/*
 * Copyright 2018-2023 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.
 */
package com.amazonaws.services.sagemaker.model;

import javax.annotation.Generated;

/**
 * <p>
 * The training input mode that the algorithm supports. For more information about input modes, see <a
 * href="https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html">Algorithms</a>.
 * </p>
 * <p>
 * <b>Pipe mode</b>
 * </p>
 * <p>
 * If an algorithm supports <code>Pipe</code> mode, Amazon SageMaker streams data directly from Amazon S3 to the
 * container.
 * </p>
 * <p>
 * <b>File mode</b>
 * </p>
 * <p>
 * If an algorithm supports <code>File</code> mode, SageMaker downloads the training data from S3 to the provisioned ML
 * storage volume, and mounts the directory to the Docker volume for the training container.
 * </p>
 * <p>
 * You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In
 * addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the
 * ML storage volume to also store intermediate information, if any.
 * </p>
 * <p>
 * For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the
 * input data objects sizes are approximately the same. SageMaker does not split the files any further for model
 * training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one
 * host in a training cluster is overloaded, thus becoming a bottleneck in training.
 * </p>
 * <p>
 * <b>FastFile mode</b>
 * </p>
 * <p>
 * If an algorithm supports <code>FastFile</code> mode, SageMaker streams data directly from S3 to the container with no
 * code changes, and provides file system access to the data. Users can author their training script to interact with
 * these files as if they were stored on disk.
 * </p>
 * <p>
 * <code>FastFile</code> mode works best when the data is read sequentially. Augmented manifest files aren't supported.
 * The startup time is lower when there are fewer files in the S3 bucket provided.
 * </p>
 */
@Generated("com.amazonaws:aws-java-sdk-code-generator")
public enum TrainingInputMode {

    Pipe("Pipe"),
    File("File"),
    FastFile("FastFile");

    private String value;

    private TrainingInputMode(String value) {
        this.value = value;
    }

    @Override
    public String toString() {
        return this.value;
    }

    /**
     * Use this in place of valueOf.
     *
     * @param value
     *        real value
     * @return TrainingInputMode corresponding to the value
     *
     * @throws IllegalArgumentException
     *         If the specified value does not map to one of the known values in this enum.
     */
    public static TrainingInputMode fromValue(String value) {
        if (value == null || "".equals(value)) {
            throw new IllegalArgumentException("Value cannot be null or empty!");
        }

        for (TrainingInputMode enumEntry : TrainingInputMode.values()) {
            if (enumEntry.toString().equals(value)) {
                return enumEntry;
            }
        }

        throw new IllegalArgumentException("Cannot create enum from " + value + " value!");
    }
}