@Generated(value="software.amazon.awssdk:codegen") public final class CreateTrainingJobRequest extends SageMakerRequest implements ToCopyableBuilder<CreateTrainingJobRequest.Builder,CreateTrainingJobRequest>
Modifier and Type | Class and Description |
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static interface |
CreateTrainingJobRequest.Builder |
Modifier and Type | Method and Description |
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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 |
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.
|
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 can assume to perform tasks on your behalf.
|
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.
|
overrideConfiguration
copy
public String trainingJobName()
The name of the training job. The name must be unique within an AWS Region in an AWS account. It appears in the Amazon SageMaker console.
public Map<String,String> hyperParameters()
Algorithm-specific parameters. You set hyperparameters before you start the learning process. Hyperparameters influence the quality of the model. 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 Bring Your Own Algorithms .
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.
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.
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 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 CreateTrainingJobRequest.Builder toBuilder()
ToCopyableBuilder
toBuilder
in interface ToCopyableBuilder<CreateTrainingJobRequest.Builder,CreateTrainingJobRequest>
toBuilder
in class SageMakerRequest
public static CreateTrainingJobRequest.Builder builder()
public static Class<? extends CreateTrainingJobRequest.Builder> serializableBuilderClass()
public <T> Optional<T> getValueForField(String fieldName, Class<T> clazz)
SdkRequest
SdkRequest
. 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 SdkRequest
fieldName
- 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.