/**
 * Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
 * SPDX-License-Identifier: Apache-2.0.
 */

#pragma once
#include <aws/sagemaker/SageMaker_EXPORTS.h>
#include <aws/sagemaker/model/EmrServerlessComputeConfig.h>

#include <utility>

namespace Aws {
namespace Utils {
namespace Json {
class JsonValue;
class JsonView;
}  // namespace Json
}  // namespace Utils
namespace SageMaker {
namespace Model {

/**
 * <p> <p>This data type is intended for use exclusively by SageMaker Canvas
 * and cannot be used in other contexts at the moment.</p>  <p>Specifies the
 * compute configuration for an AutoML job V2.</p></p><p><h3>See Also:</h3>   <a
 * href="http://docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/AutoMLComputeConfig">AWS
 * API Reference</a></p>
 */
class AutoMLComputeConfig {
 public:
  AWS_SAGEMAKER_API AutoMLComputeConfig() = default;
  AWS_SAGEMAKER_API AutoMLComputeConfig(Aws::Utils::Json::JsonView jsonValue);
  AWS_SAGEMAKER_API AutoMLComputeConfig& operator=(Aws::Utils::Json::JsonView jsonValue);
  AWS_SAGEMAKER_API Aws::Utils::Json::JsonValue Jsonize() const;

  ///@{
  /**
   * <p>The configuration for using <a
   * href="https://docs.aws.amazon.com/emr/latest/EMR-Serverless-UserGuide/emr-serverless.html">
   * EMR Serverless</a> to run the AutoML job V2.</p> <p>To allow your AutoML job V2
   * to automatically initiate a remote job on EMR Serverless when additional compute
   * resources are needed to process large datasets, you need to provide an
   * <code>EmrServerlessComputeConfig</code> object, which includes an
   * <code>ExecutionRoleARN</code> attribute, to the <code>AutoMLComputeConfig</code>
   * of the AutoML job V2 input request.</p> <p>By seamlessly transitioning to EMR
   * Serverless when required, the AutoML job can handle datasets that would
   * otherwise exceed the initially provisioned resources, without any manual
   * intervention from you. </p> <p>EMR Serverless is available for the tabular and
   * time series problem types. We recommend setting up this option for tabular
   * datasets larger than 5 GB and time series datasets larger than 30 GB.</p>
   */
  inline const EmrServerlessComputeConfig& GetEmrServerlessComputeConfig() const { return m_emrServerlessComputeConfig; }
  inline bool EmrServerlessComputeConfigHasBeenSet() const { return m_emrServerlessComputeConfigHasBeenSet; }
  template <typename EmrServerlessComputeConfigT = EmrServerlessComputeConfig>
  void SetEmrServerlessComputeConfig(EmrServerlessComputeConfigT&& value) {
    m_emrServerlessComputeConfigHasBeenSet = true;
    m_emrServerlessComputeConfig = std::forward<EmrServerlessComputeConfigT>(value);
  }
  template <typename EmrServerlessComputeConfigT = EmrServerlessComputeConfig>
  AutoMLComputeConfig& WithEmrServerlessComputeConfig(EmrServerlessComputeConfigT&& value) {
    SetEmrServerlessComputeConfig(std::forward<EmrServerlessComputeConfigT>(value));
    return *this;
  }
  ///@}
 private:
  EmrServerlessComputeConfig m_emrServerlessComputeConfig;
  bool m_emrServerlessComputeConfigHasBeenSet = false;
};

}  // namespace Model
}  // namespace SageMaker
}  // namespace Aws
