public static interface CreateTrainingJobRequest.Builder extends SageMakerRequest.Builder, SdkPojo, CopyableBuilder<CreateTrainingJobRequest.Builder,CreateTrainingJobRequest>
| Modifier and Type | Method and Description |
|---|---|
CreateTrainingJobRequest.Builder |
algorithmSpecification(AlgorithmSpecification algorithmSpecification)
The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata,
including the input mode.
|
default CreateTrainingJobRequest.Builder |
algorithmSpecification(Consumer<AlgorithmSpecification.Builder> algorithmSpecification)
The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata,
including the input mode.
|
CreateTrainingJobRequest.Builder |
enableNetworkIsolation(Boolean enableNetworkIsolation)
Isolates the training container.
|
CreateTrainingJobRequest.Builder |
hyperParameters(Map<String,String> hyperParameters)
Algorithm-specific parameters that influence the quality of the model.
|
CreateTrainingJobRequest.Builder |
inputDataConfig(Channel... inputDataConfig)
An array of
Channel objects. |
CreateTrainingJobRequest.Builder |
inputDataConfig(Collection<Channel> inputDataConfig)
An array of
Channel objects. |
CreateTrainingJobRequest.Builder |
inputDataConfig(Consumer<Channel.Builder>... inputDataConfig)
An array of
Channel objects. |
default CreateTrainingJobRequest.Builder |
outputDataConfig(Consumer<OutputDataConfig.Builder> outputDataConfig)
Specifies the path to the S3 bucket where you want to store model artifacts.
|
CreateTrainingJobRequest.Builder |
outputDataConfig(OutputDataConfig outputDataConfig)
Specifies the path to the S3 bucket where you want to store model artifacts.
|
CreateTrainingJobRequest.Builder |
overrideConfiguration(AwsRequestOverrideConfiguration overrideConfiguration)
Add an optional request override configuration.
|
CreateTrainingJobRequest.Builder |
overrideConfiguration(Consumer<AwsRequestOverrideConfiguration.Builder> builderConsumer)
Add an optional request override configuration.
|
default CreateTrainingJobRequest.Builder |
resourceConfig(Consumer<ResourceConfig.Builder> resourceConfig)
The resources, including the ML compute instances and ML storage volumes, to use for model training.
|
CreateTrainingJobRequest.Builder |
resourceConfig(ResourceConfig resourceConfig)
The resources, including the ML compute instances and ML storage volumes, to use for model training.
|
CreateTrainingJobRequest.Builder |
roleArn(String roleArn)
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker assumes to perform tasks on your behalf.
|
default CreateTrainingJobRequest.Builder |
stoppingCondition(Consumer<StoppingCondition.Builder> stoppingCondition)
Sets a duration for training.
|
CreateTrainingJobRequest.Builder |
stoppingCondition(StoppingCondition stoppingCondition)
Sets a duration for training.
|
CreateTrainingJobRequest.Builder |
tags(Collection<Tag> tags)
An array of key-value pairs.
|
CreateTrainingJobRequest.Builder |
tags(Consumer<Tag.Builder>... tags)
An array of key-value pairs.
|
CreateTrainingJobRequest.Builder |
tags(Tag... tags)
An array of key-value pairs.
|
CreateTrainingJobRequest.Builder |
trainingJobName(String trainingJobName)
The name of the training job.
|
default CreateTrainingJobRequest.Builder |
vpcConfig(Consumer<VpcConfig.Builder> vpcConfig)
A VpcConfig object that specifies the VPC that you want your training job to connect to.
|
CreateTrainingJobRequest.Builder |
vpcConfig(VpcConfig vpcConfig)
A VpcConfig object that specifies the VPC that you want your training job to connect to.
|
buildoverrideConfigurationcopyapplyMutation, buildCreateTrainingJobRequest.Builder trainingJobName(String trainingJobName)
The name of the training job. The name must be unique within an AWS Region in an AWS account.
trainingJobName - The name of the training job. The name must be unique within an AWS Region in an AWS account.CreateTrainingJobRequest.Builder hyperParameters(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.
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.
CreateTrainingJobRequest.Builder algorithmSpecification(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.
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.default CreateTrainingJobRequest.Builder algorithmSpecification(Consumer<AlgorithmSpecification.Builder> 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.
