com.amazonaws.services.sagemaker.model.TrainingInputMode Maven / Gradle / Ivy
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/*
* Copyright 2019-2024 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;
/**
*
* The training input mode that the algorithm supports. For more information about input modes, see Algorithms.
*
*
* Pipe mode
*
*
* If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the
* container.
*
*
* File mode
*
*
* If an algorithm supports File 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.
*
*
* 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.
*
*
* 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.
*
*
* FastFile mode
*
*
* If an algorithm supports FastFile 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.
*
*
* FastFile 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.
*
*/
@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!");
}
}