead.
DataCacheConfig Pulumi.AwsNative.SageMaker.Inputs.InferenceComponentDataCacheConfig
ModelName string
SchedulingConfig Pulumi.AwsNative.SageMaker.Inputs.InferenceComponentSchedulingConfig
StartupParameters Pulumi.AwsNative.SageMaker.Inputs.InferenceComponentStartupParameters

InstanceType string
ComputeResourceRequirements InferenceComponentComputeResourceRequirements
Container InferenceComponentContainerSpecificationForInstanceType
CurrentDataCacheConfig InferenceComponentDataCacheConfig: The data caching configuration actually in effect for this instance type, including a value the service chose rather than the template: SageMaker enables caching automatically on instance types with more than 232 GiB of local NVMe storage, whether or not DataCacheConfig was set. Returned by Describe and not settable; set DataCacheConfig instead.
DataCacheConfig InferenceComponentDataCacheConfig
ModelName string
SchedulingConfig InferenceComponentSchedulingConfig
StartupParameters InferenceComponentStartupParameters

instance_type string
compute_resource_requirements object
container object
current_data_cache_config object: The data caching configuration actually in effect for this instance type, including a value the service chose rather than the template: SageMaker enables caching automatically on instance types with more than 232 GiB of local NVMe storage, whether or not DataCacheConfig was set. Returned by Describe and not settable; set DataCacheConfig instead.
data_cache_config object
model_name string
scheduling_config object
startup_parameters object

instanceType String
computeResourceRequirements InferenceComponentComputeResourceRequirements
container InferenceComponentContainerSpecificationForInstanceType
currentDataCacheConfig InferenceComponentDataCacheConfig: The data caching configuration actually in effect for this instance type, including a value the service chose rather than the template: SageMaker enables caching automatically on instance types with more than 232 GiB of local NVMe storage, whether or not DataCacheConfig was set. Returned by Describe and not settable; set DataCacheConfig instead.
dataCacheConfig InferenceComponentDataCacheConfig
modelName String
schedulingConfig InferenceComponentSchedulingConfig
startupParameters InferenceComponentStartupParameters

instanceType string
computeResourceRequirements InferenceComponentComputeResourceRequirements
container InferenceComponentContainerSpecificationForInstanceType
currentDataCacheConfig InferenceComponentDataCacheConfig: The data caching configuration actually in effect for this instance type, including a value the service chose rather than the template: SageMaker enables caching automatically on instance types with more than 232 GiB of local NVMe storage, whether or not DataCacheConfig was set. Returned by Describe and not settable; set DataCacheConfig instead.
dataCacheConfig InferenceComponentDataCacheConfig
modelName string
schedulingConfig InferenceComponentSchedulingConfig
startupParameters InferenceComponentStartupParameters

instance_type str
compute_resource_requirements InferenceComponentComputeResourceRequirements
container InferenceComponentContainerSpecificationForInstanceType
current_data_cache_config InferenceComponentDataCacheConfig: The data caching configuration actually in effect for this instance type, including a value the service chose rather than the template: SageMaker enables caching automatically on instance types with more than 232 GiB of local NVMe storage, whether or not DataCacheConfig was set. Returned by Describe and not settable; set DataCacheConfig instead.
data_cache_config InferenceComponentDataCacheConfig
model_name str
scheduling_config InferenceComponentSchedulingConfig
startup_parameters InferenceComponentStartupParameters

instanceType String
computeResourceRequirements Property Map
container Property Map
currentDataCacheConfig Property Map: The data caching configuration actually in effect for this instance type, including a value the service chose rather than the template: SageMaker enables caching automatically on instance types with more than 232 GiB of local NVMe storage, whether or not DataCacheConfig was set. Returned by Describe and not settable; set DataCacheConfig instead.
dataCacheConfig Property Map
modelName String
schedulingConfig Property Map
startupParameters Property Map

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.

container_startup_health_check_timeout_in_seconds 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 .
model_data_download_timeout_in_seconds 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.

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.

Viewing docs for AWS Cloud Control v1.81.0
published on Monday, Sep 28, 2026 by Pulumi

Schema (JSON)

pulumi/pulumi-aws-native

aws-native.sagemaker.getInferenceComponent

On this page

On this page

Using getInferenceComponent

getInferenceComponent Result

Supporting Types

InferenceComponentAvailabilityZoneBalance

InferenceComponentAvailabilityZoneBalanceEnforcementMode

InferenceComponentComputeResourceRequirements

InferenceComponentContainerMetricsConfig

InferenceComponentContainerSpecification

InferenceComponentContainerSpecificationForInstanceType

InferenceComponentDataCacheConfig

InferenceComponentDeployedImage

InferenceComponentMetricsEndpoint

InferenceComponentPlacementStatus

InferenceComponentPlacementStrategy

InferenceComponentRuntimeConfig

InferenceComponentSchedulingConfig

InferenceComponentSpecification

InferenceComponentSpecificationForInstanceType

InferenceComponentStartupParameters

InferenceComponentStatus

Tag

Package Details

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aws-native.sagemaker.getInferenceComponent

On this page

On this page

Using getInferenceComponent

getInferenceComponent Result

Supporting Types

InferenceComponentAvailabilityZoneBalance

InferenceComponentAvailabilityZoneBalanceEnforcementMode

InferenceComponentComputeResourceRequirements

InferenceComponentContainerMetricsConfig

InferenceComponentContainerSpecification

InferenceComponentContainerSpecificationForInstanceType

InferenceComponentDataCacheConfig

InferenceComponentDeployedImage

InferenceComponentMetricsEndpoint

InferenceComponentPlacementStatus

InferenceComponentPlacementStrategy

InferenceComponentRuntimeConfig

InferenceComponentSchedulingConfig

InferenceComponentSpecification

InferenceComponentSpecificationForInstanceType

InferenceComponentStartupParameters

InferenceComponentStatus

Tag

Package Details

On this page

On this page

Try Pulumi Cloud free.Your team will thank you.

Try Pulumi Cloud free.
Your team will thank you.