fn create_model(
        &self,
        input: CreateModelInput,
    ) -> RusotoFuture<CreateModelOutput, CreateModelError> {
        let mut request = SignedRequest::new("POST", "sagemaker", &self.region, "/");

        request.set_content_type("application/x-amz-json-1.1".to_owned());
        request.add_header("x-amz-target", "SageMaker.CreateModel");
        let encoded = serde_json::to_string(&input).unwrap();
        request.set_payload(Some(encoded.into_bytes()));

        self.client.sign_and_dispatch(request, |response| {
            if response.status.is_success() {
                Box::new(response.buffer().from_err().map(|response| {
                    let mut body = response.body;

                    if body.is_empty() || body == b"null" {
                        body = b"{}".to_vec();
                    }

                    serde_json::from_str::<CreateModelOutput>(
                        String::from_utf8_lossy(body.as_ref()).as_ref(),
                    )
                    .unwrap()
                }))
            } else {
                Box::new(
                    response
                        .buffer()
                        .from_err()
                        .and_then(|response| Err(CreateModelError::from_response(response))),
                )
            }
        })
    }
/// <p>Creates a model in Amazon SageMaker. In the request, you name the model and describe one or more containers. For each container, you specify the docker image containing inference code, artifacts (from prior training), and custom environment map that the inference code uses when you deploy the model into production. </p> <p>Use this API to create a model only if you want to use Amazon SageMaker hosting services. To host your model, you create an endpoint configuration with the <code>CreateEndpointConfig</code> API, and then create an endpoint with the <code>CreateEndpoint</code> API. </p> <p>Amazon SageMaker then deploys all of the containers that you defined for the model in the hosting environment. </p> <p>In the <code>CreateModel</code> request, you must define a container with the <code>PrimaryContainer</code> parameter. </p> <p>In the request, you also provide an IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other AWS resources, you grant necessary permissions via this role.</p>
Creates a model in Amazon SageMaker. In the request, you name the model and describe one or more containers. For each container, you specify the docker image containing inference code, artifacts (from prior training), and custom environment map that the inference code uses when you deploy the model into production.  Use this API to create a model only if you want to use Amazon SageMaker hosting services. To host your model, you create an endpoint configuration with the CreateEndpointConfig API, and then create an endpoint with the CreateEndpoint API.  Amazon SageMaker then deploys all of the containers that you defined for the model in the hosting environment.  In the CreateModel request, you must define a container with the PrimaryContainer parameter.  In the request, you also provide an IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other AWS resources, you grant necessary permissions via this role.
Creates a model in Amazon SageMaker . In the request you name the model and describe one or more containers . For each container you specify the docker image containing inference code artifacts ( from prior training ) and custom environment map that the inference code uses when you deploy the model into production . Use this API to create a model only if you want to use Amazon SageMaker hosting services . To host your model you create an endpoint configuration with the CreateEndpointConfig API and then create an endpoint with the CreateEndpoint API . Amazon SageMaker then deploys all of the containers that you defined for the model in the hosting environment . In the CreateModel request you must define a container with the PrimaryContainer parameter . In the request you also provide an IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances . In addition you also use the IAM role to manage permissions the inference code needs . For example if the inference code access any other AWS resources you grant necessary permissions via this role .
fn create_model(
        &self,
        input: CreateModelInput,
    ) -> RusotoFuture<CreateModelOutput, CreateModelError> {
        let mut request = SignedRequest::new("POST", "sagemaker", &self.region, "/");
        request.set_content_type("application/x-amz-json-1.1".to_owned());
        request.add_header("x-amz-target", "SageMaker.CreateModel");
        let encoded = serde_json::to_string(&input).unwrap();
        request.set_payload(Some(encoded.into_bytes()));
        self.client.sign_and_dispatch(request, |response| {
            if response.status.is_success() {
                Box::new(response.buffer().from_err().map(|response| {
                    let mut body = response.body;
                    if body.is_empty() || body == b"null" {
                        body = b"{}".to_vec();
                    }
                    serde_json::from_str::<CreateModelOutput>(
                        String::from_utf8_lossy(body.as_ref()).as_ref(),
                    )
                    .unwrap()
                }))
            } else {
                Box::new(
                    response
                        .buffer()
                        .from_err()
                        .and_then(|response| Err(CreateModelError::from_response(response))),
                )
            }
        })
    }
fn create_model ( & self , input : CreateModelInput , ) -> RusotoFuture < CreateModelOutput , CreateModelError > { let mut request = SignedRequest :: new ( " " , " " , & self . region , " " ) ; request . set_content_type ( " " . to_owned ( ) ) ; request . add_header ( " " , " " ) ; let encoded = serde_json :: to_string ( & input ) . unwrap ( ) ; request . set_payload ( Some ( encoded . into_bytes ( ) ) ) ; self . client . sign_and_dispatch ( request , | response | { if response . status . is_success ( ) { Box :: new ( response . buffer ( ) . from_err ( ) . map ( | response | { let mut body = response . body ; if body . is_empty ( ) || body == b" " { body = b" " . to_vec ( ) ; } serde_json :: from_str :: < CreateModelOutput > ( String :: from_utf8_lossy ( body . as_ref ( ) ) . as_ref ( ) , ) . unwrap ( ) } ) ) } else { Box :: new ( response . buffer ( ) . from_err ( ) . and_then ( | response | Err ( CreateModelError :: from_response ( response ) ) ) , ) } } ) }
<p>Creates a model in Amazon SageMaker.
<p > Creates a model in Amazon SageMaker .
&self
input
CreateModelInput
&self
input
CreateModelInput