@Generated(value="com.amazonaws:aws-java-sdk-code-generator") public class CreateTrainingJobRequest extends AmazonWebServiceRequest implements Serializable, Cloneable
NOOP| Constructor and Description |
|---|
CreateTrainingJobRequest() |
| Modifier and Type | Method and Description |
|---|---|
CreateTrainingJobRequest |
addHyperParametersEntry(String key,
String value) |
CreateTrainingJobRequest |
clearHyperParametersEntries()
Removes all the entries added into HyperParameters.
|
CreateTrainingJobRequest |
clone()
Creates a shallow clone of this object for all fields except the handler context.
|
boolean |
equals(Object obj) |
AlgorithmSpecification |
getAlgorithmSpecification()
The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata,
including the input mode.
|
Map<String,String> |
getHyperParameters()
Algorithm-specific parameters.
|
List<Channel> |
getInputDataConfig()
An array of
Channel objects. |
OutputDataConfig |
getOutputDataConfig()
Specifies the path to the S3 bucket where you want to store model artifacts.
|
ResourceConfig |
getResourceConfig()
The resources, including the ML compute instances and ML storage volumes, to use for model training.
|
String |
getRoleArn()
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
|
StoppingCondition |
getStoppingCondition()
Sets a duration for training.
|
List<Tag> |
getTags()
An array of key-value pairs.
|
String |
getTrainingJobName()
The name of the training job.
|
int |
hashCode() |
void |
setAlgorithmSpecification(AlgorithmSpecification algorithmSpecification)
The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata,
including the input mode.
|
void |
setHyperParameters(Map<String,String> hyperParameters)
Algorithm-specific parameters.
|
void |
setInputDataConfig(Collection<Channel> inputDataConfig)
An array of
Channel objects. |
void |
setOutputDataConfig(OutputDataConfig outputDataConfig)
Specifies the path to the S3 bucket where you want to store model artifacts.
|
void |
setResourceConfig(ResourceConfig resourceConfig)
The resources, including the ML compute instances and ML storage volumes, to use for model training.
|
void |
setRoleArn(String roleArn)
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
|
void |
setStoppingCondition(StoppingCondition stoppingCondition)
Sets a duration for training.
|
void |
setTags(Collection<Tag> tags)
An array of key-value pairs.
|
void |
setTrainingJobName(String trainingJobName)
The name of the training job.
|
String |
toString()
Returns a string representation of this object; useful for testing and debugging.
|
CreateTrainingJobRequest |
withAlgorithmSpecification(AlgorithmSpecification algorithmSpecification)
The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata,
including the input mode.
|
CreateTrainingJobRequest |
withHyperParameters(Map<String,String> hyperParameters)
Algorithm-specific parameters.
|
CreateTrainingJobRequest |
withInputDataConfig(Channel... inputDataConfig)
An array of
Channel objects. |
CreateTrainingJobRequest |
withInputDataConfig(Collection<Channel> inputDataConfig)
An array of
Channel objects. |
CreateTrainingJobRequest |
withOutputDataConfig(OutputDataConfig outputDataConfig)
Specifies the path to the S3 bucket where you want to store model artifacts.
|
CreateTrainingJobRequest |
withResourceConfig(ResourceConfig resourceConfig)
The resources, including the ML compute instances and ML storage volumes, to use for model training.
|
CreateTrainingJobRequest |
withRoleArn(String roleArn)
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
|
CreateTrainingJobRequest |
withStoppingCondition(StoppingCondition stoppingCondition)
Sets a duration for training.
|
CreateTrainingJobRequest |
withTags(Collection<Tag> tags)
An array of key-value pairs.
|
CreateTrainingJobRequest |
withTags(Tag... tags)
An array of key-value pairs.
|
CreateTrainingJobRequest |
withTrainingJobName(String trainingJobName)
The name of the training job.
|
addHandlerContext, getCloneRoot, getCloneSource, getCustomQueryParameters, getCustomRequestHeaders, getGeneralProgressListener, getHandlerContext, getReadLimit, getRequestClientOptions, getRequestCredentials, getRequestCredentialsProvider, getRequestMetricCollector, getSdkClientExecutionTimeout, getSdkRequestTimeout, putCustomQueryParameter, putCustomRequestHeader, setGeneralProgressListener, setRequestCredentials, setRequestCredentialsProvider, setRequestMetricCollector, setSdkClientExecutionTimeout, setSdkRequestTimeout, withGeneralProgressListener, withRequestCredentialsProvider, withRequestMetricCollector, withSdkClientExecutionTimeout, withSdkRequestTimeoutpublic void setTrainingJobName(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.
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 String getTrainingJobName()
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 CreateTrainingJobRequest withTrainingJobName(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.
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> getHyperParameters()
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.
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 void setHyperParameters(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.
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.
public CreateTrainingJobRequest withHyperParameters(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.
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.
public CreateTrainingJobRequest addHyperParametersEntry(String key, String value)
public CreateTrainingJobRequest clearHyperParametersEntries()
public void setAlgorithmSpecification(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 .
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 AlgorithmSpecification getAlgorithmSpecification()
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 CreateTrainingJobRequest withAlgorithmSpecification(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 .
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 void setRoleArn(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.
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.
public String getRoleArn()
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 CreateTrainingJobRequest withRoleArn(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.
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.
public List<Channel> getInputDataConfig()
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.
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 void setInputDataConfig(Collection<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.
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.
public CreateTrainingJobRequest withInputDataConfig(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.
NOTE: This method appends the values to the existing list (if any). Use
setInputDataConfig(java.util.Collection) or withInputDataConfig(java.util.Collection) if you
want to override the existing values.
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.
public CreateTrainingJobRequest withInputDataConfig(Collection<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.
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.
public void setOutputDataConfig(OutputDataConfig outputDataConfig)
Specifies the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.
outputDataConfig - Specifies the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates
subfolders for the artifacts.public OutputDataConfig getOutputDataConfig()
Specifies the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.
public CreateTrainingJobRequest withOutputDataConfig(OutputDataConfig outputDataConfig)
Specifies the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.
outputDataConfig - Specifies the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates
subfolders for the artifacts.public void setResourceConfig(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.
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.
public ResourceConfig getResourceConfig()
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 CreateTrainingJobRequest withResourceConfig(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.
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.
public void setStoppingCondition(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.
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.
public StoppingCondition getStoppingCondition()
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 CreateTrainingJobRequest withStoppingCondition(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.
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.
public List<Tag> getTags()
An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.
public void setTags(Collection<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.
tags - An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.public CreateTrainingJobRequest withTags(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.
NOTE: This method appends the values to the existing list (if any). Use
setTags(java.util.Collection) or withTags(java.util.Collection) if you want to override the
existing values.
tags - An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.public CreateTrainingJobRequest withTags(Collection<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.
tags - An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.public String toString()
toString in class ObjectObject.toString()public CreateTrainingJobRequest clone()
AmazonWebServiceRequestclone in class AmazonWebServiceRequestObject.clone()Copyright © 2013 Amazon Web Services, Inc. All Rights Reserved.