Table of Contents
SageMakerRuntime.
Client
¶A low-level client representing Amazon SageMaker Runtime
The Amazon SageMaker runtime API.
client = session.create_client('sagemaker-runtime')
These are the available methods:
can_paginate
(operation_name)¶Check if an operation can be paginated.
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")
.True
if the operation can be paginated,
False
otherwise.close
()¶Closes underlying endpoint connections.
get_paginator
(operation_name)¶Create a paginator for an operation.
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")
.client.can_paginate
method to
check if an operation is pageable.get_waiter
(waiter_name)¶Returns an object that can wait for some condition.
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'
)
[REQUIRED]
The name of the endpoint that you specified when you created the endpoint using the CreateEndpoint API.
[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 .
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.
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
dict
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.
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
)
[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.
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.
[REQUIRED]
The Amazon S3 URI where the inference request payload is stored.
dict
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
Client exceptions are available on a client instance via the exceptions
property. For more detailed instructions and examples on the exact usage of client exceptions, see the error handling user guide.
The available client exceptions are:
SageMakerRuntime.Client.exceptions.InternalDependencyException
SageMakerRuntime.Client.exceptions.InternalFailure
SageMakerRuntime.Client.exceptions.ModelError
SageMakerRuntime.Client.exceptions.ModelNotReadyException
SageMakerRuntime.Client.exceptions.ServiceUnavailable
SageMakerRuntime.Client.exceptions.ValidationError
SageMakerRuntime.Client.exceptions.
InternalDependencyException
¶Your request caused an exception with an internal dependency. Contact customer support.
Example
try:
...
except client.exceptions.InternalDependencyException as e:
print(e.response)
response
¶The parsed error response. All exceptions have a top level Error
key that provides normalized access to common exception atrributes. All other keys are specific to this service or exception class.
Syntax
{
'Message': 'string',
'Error': {
'Code': 'string',
'Message': 'string'
}
}
Structure
(dict) --
Your request caused an exception with an internal dependency. Contact customer support.
SageMakerRuntime.Client.exceptions.
InternalFailure
¶An internal failure occurred.
Example
try:
...
except client.exceptions.InternalFailure as e:
print(e.response)
response
¶The parsed error response. All exceptions have a top level Error
key that provides normalized access to common exception atrributes. All other keys are specific to this service or exception class.
Syntax
{
'Message': 'string',
'Error': {
'Code': 'string',
'Message': 'string'
}
}
Structure
(dict) --
An internal failure occurred.
SageMakerRuntime.Client.exceptions.
ModelError
¶Model (owned by the customer in the container) returned 4xx or 5xx error code.
Example
try:
...
except client.exceptions.ModelError as e:
print(e.response)
response
¶The parsed error response. All exceptions have a top level Error
key that provides normalized access to common exception atrributes. All other keys are specific to this service or exception class.
Syntax
{
'Message': 'string',
'OriginalStatusCode': 123,
'OriginalMessage': 'string',
'LogStreamArn': 'string',
'Error': {
'Code': 'string',
'Message': 'string'
}
}
Structure
(dict) --
Model (owned by the customer in the container) returned 4xx or 5xx error code.
Message (string) --
OriginalStatusCode (integer) --
Original status code.
OriginalMessage (string) --
Original message.
LogStreamArn (string) --
The Amazon Resource Name (ARN) of the log stream.
Error (dict) -- Normalized access to common exception attributes.
SageMakerRuntime.Client.exceptions.
ModelNotReadyException
¶Either a serverless endpoint variant's resources are still being provisioned, or a multi-model endpoint is still downloading or loading the target model. Wait and try your request again.
Example
try:
...
except client.exceptions.ModelNotReadyException as e:
print(e.response)
response
¶The parsed error response. All exceptions have a top level Error
key that provides normalized access to common exception atrributes. All other keys are specific to this service or exception class.
Syntax
{
'Message': 'string',
'Error': {
'Code': 'string',
'Message': 'string'
}
}
Structure
(dict) --
Either a serverless endpoint variant's resources are still being provisioned, or a multi-model endpoint is still downloading or loading the target model. Wait and try your request again.
The service is unavailable. Try your call again.
Example
try:
...
except client.exceptions.ServiceUnavailable as e:
print(e.response)
The parsed error response. All exceptions have a top level Error
key that provides normalized access to common exception atrributes. All other keys are specific to this service or exception class.
Syntax
{
'Message': 'string',
'Error': {
'Code': 'string',
'Message': 'string'
}
}
Structure
(dict) --
The service is unavailable. Try your call again.
SageMakerRuntime.Client.exceptions.
ValidationError
¶Inspect your request and try again.
Example
try:
...
except client.exceptions.ValidationError as e:
print(e.response)
response
¶The parsed error response. All exceptions have a top level Error
key that provides normalized access to common exception atrributes. All other keys are specific to this service or exception class.
Syntax
{
'Message': 'string',
'Error': {
'Code': 'string',
'Message': 'string'
}
}
Structure
(dict) --
Inspect your request and try again.
The available paginators are: