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SageMakerRuntime

Table of Contents

Client

class SageMakerRuntime.Client

A low-level client representing Amazon SageMaker Runtime

The Amazon SageMaker runtime API.

import boto3

client = boto3.client('sagemaker-runtime')

These are the available methods:

can_paginate(operation_name)

Check if an operation can be paginated.

Parameters
operation_name (string) -- The operation name. This is the same name as the method name on the client. For example, if the method name is create_foo, and you'd normally invoke the operation as client.create_foo(**kwargs), if the create_foo operation can be paginated, you can use the call client.get_paginator("create_foo").
Returns
True if the operation can be paginated, False otherwise.
close()

Closes underlying endpoint connections.

get_paginator(operation_name)

Create a paginator for an operation.

Parameters
operation_name (string) -- The operation name. This is the same name as the method name on the client. For example, if the method name is create_foo, and you'd normally invoke the operation as client.create_foo(**kwargs), if the create_foo operation can be paginated, you can use the call client.get_paginator("create_foo").
Raises OperationNotPageableError
Raised if the operation is not pageable. You can use the client.can_paginate method to check if an operation is pageable.
Return type
L{botocore.paginate.Paginator}
Returns
A paginator object.
get_waiter(waiter_name)

Returns an object that can wait for some condition.

Parameters
waiter_name (str) -- The name of the waiter to get. See the waiters section of the service docs for a list of available waiters.
Returns
The specified waiter object.
Return type
botocore.waiter.Waiter
invoke_endpoint(**kwargs)

After you deploy a model into production using Amazon SageMaker hosting services, your client applications use this API to get inferences from the model hosted at the specified endpoint.

For an overview of Amazon SageMaker, see How It Works .

Amazon SageMaker strips all POST headers except those supported by the API. Amazon SageMaker might add additional headers. You should not rely on the behavior of headers outside those enumerated in the request syntax.

Calls to InvokeEndpoint are authenticated by using Amazon Web Services Signature Version 4. For information, see Authenticating Requests (Amazon Web Services Signature Version 4) in the Amazon S3 API Reference .

A customer's model containers must respond to requests within 60 seconds. The model itself can have a maximum processing time of 60 seconds before responding to invocations. If your model is going to take 50-60 seconds of processing time, the SDK socket timeout should be set to be 70 seconds.

Note

Endpoints are scoped to an individual account, and are not public. The URL does not contain the account ID, but Amazon SageMaker determines the account ID from the authentication token that is supplied by the caller.

See also: AWS API Documentation

Request Syntax

response = client.invoke_endpoint(
    EndpointName='string',
    Body=b'bytes'|file,
    ContentType='string',
    Accept='string',
    CustomAttributes='string',
    TargetModel='string',
    TargetVariant='string',
    TargetContainerHostname='string',
    InferenceId='string',
    EnableExplanations='string'
)
Parameters
  • EndpointName (string) --

    [REQUIRED]

    The name of the endpoint that you specified when you created the endpoint using the CreateEndpoint API.

  • Body (bytes or seekable file-like object) --

    [REQUIRED]

    Provides input data, in the format specified in the ContentType request header. Amazon SageMaker passes all of the data in the body to the model.

    For information about the format of the request body, see Common Data Formats-Inference .

  • ContentType (string) -- The MIME type of the input data in the request body.
  • Accept (string) -- The desired MIME type of the inference in the response.
  • CustomAttributes (string) --

    Provides additional information about a request for an inference submitted to a model hosted at an Amazon SageMaker endpoint. The information is an opaque value that is forwarded verbatim. You could use this value, for example, to provide an ID that you can use to track a request or to provide other metadata that a service endpoint was programmed to process. The value must consist of no more than 1024 visible US-ASCII characters as specified in Section 3.3.6. Field Value Components of the Hypertext Transfer Protocol (HTTP/1.1).

    The code in your model is responsible for setting or updating any custom attributes in the response. If your code does not set this value in the response, an empty value is returned. For example, if a custom attribute represents the trace ID, your model can prepend the custom attribute with Trace ID: in your post-processing function.

    This feature is currently supported in the Amazon Web Services SDKs but not in the Amazon SageMaker Python SDK.

  • TargetModel (string) -- The model to request for inference when invoking a multi-model endpoint.
  • TargetVariant (string) --

    Specify the production variant to send the inference request to when invoking an endpoint that is running two or more variants. Note that this parameter overrides the default behavior for the endpoint, which is to distribute the invocation traffic based on the variant weights.

    For information about how to use variant targeting to perform a/b testing, see Test models in production

  • TargetContainerHostname (string) -- If the endpoint hosts multiple containers and is configured to use direct invocation, this parameter specifies the host name of the container to invoke.
  • InferenceId (string) -- If you provide a value, it is added to the captured data when you enable data capture on the endpoint. For information about data capture, see Capture Data .
  • EnableExplanations (string) -- An optional JMESPath expression used to override the EnableExplanations parameter of the ClarifyExplainerConfig API. See the EnableExplanations section in the developer guide for more information.
Return type

dict

Returns

Response Syntax

{
    'Body': StreamingBody(),
    'ContentType': 'string',
    'InvokedProductionVariant': 'string',
    'CustomAttributes': 'string'
}

Response Structure

  • (dict) --

    • Body (StreamingBody) --

      Includes the inference provided by the model.

      For information about the format of the response body, see Common Data Formats-Inference .

      If the explainer is activated, the body includes the explanations provided by the model. For more information, see the Response section under Invoke the Endpoint in the Developer Guide.

    • ContentType (string) --

      The MIME type of the inference returned in the response body.

    • InvokedProductionVariant (string) --

      Identifies the production variant that was invoked.

    • CustomAttributes (string) --

      Provides additional information in the response about the inference returned by a model hosted at an Amazon SageMaker endpoint. The information is an opaque value that is forwarded verbatim. You could use this value, for example, to return an ID received in the CustomAttributes header of a request or other metadata that a service endpoint was programmed to produce. The value must consist of no more than 1024 visible US-ASCII characters as specified in Section 3.3.6. Field Value Components of the Hypertext Transfer Protocol (HTTP/1.1). If the customer wants the custom attribute returned, the model must set the custom attribute to be included on the way back.

      The code in your model is responsible for setting or updating any custom attributes in the response. If your code does not set this value in the response, an empty value is returned. For example, if a custom attribute represents the trace ID, your model can prepend the custom attribute with Trace ID: in your post-processing function.

      This feature is currently supported in the Amazon Web Services SDKs but not in the Amazon SageMaker Python SDK.

Exceptions

  • SageMakerRuntime.Client.exceptions.InternalFailure
  • SageMakerRuntime.Client.exceptions.ServiceUnavailable
  • SageMakerRuntime.Client.exceptions.ValidationError
  • SageMakerRuntime.Client.exceptions.ModelError
  • SageMakerRuntime.Client.exceptions.InternalDependencyException
  • SageMakerRuntime.Client.exceptions.ModelNotReadyException
invoke_endpoint_async(**kwargs)

After you deploy a model into production using Amazon SageMaker hosting services, your client applications use this API to get inferences from the model hosted at the specified endpoint in an asynchronous manner.

Inference requests sent to this API are enqueued for asynchronous processing. The processing of the inference request may or may not complete before the you receive a response from this API. The response from this API will not contain the result of the inference request but contain information about where you can locate it.

Amazon SageMaker strips all POST headers except those supported by the API. Amazon SageMaker might add additional headers. You should not rely on the behavior of headers outside those enumerated in the request syntax.

Calls to InvokeEndpointAsync are authenticated by using Amazon Web Services Signature Version 4. For information, see Authenticating Requests (Amazon Web Services Signature Version 4) in the Amazon S3 API Reference .

See also: AWS API Documentation

Request Syntax

response = client.invoke_endpoint_async(
    EndpointName='string',
    ContentType='string',
    Accept='string',
    CustomAttributes='string',
    InferenceId='string',
    InputLocation='string',
    RequestTTLSeconds=123
)
Parameters
  • EndpointName (string) --

    [REQUIRED]

    The name of the endpoint that you specified when you created the endpoint using the ` CreateEndpoint https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpoint.html`__ API.

  • ContentType (string) -- The MIME type of the input data in the request body.
  • Accept (string) -- The desired MIME type of the inference in the response.
  • CustomAttributes (string) --

    Provides additional information about a request for an inference submitted to a model hosted at an Amazon SageMaker endpoint. The information is an opaque value that is forwarded verbatim. You could use this value, for example, to provide an ID that you can use to track a request or to provide other metadata that a service endpoint was programmed to process. The value must consist of no more than 1024 visible US-ASCII characters as specified in Section 3.3.6. Field Value Components of the Hypertext Transfer Protocol (HTTP/1.1).

    The code in your model is responsible for setting or updating any custom attributes in the response. If your code does not set this value in the response, an empty value is returned. For example, if a custom attribute represents the trace ID, your model can prepend the custom attribute with Trace ID : in your post-processing function.

    This feature is currently supported in the Amazon Web Services SDKs but not in the Amazon SageMaker Python SDK.

  • InferenceId (string) -- The identifier for the inference request. Amazon SageMaker will generate an identifier for you if none is specified.
  • InputLocation (string) --

    [REQUIRED]

    The Amazon S3 URI where the inference request payload is stored.

  • RequestTTLSeconds (integer) -- Maximum age in seconds a request can be in the queue before it is marked as expired.
Return type

dict

Returns

Response Syntax

{
    'InferenceId': 'string',
    'OutputLocation': 'string'
}

Response Structure

  • (dict) --

    • InferenceId (string) --

      Identifier for an inference request. This will be the same as the InferenceId specified in the input. Amazon SageMaker will generate an identifier for you if you do not specify one.

    • OutputLocation (string) --

      The Amazon S3 URI where the inference response payload is stored.

Exceptions

  • SageMakerRuntime.Client.exceptions.InternalFailure
  • SageMakerRuntime.Client.exceptions.ServiceUnavailable
  • SageMakerRuntime.Client.exceptions.ValidationError

Paginators

The available paginators are:

SageMakerFeatureStoreRuntime
SavingsPlans
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