SageMakerRuntime

Table of Contents

Client

class SageMakerRuntime.Client

A low-level client representing Amazon SageMaker Runtime:

client = session.create_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.
generate_presigned_url(ClientMethod, Params=None, ExpiresIn=3600, HttpMethod=None)

Generate a presigned url given a client, its method, and arguments

Parameters
  • ClientMethod (string) -- The client method to presign for
  • Params (dict) -- The parameters normally passed to ClientMethod.
  • ExpiresIn (int) -- The number of seconds the presigned url is valid for. By default it expires in an hour (3600 seconds)
  • HttpMethod (string) -- The http method to use on the generated url. By default, the http method is whatever is used in the method's model.
Returns

The presigned url

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 AWS Signature Version 4. For information, see Authenticating Requests (AWS 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 the /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'
)
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). This feature is currently supported in the AWS SDKs but not in the Amazon SageMaker Python SDK.
  • TargetModel (string) -- Specifies the model to be requested for an inference when invoking a multi-model endpoint.
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 .

    • 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.

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

Paginators

The available paginators are: