SageMakerRuntimeClient
Index > SageMakerRuntime > SageMakerRuntimeClient
Auto-generated documentation for SageMakerRuntime type annotations stubs module mypy-boto3-sagemaker-runtime.
SageMakerRuntimeClient
Type annotations and code completion for boto3.client("sagemaker-runtime")
.
boto3 documentation
from boto3.session import Session
from mypy_boto3_sagemaker_runtime.client import SageMakerRuntimeClient
def get_sagemaker-runtime_client() -> SageMakerRuntimeClient:
return Session().client("sagemaker-runtime")
Exceptions
boto3
client exceptions are generated in runtime.
This class provides code completion for boto3.client("sagemaker-runtime").exceptions
structure.
client = boto3.client("sagemaker-runtime")
try:
do_something(client)
except (
client.ClientError,
client.InternalDependencyException,
client.InternalFailure,
client.ModelError,
client.ModelNotReadyException,
client.ServiceUnavailable,
client.ValidationError,
) as e:
print(e)
from mypy_boto3_sagemaker_runtime.client import Exceptions
def handle_error(exc: Exceptions.ClientError) -> None:
...
Methods
can_paginate
Check if an operation can be paginated.
Type annotations and code completion for boto3.client("sagemaker-runtime").can_paginate
method.
boto3 documentation
close
Closes underlying endpoint connections.
Type annotations and code completion for boto3.client("sagemaker-runtime").close
method.
boto3 documentation
generate_presigned_url
Generate a presigned url given a client, its method, and arguments.
Type annotations and code completion for boto3.client("sagemaker-runtime").generate_presigned_url
method.
boto3 documentation
def generate_presigned_url(
self,
ClientMethod: str,
Params: Mapping[str, Any] = ...,
ExpiresIn: int = 3600,
HttpMethod: str = ...,
) -> str:
...
invoke_endpoint
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.
Type annotations and code completion for boto3.client("sagemaker-runtime").invoke_endpoint
method.
boto3 documentation
def invoke_endpoint(
self,
*,
EndpointName: str,
Body: Union[str, bytes, IO[Any], StreamingBody],
ContentType: str = ...,
Accept: str = ...,
CustomAttributes: str = ...,
TargetModel: str = ...,
TargetVariant: str = ...,
TargetContainerHostname: str = ...,
InferenceId: str = ...,
EnableExplanations: str = ...,
) -> InvokeEndpointOutputTypeDef: # (1)
...
kwargs: InvokeEndpointInputRequestTypeDef = { # (1)
"EndpointName": ...,
"Body": ...,
}
parent.invoke_endpoint(**kwargs)
invoke_endpoint_async
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.
Type annotations and code completion for boto3.client("sagemaker-runtime").invoke_endpoint_async
method.
boto3 documentation
def invoke_endpoint_async(
self,
*,
EndpointName: str,
InputLocation: str,
ContentType: str = ...,
Accept: str = ...,
CustomAttributes: str = ...,
InferenceId: str = ...,
RequestTTLSeconds: int = ...,
InvocationTimeoutSeconds: int = ...,
) -> InvokeEndpointAsyncOutputTypeDef: # (1)
...