@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.
|
DebugHookConfig |
debugHookConfig()
Returns the value of the DebugHookConfig property for this object.
|
List<DebugRuleConfiguration> |
debugRuleConfigurations()
Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.
|
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.
|
Map<String,String> |
environment()
The environment variables to set in the Docker container.
|
boolean |
equals(Object obj) |
boolean |
equalsBySdkFields(Object obj) |
ExperimentConfig |
experimentConfig()
Returns the value of the ExperimentConfig property for this object.
|
<T> Optional<T> |
getValueForField(String fieldName,
Class<T> clazz) |
boolean |
hasDebugRuleConfigurations()
For responses, this returns true if the service returned a value for the DebugRuleConfigurations property.
|
boolean |
hasEnvironment()
For responses, this returns true if the service returned a value for the Environment property.
|
int |
hashCode() |
boolean |
hasHyperParameters()
For responses, this returns true if the service returned a value for the HyperParameters property.
|
boolean |
hasInputDataConfig()
For responses, this returns true if the service returned a value for the InputDataConfig property.
|
boolean |
hasProfilerRuleConfigurations()
For responses, this returns true if the service returned a value for the ProfilerRuleConfigurations property.
|
boolean |
hasTags()
For responses, this returns true if the service returned a value for the Tags property.
|
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.
|
ProfilerConfig |
profilerConfig()
Returns the value of the ProfilerConfig property for this object.
|
List<ProfilerRuleConfiguration> |
profilerRuleConfigurations()
Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.
|
ResourceConfig |
resourceConfig()
The resources, including the ML compute instances and ML storage volumes, to use for model training.
|
RetryStrategy |
retryStrategy()
The number of times to retry the job when the job fails due to an
InternalServerError. |
String |
roleArn()
The Amazon Resource Name (ARN) of an IAM role that 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.
|
TensorBoardOutputConfig |
tensorBoardOutputConfig()
Returns the value of the TensorBoardOutputConfig property for this object.
|
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 final String trainingJobName()
The name of the training job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.
public final boolean hasHyperParameters()
isEmpty() method on the property).
This is useful because the SDK will never return a null collection or map, but you may need to differentiate
between the service returning nothing (or null) and the service returning an empty collection or map. For
requests, this returns true if a value for the property was specified in the request builder, and false if a
value was not specified.public final 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 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.
Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.
Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.
This method will never return null. If you would like to know whether the service returned this field (so that
you can differentiate between null and empty), you can use the hasHyperParameters() method.
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.
Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.
public final 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 SageMaker, see Algorithms. For information about providing your own algorithms, see Using Your Own Algorithms with Amazon SageMaker.
public final String roleArn()
The Amazon Resource Name (ARN) of an IAM role that SageMaker can assume to perform tasks on your behalf.
During model training, 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 SageMaker Roles.
To be able to pass this role to SageMaker, the caller of this API must have the iam:PassRole
permission.
During model training, 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 SageMaker Roles.
To be able to pass this role to SageMaker, the caller of this API must have the iam:PassRole
permission.
public final boolean hasInputDataConfig()
isEmpty() method on the property).
This is useful because the SDK will never return a null collection or map, but you may need to differentiate
between the service returning nothing (or null) and the service returning an empty collection or map. For
requests, this returns true if a value for the property was specified in the request builder, and false if a
value was not specified.public final 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, 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 are 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.
This method will never return null. If you would like to know whether the service returned this field (so that
you can differentiate between null and empty), you can use the hasInputDataConfig() method.
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, 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 are available as input streams. They do not need to be downloaded.
public final OutputDataConfig outputDataConfig()
Specifies the path to the S3 location where you want to store model artifacts. SageMaker creates subfolders for the artifacts.
public final 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 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 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 final 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 final StoppingCondition stoppingCondition()
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.
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, so the results of training
are not lost.
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, so
the results of training are not lost.
public final boolean hasTags()
isEmpty() method on the property). This is useful
because the SDK will never return a null collection or map, but you may need to differentiate between the service
returning nothing (or null) and the service returning an empty collection or map. For requests, this returns true
if a value for the property was specified in the request builder, and false if a value was not specified.public final List<Tag> tags()
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.
This method will never return null. If you would like to know whether the service returned this field (so that
you can differentiate between null and empty), you can use the hasTags() method.
public final 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, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
public final 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 final 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 final CheckpointConfig checkpointConfig()
Contains information about the output location for managed spot training checkpoint data.
public final DebugHookConfig debugHookConfig()
public final boolean hasDebugRuleConfigurations()
isEmpty() method on the
property). This is useful because the SDK will never return a null collection or map, but you may need to
differentiate between the service returning nothing (or null) and the service returning an empty collection or
map. For requests, this returns true if a value for the property was specified in the request builder, and false
if a value was not specified.public final List<DebugRuleConfiguration> debugRuleConfigurations()
Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.
Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.
This method will never return null. If you would like to know whether the service returned this field (so that
you can differentiate between null and empty), you can use the hasDebugRuleConfigurations() method.
public final TensorBoardOutputConfig tensorBoardOutputConfig()
public final ExperimentConfig experimentConfig()
public final ProfilerConfig profilerConfig()
public final boolean hasProfilerRuleConfigurations()
isEmpty() method on the
property). This is useful because the SDK will never return a null collection or map, but you may need to
differentiate between the service returning nothing (or null) and the service returning an empty collection or
map. For requests, this returns true if a value for the property was specified in the request builder, and false
if a value was not specified.public final List<ProfilerRuleConfiguration> profilerRuleConfigurations()
Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.
Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.
This method will never return null. If you would like to know whether the service returned this field (so that
you can differentiate between null and empty), you can use the hasProfilerRuleConfigurations() method.
public final boolean hasEnvironment()
isEmpty() method on the property).
This is useful because the SDK will never return a null collection or map, but you may need to differentiate
between the service returning nothing (or null) and the service returning an empty collection or map. For
requests, this returns true if a value for the property was specified in the request builder, and false if a
value was not specified.public final Map<String,String> environment()
The environment variables to set in the Docker container.
Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.
This method will never return null. If you would like to know whether the service returned this field (so that
you can differentiate between null and empty), you can use the hasEnvironment() method.
public final RetryStrategy retryStrategy()
The number of times to retry the job when the job fails due to an InternalServerError.
InternalServerError.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 final int hashCode()
hashCode in class AwsRequestpublic final boolean equals(Object obj)
equals in class AwsRequestpublic final boolean equalsBySdkFields(Object obj)
equalsBySdkFields in interface SdkPojopublic final String toString()
public final <T> Optional<T> getValueForField(String fieldName, Class<T> clazz)
getValueForField in class SdkRequestCopyright © 2023. All rights reserved.