/// <summary>
/// The realtime prediction endpoint for the given MLModel.
/// </summary>
public async Task<string> GetEndpointAsync()
{
if (null == this.endpoint)
{
GetMLModelRequest request = new GetMLModelRequest { MLModelId = ModelId };
this.endpoint = (await client.GetMLModelAsync(request).ConfigureAwait(false)).EndpointInfo.EndpointUrl;
}
return this.endpoint;
}
public object Execute(ExecutorContext context)
{
var cmdletContext = context as CmdletContext;
// create request
var request = new Amazon.MachineLearning.Model.GetMLModelRequest();
if (cmdletContext.MLModelId != null)
{
request.MLModelId = cmdletContext.MLModelId;
}
if (cmdletContext.VerboseResponse != null)
{
request.Verbose = cmdletContext.VerboseResponse.Value;
}
CmdletOutput output;
// issue call
var client = Client ?? CreateClient(_CurrentCredentials, _RegionEndpoint);
try
{
var response = CallAWSServiceOperation(client, request);
object pipelineOutput = null;
pipelineOutput = cmdletContext.Select(response, this);
output = new CmdletOutput
{
PipelineOutput = pipelineOutput,
ServiceResponse = response
};
}
catch (Exception e)
{
output = new CmdletOutput {
ErrorResponse = e
};
}
return(output);
}
/// <summary>
/// Initiates the asynchronous execution of the GetMLModel operation.
/// </summary>
///
/// <param name="request">Container for the necessary parameters to execute the GetMLModel operation.</param>
/// <param name="cancellationToken">
/// A cancellation token that can be used by other objects or threads to receive notice of cancellation.
/// </param>
/// <returns>The task object representing the asynchronous operation.</returns>
public Task<GetMLModelResponse> GetMLModelAsync(GetMLModelRequest request, System.Threading.CancellationToken cancellationToken = default(CancellationToken))
{
var marshaller = new GetMLModelRequestMarshaller();
var unmarshaller = GetMLModelResponseUnmarshaller.Instance;
return InvokeAsync<GetMLModelRequest,GetMLModelResponse>(request, marshaller,
unmarshaller, cancellationToken);
}
/// <summary>
/// Returns an <code>MLModel</code> that includes detailed metadata, and data source information
/// as well as the current status of the <code>MLModel</code>.
///
///
/// <para>
/// <code>GetMLModel</code> provides results in normal or verbose format.
/// </para>
/// </summary>
/// <param name="mlModelId">The ID assigned to the <code>MLModel</code> at creation.</param>
/// <param name="verbose">Specifies whether the <code>GetMLModel</code> operation should return <code>Recipe</code>. If true, <code>Recipe</code> is returned. If false, <code>Recipe</code> is not returned.</param>
/// <param name="cancellationToken">
/// A cancellation token that can be used by other objects or threads to receive notice of cancellation.
/// </param>
///
/// <returns>The response from the GetMLModel service method, as returned by MachineLearning.</returns>
/// <exception cref="Amazon.MachineLearning.Model.InternalServerException">
/// An error on the server occurred when trying to process a request.
/// </exception>
/// <exception cref="Amazon.MachineLearning.Model.InvalidInputException">
/// An error on the client occurred. Typically, the cause is an invalid input value.
/// </exception>
/// <exception cref="Amazon.MachineLearning.Model.ResourceNotFoundException">
/// A specified resource cannot be located.
/// </exception>
public Task<GetMLModelResponse> GetMLModelAsync(string mlModelId, bool verbose, System.Threading.CancellationToken cancellationToken = default(CancellationToken))
{
var request = new GetMLModelRequest();
request.MLModelId = mlModelId;
request.Verbose = verbose;
return GetMLModelAsync(request, cancellationToken);
}
internal GetMLModelResponse GetMLModel(GetMLModelRequest request)
{
var marshaller = new GetMLModelRequestMarshaller();
var unmarshaller = GetMLModelResponseUnmarshaller.Instance;
return Invoke<GetMLModelRequest,GetMLModelResponse>(request, marshaller, unmarshaller);
}
/// <summary>
/// Returns an <code>MLModel</code> that includes detailed metadata, data source information,
/// and the current status of the <code>MLModel</code>.
///
///
/// <para>
/// <code>GetMLModel</code> provides results in normal or verbose format.
/// </para>
/// </summary>
/// <param name="mlModelId">The ID assigned to the <code>MLModel</code> at creation.</param>
/// <param name="verbose">Specifies whether the <code>GetMLModel</code> operation should return <code>Recipe</code>. If true, <code>Recipe</code> is returned. If false, <code>Recipe</code> is not returned.</param>
///
/// <returns>The response from the GetMLModel service method, as returned by MachineLearning.</returns>
/// <exception cref="Amazon.MachineLearning.Model.InternalServerException">
/// An error on the server occurred when trying to process a request.
/// </exception>
/// <exception cref="Amazon.MachineLearning.Model.InvalidInputException">
/// An error on the client occurred. Typically, the cause is an invalid input value.
/// </exception>
/// <exception cref="Amazon.MachineLearning.Model.ResourceNotFoundException">
/// A specified resource cannot be located.
/// </exception>
public GetMLModelResponse GetMLModel(string mlModelId, bool verbose)
{
var request = new GetMLModelRequest();
request.MLModelId = mlModelId;
request.Verbose = verbose;
return GetMLModel(request);
}
/// <summary>
/// Returns an <code>MLModel</code> that includes detailed metadata, data source information,
/// and the current status of the <code>MLModel</code>.
///
///
/// <para>
/// <code>GetMLModel</code> provides results in normal or verbose format.
/// </para>
/// </summary>
/// <param name="mlModelId">The ID assigned to the <code>MLModel</code> at creation.</param>
///
/// <returns>The response from the GetMLModel service method, as returned by MachineLearning.</returns>
/// <exception cref="Amazon.MachineLearning.Model.InternalServerException">
/// An error on the server occurred when trying to process a request.
/// </exception>
/// <exception cref="Amazon.MachineLearning.Model.InvalidInputException">
/// An error on the client occurred. Typically, the cause is an invalid input value.
/// </exception>
/// <exception cref="Amazon.MachineLearning.Model.ResourceNotFoundException">
/// A specified resource cannot be located.
/// </exception>
public GetMLModelResponse GetMLModel(string mlModelId)
{
var request = new GetMLModelRequest();
request.MLModelId = mlModelId;
return GetMLModel(request);
}
/// <summary>
/// Initiates the asynchronous execution of the GetMLModel operation.
/// </summary>
///
/// <param name="request">Container for the necessary parameters to execute the GetMLModel operation on AmazonMachineLearningClient.</param>
/// <param name="callback">An AsyncCallback delegate that is invoked when the operation completes.</param>
/// <param name="state">A user-defined state object that is passed to the callback procedure. Retrieve this object from within the callback
/// procedure using the AsyncState property.</param>
///
/// <returns>An IAsyncResult that can be used to poll or wait for results, or both; this value is also needed when invoking EndGetMLModel
/// operation.</returns>
public IAsyncResult BeginGetMLModel(GetMLModelRequest request, AsyncCallback callback, object state)
{
var marshaller = new GetMLModelRequestMarshaller();
var unmarshaller = GetMLModelResponseUnmarshaller.Instance;
return BeginInvoke<GetMLModelRequest>(request, marshaller, unmarshaller,
callback, state);
}
private Amazon.MachineLearning.Model.GetMLModelResponse CallAWSServiceOperation(IAmazonMachineLearning client, Amazon.MachineLearning.Model.GetMLModelRequest request)
{
Utils.Common.WriteVerboseEndpointMessage(this, client.Config, "Amazon Machine Learning", "GetMLModel");
try
{
#if DESKTOP
return(client.GetMLModel(request));
#elif CORECLR
return(client.GetMLModelAsync(request).GetAwaiter().GetResult());
#else
#error "Unknown build edition"
#endif
}
catch (AmazonServiceException exc)
{
var webException = exc.InnerException as System.Net.WebException;
if (webException != null)
{
throw new Exception(Utils.Common.FormatNameResolutionFailureMessage(client.Config, webException.Message), webException);
}
throw;
}
}
