Module Awso_sagemaker_runtime.ValuesSource

Sourceval service : Awso.Service.t
Sourceval apiVersion : string
Sourceval endpointPrefix : string
Sourceval serviceFullName : string
Sourceval signatureVersion : string
Sourceval protocol : string
Sourceval globalEndpoint : string
Sourceval simple_to_json : ('a -> Awso__Botodata.value) -> 'a -> Yojson.Safe.t
Sourceval composed_to_json : ('a -> Awso__Botodata.value) -> 'a -> Yojson.Safe.t
Sourceval to_query : ('a -> Awso.Client.Query.value) -> 'a -> Awso.Client.Query.t
Sourceval structure_to_value_aux : ('a * 'b option) list -> f:(('a * 'b) list -> 'c) -> [> `Structure of 'c ]
Sourceval structure_to_value : ('a * 'b option) list -> [> `Structure of ('a * 'b) list ]
Sourceval structure_to_wrapped_value : wrapper:'a -> response:'a -> ('b * 'c option) list -> [> `Structure of ('a * [> `Structure of ('b * 'c) list ]) list ]
Sourcemodule Message : sig ... end
Sourcemodule ErrorCode : sig ... end
Sourcemodule PartBlob : sig ... end
Sourcemodule LogStreamArn : sig ... end
Sourcemodule StatusCode : sig ... end
Sourcemodule InternalStreamFailure : sig ... end

The stream processing failed because of an unknown error, exception or failure. Try your request again.

Sourcemodule ModelStreamError : sig ... end

An error occurred while streaming the response body. This error can have the following error codes: ModelInvocationTimeExceeded The model failed to finish sending the response within the timeout period allowed by Amazon SageMaker AI. StreamBroken The Transmission Control Protocol (TCP) connection between the client and the model was reset or closed.

Sourcemodule PayloadPart : sig ... end

A wrapper for pieces of the payload that's returned in response to a streaming inference request. A streaming inference response consists of one or more payload parts.

Sourcemodule CustomAttributesHeader : sig ... end
Sourcemodule Header : sig ... end
Sourcemodule InternalFailure : sig ... end

An internal failure occurred.

Sourcemodule ModelError : sig ... end

Model (owned by the customer in the container) returned 4xx or 5xx error code.

Sourcemodule ResponseStream : sig ... end

A stream of payload parts. Each part contains a portion of the response for a streaming inference request.

Sourcemodule ServiceUnavailable : sig ... end

The service is unavailable. Try your call again.

Sourcemodule ValidationError : sig ... end

Inspect your request and try again.

Sourcemodule BodyBlob : sig ... end
Sourcemodule EndpointName : sig ... end
Sourcemodule InferenceComponentHeader : sig ... end
Sourcemodule InferenceId : sig ... end
Sourcemodule SessionIdHeader : sig ... end
Sourcemodule TargetVariantHeader : sig ... end

Your request caused an exception with an internal dependency. Contact customer support.

Sourcemodule ModelNotReadyException : sig ... end

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.

Sourcemodule NewSessionResponseHeader : sig ... end
Sourcemodule EnableExplanationsHeader : sig ... end
Sourcemodule TargetModelHeader : sig ... end
Sourcemodule FilenameHeader : sig ... end
Sourcemodule InputLocationHeader : sig ... end
Sourcemodule RequestTTLSecondsHeader : sig ... end

Invokes a model at the specified endpoint to return the inference response as a stream. The inference stream provides the response payload incrementally as a series of parts. Before you can get an inference stream, you must have access to a model that's deployed using Amazon SageMaker AI hosting services, and the container for that model must support inference streaming. For more information that can help you use this API, see the following sections in the Amazon SageMaker AI Developer Guide: For information about how to add streaming support to a model, see How Containers Serve Requests. For information about how to process the streaming response, see Invoke real-time endpoints. Before you can use this operation, your IAM permissions must allow the sagemaker:InvokeEndpoint action. For more information about Amazon SageMaker AI actions for IAM policies, see Actions, resources, and condition keys for Amazon SageMaker AI in the IAM Service Authorization Reference. Amazon SageMaker AI strips all POST headers except those supported by the API. Amazon SageMaker AI might add additional headers. You should not rely on the behavior of headers outside those enumerated in the request syntax. Calls to InvokeEndpointWithResponseStream 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.

Invokes a model at the specified endpoint to return the inference response as a stream. The inference stream provides the response payload incrementally as a series of parts. Before you can get an inference stream, you must have access to a model that's deployed using Amazon SageMaker AI hosting services, and the container for that model must support inference streaming. For more information that can help you use this API, see the following sections in the Amazon SageMaker AI Developer Guide: For information about how to add streaming support to a model, see How Containers Serve Requests. For information about how to process the streaming response, see Invoke real-time endpoints. Before you can use this operation, your IAM permissions must allow the sagemaker:InvokeEndpoint action. For more information about Amazon SageMaker AI actions for IAM policies, see Actions, resources, and condition keys for Amazon SageMaker AI in the IAM Service Authorization Reference. Amazon SageMaker AI strips all POST headers except those supported by the API. Amazon SageMaker AI might add additional headers. You should not rely on the behavior of headers outside those enumerated in the request syntax. Calls to InvokeEndpointWithResponseStream 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.

Sourcemodule InvokeEndpointOutput : sig ... end

After you deploy a model into production using Amazon SageMaker AI 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 AI, see How It Works. Amazon SageMaker AI strips all POST headers except those supported by the API. Amazon SageMaker AI 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. Endpoints are scoped to an individual account, and are not public. The URL does not contain the account ID, but Amazon SageMaker AI determines the account ID from the authentication token that is supplied by the caller.

Sourcemodule InvokeEndpointInput : sig ... end

After you deploy a model into production using Amazon SageMaker AI 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 AI, see How It Works. Amazon SageMaker AI strips all POST headers except those supported by the API. Amazon SageMaker AI 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. Endpoints are scoped to an individual account, and are not public. The URL does not contain the account ID, but Amazon SageMaker AI determines the account ID from the authentication token that is supplied by the caller.

Sourcemodule InvokeEndpointAsyncOutput : sig ... end

After you deploy a model into production using Amazon SageMaker AI 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 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 AI strips all POST headers except those supported by the API. Amazon SageMaker AI 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.

Sourcemodule InvokeEndpointAsyncInput : sig ... end

After you deploy a model into production using Amazon SageMaker AI 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 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 AI strips all POST headers except those supported by the API. Amazon SageMaker AI 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.