@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() |
Boolean |
enableNetworkIsolation()
Isolates the training container.
|
boolean |
equals(Object obj) |
<T> Optional<T> |
getValueForField(String fieldName,
Class<T> clazz)
Used to retrieve the value of a field from any class that extends
SdkRequest. |
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 bucket 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 assumes to perform tasks on your behalf.
|
List<SdkField<?>> |
sdkFields() |
static Class<? extends CreateTrainingJobRequest.Builder> |
serializableBuilderClass() |
StoppingCondition |
stoppingCondition()
Sets a duration for training.
|
List<Tag> |
tags()
An array of key-value pairs.
|
CreateTrainingJobRequest.Builder |
toBuilder()
Take this object and create a builder that contains all of the current property values of this object.
|
String |
toString() |
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.
|
overrideConfigurationcopypublic 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 assumes 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 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.
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 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.
public OutputDataConfig outputDataConfig()
Specifies the path to the S3 bucket 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()
Sets a duration for training. Use this parameter 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
might use this 120-second window to save the model artifacts.
When Amazon SageMaker terminates a job because the stopping condition has been met, training algorithms provided
by Amazon SageMaker save the intermediate results of the job. This intermediate data is a valid model artifact.
You can use it to create a model using the CreateModel API.
SIGTERM signal, which delays job termination for 120
seconds. Algorithms might use this 120-second window to save the model artifacts.
When Amazon SageMaker terminates a job because the stopping condition has been met, training algorithms
provided by Amazon SageMaker save the intermediate results of the job. This intermediate data is a valid
model artifact. You can use it to create a model using the CreateModel API.
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 network isolation is used for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specifed 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 CreateTrainingJobRequest.Builder toBuilder()
ToCopyableBuildertoBuilder in interface ToCopyableBuilder<CreateTrainingJobRequest.Builder,CreateTrainingJobRequest>toBuilder in class SageMakerRequestpublic static CreateTrainingJobRequest.Builder builder()
public static Class<? extends CreateTrainingJobRequest.Builder> serializableBuilderClass()
public <T> Optional<T> getValueForField(String fieldName, Class<T> clazz)
SdkRequestSdkRequest. The field name
specified should match the member name from the corresponding service-2.json model specified in the
codegen-resources folder for a given service. The class specifies what class to cast the returned value to.
If the returned value is also a modeled class, the SdkRequest.getValueForField(String, Class) method will
again be available.getValueForField in class SdkRequestfieldName - The name of the member to be retrieved.clazz - The class to cast the returned object to.Copyright © 2017 Amazon Web Services, Inc. All Rights Reserved.