/*
 * Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
 * 
 * Licensed under the Apache License, Version 2.0 (the "License").
 * You may not use this file except in compliance with the License.
 * A copy of the License is located at
 * 
 *  http://aws.amazon.com/apache2.0
 * 
 * or in the "license" file accompanying this file. This file is distributed
 * on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
 * express or implied. See the License for the specific language governing
 * permissions and limitations under the License.
 */

/*
 * Do not modify this file. This file is generated from the sagemaker-2017-07-24.normal.json service model.
 */
using System;
using System.Collections.Generic;
using System.Xml.Serialization;
using System.Text;
using System.IO;
using System.Net;

using Amazon.Runtime;
using Amazon.Runtime.Internal;

namespace Amazon.SageMaker.Model
{
    /// <summary>
    /// Defines the input needed to run a training job using the algorithm.
    /// </summary>
    public partial class TrainingJobDefinition
    {
        private Dictionary<string, string> _hyperParameters = new Dictionary<string, string>();
        private List<Channel> _inputDataConfig = new List<Channel>();
        private OutputDataConfig _outputDataConfig;
        private ResourceConfig _resourceConfig;
        private StoppingCondition _stoppingCondition;
        private TrainingInputMode _trainingInputMode;

        /// <summary>
        /// Gets and sets the property HyperParameters. 
        /// <para>
        /// The hyperparameters used for the training job.
        /// </para>
        /// </summary>
        [AWSProperty(Min=0, Max=100)]
        public Dictionary<string, string> HyperParameters
        {
            get { return this._hyperParameters; }
            set { this._hyperParameters = value; }
        }

        // Check to see if HyperParameters property is set
        internal bool IsSetHyperParameters()
        {
            return this._hyperParameters != null && this._hyperParameters.Count > 0; 
        }

        /// <summary>
        /// Gets and sets the property InputDataConfig. 
        /// <para>
        /// An array of <code>Channel</code> objects, each of which specifies an input source.
        /// </para>
        /// </summary>
        [AWSProperty(Required=true, Min=1, Max=20)]
        public List<Channel> InputDataConfig
        {
            get { return this._inputDataConfig; }
            set { this._inputDataConfig = value; }
        }

        // Check to see if InputDataConfig property is set
        internal bool IsSetInputDataConfig()
        {
            return this._inputDataConfig != null && this._inputDataConfig.Count > 0; 
        }

        /// <summary>
        /// Gets and sets the property OutputDataConfig. 
        /// <para>
        /// the path to the S3 bucket where you want to store model artifacts. SageMaker creates
        /// subfolders for the artifacts.
        /// </para>
        /// </summary>
        [AWSProperty(Required=true)]
        public OutputDataConfig OutputDataConfig
        {
            get { return this._outputDataConfig; }
            set { this._outputDataConfig = value; }
        }

        // Check to see if OutputDataConfig property is set
        internal bool IsSetOutputDataConfig()
        {
            return this._outputDataConfig != null;
        }

        /// <summary>
        /// Gets and sets the property ResourceConfig. 
        /// <para>
        /// The resources, including the ML compute instances and ML storage volumes, to use for
        /// model training.
        /// </para>
        /// </summary>
        [AWSProperty(Required=true)]
        public ResourceConfig ResourceConfig
        {
            get { return this._resourceConfig; }
            set { this._resourceConfig = value; }
        }

        // Check to see if ResourceConfig property is set
        internal bool IsSetResourceConfig()
        {
            return this._resourceConfig != null;
        }

        /// <summary>
        /// Gets and sets the property StoppingCondition. 
        /// <para>
        /// Specifies a limit to how long a model training job can run. It also specifies how
        /// long a managed Spot training job has to complete. When the job reaches the time limit,
        /// SageMaker ends the training job. Use this API to cap model training costs.
        /// </para>
        ///  
        /// <para>
        /// To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job
        /// termination for 120 seconds. Algorithms can use this 120-second window to save the
        /// model artifacts.
        /// </para>
        /// </summary>
        [AWSProperty(Required=true)]
        public StoppingCondition StoppingCondition
        {
            get { return this._stoppingCondition; }
            set { this._stoppingCondition = value; }
        }

        // Check to see if StoppingCondition property is set
        internal bool IsSetStoppingCondition()
        {
            return this._stoppingCondition != null;
        }

        /// <summary>
        /// Gets and sets the property TrainingInputMode.
        /// </summary>
        [AWSProperty(Required=true)]
        public TrainingInputMode TrainingInputMode
        {
            get { return this._trainingInputMode; }
            set { this._trainingInputMode = value; }
        }

        // Check to see if TrainingInputMode property is set
        internal bool IsSetTrainingInputMode()
        {
            return this._trainingInputMode != null;
        }

    }
}