Client
sagemakerruntime | R Documentation |
Amazon SageMaker Runtime¶
Description¶
The Amazon SageMaker runtime API.
Usage¶
Arguments¶
config
Optional configuration of credentials, endpoint, and/or region.
credentials:
creds:
access_key_id: AWS access key ID
secret_access_key: AWS secret access key
session_token: AWS temporary session token
profile: The name of a profile to use. If not given, then the default profile is used.
anonymous: Set anonymous credentials.
endpoint: The complete URL to use for the constructed client.
region: The AWS Region used in instantiating the client.
close_connection: Immediately close all HTTP connections.
timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.
s3_force_path_style: Set this to
true
to force the request to use path-style addressing, i.e.http://s3.amazonaws.com/BUCKET/KEY
.sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html
credentials
Optional credentials shorthand for the config parameter
creds:
access_key_id: AWS access key ID
secret_access_key: AWS secret access key
session_token: AWS temporary session token
profile: The name of a profile to use. If not given, then the default profile is used.
anonymous: Set anonymous credentials.
endpoint
Optional shorthand for complete URL to use for the constructed client.
region
Optional shorthand for AWS Region used in instantiating the client.
Value¶
A client for the service. You can call the service's operations using
syntax like svc$operation(...)
, where svc
is the name you've
assigned to the client. The available operations are listed in the
Operations section.
Service syntax¶
svc <- sagemakerruntime(
config = list(
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string",
close_connection = "logical",
timeout = "numeric",
s3_force_path_style = "logical",
sts_regional_endpoint = "string"
),
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string"
)
Operations¶
- 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
- 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
- invoke_endpoint_with_response_stream
- Invokes a model at the specified endpoint to return the inference response as a stream