This is a convenience that creates an instance of theAlgorithmSpecification.Builder avoiding the
need to create one manually via AlgorithmSpecification.builder().
When the Consumer completes, SdkBuilder.build() is called immediately and
its result is passed to algorithmSpecification(AlgorithmSpecification).algorithmSpecification - a consumer that will call methods on AlgorithmSpecification.BuilderalgorithmSpecification(AlgorithmSpecification)CreateTrainingJobRequest.Builder roleArn(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.
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.
CreateTrainingJobRequest.Builder inputDataConfig(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.
CreateTrainingJobRequest.Builder inputDataConfig(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.
CreateTrainingJobRequest.Builder inputDataConfig(Consumer<Channel.Builder>... 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.
This is a convenience that creates an instance of theList.Builder avoiding the need to
create one manually via List#builder() .
When the Consumer completes, List.Builder#build() is called immediately and its
result is passed to #inputDataConfig(List) .inputDataConfig - a consumer that will call methods on List.Builder #inputDataConfig(List) CreateTrainingJobRequest.Builder outputDataConfig(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.default CreateTrainingJobRequest.Builder outputDataConfig(Consumer<OutputDataConfig.Builder> outputDataConfig)
Specifies the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.
This is a convenience that creates an instance of theOutputDataConfig.Builder avoiding the need to
create one manually via OutputDataConfig.builder().
When the Consumer completes, SdkBuilder.build() is called immediately and its
result is passed to outputDataConfig(OutputDataConfig).outputDataConfig - a consumer that will call methods on OutputDataConfig.BuilderoutputDataConfig(OutputDataConfig)CreateTrainingJobRequest.Builder resourceConfig(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.
default CreateTrainingJobRequest.Builder resourceConfig(Consumer<ResourceConfig.Builder> 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.Builder avoiding the need to
create one manually via ResourceConfig.builder().
When the Consumer completes, SdkBuilder.build() is called immediately and its
result is passed to resourceConfig(ResourceConfig).resourceConfig - a consumer that will call methods on ResourceConfig.BuilderresourceConfig(ResourceConfig)CreateTrainingJobRequest.Builder vpcConfig(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.
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.default CreateTrainingJobRequest.Builder vpcConfig(Consumer<VpcConfig.Builder> 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.
This is a convenience that creates an instance of theVpcConfig.Builder avoiding the need to create
one manually via VpcConfig.builder().
When the Consumer completes, SdkBuilder.build() is called immediately and its result
is passed to vpcConfig(VpcConfig).vpcConfig - a consumer that will call methods on VpcConfig.BuildervpcConfig(VpcConfig)CreateTrainingJobRequest.Builder stoppingCondition(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.
default CreateTrainingJobRequest.Builder stoppingCondition(Consumer<StoppingCondition.Builder> 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.Builder avoiding the need to
create one manually via StoppingCondition.builder().
When the Consumer completes, SdkBuilder.build() is called immediately and its
result is passed to stoppingCondition(StoppingCondition).stoppingCondition - a consumer that will call methods on StoppingCondition.BuilderstoppingCondition(StoppingCondition)CreateTrainingJobRequest.Builder tags(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.CreateTrainingJobRequest.Builder tags(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.CreateTrainingJobRequest.Builder tags(Consumer<Tag.Builder>... tags)
An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.
This is a convenience that creates an instance of theList.Builder avoiding the need to create
one manually via List#builder() .
When the Consumer completes, List.Builder#build() is called immediately and its result
is passed to #tags(List) .tags - a consumer that will call methods on List.Builder #tags(List) CreateTrainingJobRequest.Builder enableNetworkIsolation(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.
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.
CreateTrainingJobRequest.Builder overrideConfiguration(AwsRequestOverrideConfiguration overrideConfiguration)
AwsRequest.BuilderoverrideConfiguration in interface AwsRequest.BuilderoverrideConfiguration - The override configuration.CreateTrainingJobRequest.Builder overrideConfiguration(Consumer<AwsRequestOverrideConfiguration.Builder> builderConsumer)
AwsRequest.BuilderoverrideConfiguration in interface AwsRequest.BuilderbuilderConsumer - A Consumer to which an empty AwsRequestOverrideConfiguration.Builder will be
given.Copyright © 2017 Amazon Web Services, Inc. All Rights Reserved.