@Generated(value="software.amazon.awssdk:codegen") public final class CreateTrainingJobRequest extends SageMakerRequest implements ToCopyableBuilder<CreateTrainingJobRequest.Builder,CreateTrainingJobRequest>
| Modifier and Type | Class and Description |
|---|---|
static interface |
CreateTrainingJobRequest.Builder |
| Modifier and Type | Method and Description |
|---|---|
AlgorithmSpecification |
algorithmSpecification()
The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata,
including the input mode.
|
static CreateTrainingJobRequest.Builder |
builder() |
CheckpointConfig |
checkpointConfig()
Contains information about the output location for managed spot training checkpoint data.
|
Boolean |
enableInterContainerTrafficEncryption()
To encrypt all communications between ML compute instances in distributed training, choose
True. |
Boolean |
enableManagedSpotTraining()
To train models using managed spot training, choose
True. |
Boolean |
enableNetworkIsolation()
Isolates the training container.
|
boolean |
equals(Object obj) |
boolean |
equalsBySdkFields(Object obj) |
<T> Optional<T> |
getValueForField(String fieldName,
Class<T> clazz) |
int |
hashCode() |
Map<String,String> |
hyperParameters()
Algorithm-specific parameters that influence the quality of the model.
|
List<Channel> |
inputDataConfig()
An array of
Channel objects. |
OutputDataConfig |
outputDataConfig()
Specifies the path to the S3 location where you want to store model artifacts.
|
ResourceConfig |
resourceConfig()
The resources, including the ML compute instances and ML storage volumes, to use for model training.
|
String |
roleArn()
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
|
List<SdkField<?>> |
sdkFields() |
static Class<? extends CreateTrainingJobRequest.Builder> |
serializableBuilderClass() |
StoppingCondition |
stoppingCondition()
Specifies a limit to how long a model training job can run.
|
List<Tag> |
tags()
An array of key-value pairs.
|
CreateTrainingJobRequest.Builder |
toBuilder() |
String |
toString()
Returns a string representation of this object.
|
String |
trainingJobName()
The name of the training job.
|
VpcConfig |
vpcConfig()
A VpcConfig object that specifies the VPC that you want your training job to connect to.
|
overrideConfigurationclone, finalize, getClass, notify, notifyAll, wait, wait, waitcopypublic String trainingJobName()
The name of the training job. The name must be unique within an AWS Region in an AWS account.
public Map<String,String> hyperParameters()
Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.
You can specify a maximum of 100 hyperparameters. Each hyperparameter is a key-value pair. Each key and value is
limited to 256 characters, as specified by the Length Constraint.
Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.
You can specify a maximum of 100 hyperparameters. Each hyperparameter is a key-value pair. Each key and
value is limited to 256 characters, as specified by the Length Constraint.
public AlgorithmSpecification algorithmSpecification()
The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata, including the input mode. For more information about algorithms provided by Amazon SageMaker, see Algorithms. For information about providing your own algorithms, see Using Your Own Algorithms with Amazon SageMaker.
public String roleArn()
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
During model training, Amazon SageMaker needs your permission to read input data from an S3 bucket, download a Docker image that contains training code, write model artifacts to an S3 bucket, write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch. You grant permissions for all of these tasks to an IAM role. For more information, see Amazon SageMaker Roles.
To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole
permission.
During model training, Amazon SageMaker needs your permission to read input data from an S3 bucket, download a Docker image that contains training code, write model artifacts to an S3 bucket, write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch. You grant permissions for all of these tasks to an IAM role. For more information, see Amazon SageMaker Roles.
To be able to pass this role to Amazon SageMaker, the caller of this API must have the
iam:PassRole permission.
public List<Channel> inputDataConfig()
An array of Channel objects. Each channel is a named input source. InputDataConfig
describes the input data and its location.
Algorithms can accept input data from one or more channels. For example, an algorithm might have two channels of
input data, training_data and validation_data. The configuration for each channel
provides the S3, EFS, or FSx location where the input data is stored. It also provides information about the
stored data: the MIME type, compression method, and whether the data is wrapped in RecordIO format.
Depending on the input mode that the algorithm supports, Amazon SageMaker either copies input data files from an S3 bucket to a local directory in the Docker container, or makes it available as input streams. For example, if you specify an EFS location, input data files will be made available as input streams. They do not need to be downloaded.
Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.
Channel objects. Each channel is a named input source.
InputDataConfig describes the input data and its location.
Algorithms can accept input data from one or more channels. For example, an algorithm might have two
channels of input data, training_data and validation_data. The configuration
for each channel provides the S3, EFS, or FSx location where the input data is stored. It also provides
information about the stored data: the MIME type, compression method, and whether the data is wrapped in
RecordIO format.
Depending on the input mode that the algorithm supports, Amazon SageMaker either copies input data files from an S3 bucket to a local directory in the Docker container, or makes it available as input streams. For example, if you specify an EFS location, input data files will be made available as input streams. They do not need to be downloaded.
public OutputDataConfig outputDataConfig()
Specifies the path to the S3 location where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.
public ResourceConfig resourceConfig()
The resources, including the ML compute instances and ML storage volumes, to use for model training.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use ML storage
volumes for scratch space. If you want Amazon SageMaker to use the ML storage volume to store the training data,
choose File as the TrainingInputMode in the algorithm specification. For distributed
training algorithms, specify an instance count greater than 1.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use ML
storage volumes for scratch space. If you want Amazon SageMaker to use the ML storage volume to store the
training data, choose File as the TrainingInputMode in the algorithm
specification. For distributed training algorithms, specify an instance count greater than 1.
public VpcConfig vpcConfig()
A VpcConfig object that specifies the VPC that you want your training job to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.
public StoppingCondition stoppingCondition()
Specifies a limit to how long a model training job can run. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, Amazon 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, so the results of
training are not lost.
To stop a job, Amazon 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, so
the results of training are not lost.
public List<Tag> tags()
An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.
Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.
public Boolean enableNetworkIsolation()
Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If you enable network isolation for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
The Semantic Segmentation built-in algorithm does not support network isolation.
The Semantic Segmentation built-in algorithm does not support network isolation.
public Boolean enableInterContainerTrafficEncryption()
To encrypt all communications between ML compute instances in distributed training, choose True.
Encryption provides greater security for distributed training, but training might take longer. How long it takes
depends on the amount of communication between compute instances, especially if you use a deep learning algorithm
in distributed training. For more information, see Protect Communications Between ML
Compute Instances in a Distributed Training Job.
True. Encryption provides greater security for distributed training, but training might take
longer. How long it takes depends on the amount of communication between compute instances, especially if
you use a deep learning algorithm in distributed training. For more information, see Protect Communications Between
ML Compute Instances in a Distributed Training Job.public Boolean enableManagedSpotTraining()
To train models using managed spot training, choose True. Managed spot training provides a fully
managed and scalable infrastructure for training machine learning models. this option is useful when training
jobs can be interrupted and when there is flexibility when the training job is run.
The complete and intermediate results of jobs are stored in an Amazon S3 bucket, and can be used as a starting point to train models incrementally. Amazon SageMaker provides metrics and logs in CloudWatch. They can be used to see when managed spot training jobs are running, interrupted, resumed, or completed.
True. Managed spot training provides a
fully managed and scalable infrastructure for training machine learning models. this option is useful
when training jobs can be interrupted and when there is flexibility when the training job is run.
The complete and intermediate results of jobs are stored in an Amazon S3 bucket, and can be used as a starting point to train models incrementally. Amazon SageMaker provides metrics and logs in CloudWatch. They can be used to see when managed spot training jobs are running, interrupted, resumed, or completed.
public CheckpointConfig checkpointConfig()
Contains information about the output location for managed spot training checkpoint data.
public CreateTrainingJobRequest.Builder toBuilder()
toBuilder in interface ToCopyableBuilder<CreateTrainingJobRequest.Builder,CreateTrainingJobRequest>toBuilder in class SageMakerRequestpublic static CreateTrainingJobRequest.Builder builder()
public static Class<? extends CreateTrainingJobRequest.Builder> serializableBuilderClass()
public int hashCode()
hashCode in class AwsRequestpublic boolean equals(Object obj)
equals in class AwsRequestpublic boolean equalsBySdkFields(Object obj)
equalsBySdkFields in interface SdkPojopublic String toString()
public <T> Optional<T> getValueForField(String fieldName, Class<T> clazz)
getValueForField in class SdkRequestCopyright © 2019. All rights reserved.