NAME
Paws::SageMaker::CreateTrainingJob - Arguments for method CreateTrainingJob on Paws::SageMaker
DESCRIPTION
This class represents the parameters used for calling the method CreateTrainingJob on the Amazon SageMaker Service service. Use the attributes of this class as arguments to method CreateTrainingJob.
You shouldn't make instances of this class. Each attribute should be used as a named argument in the call to CreateTrainingJob.
SYNOPSIS
my $api.sagemaker = Paws->service('SageMaker');
my $CreateTrainingJobResponse = $api . sagemaker->CreateTrainingJob(
AlgorithmSpecification => {
TrainingInputMode => 'Pipe', # values: Pipe, File
AlgorithmName => 'MyArnOrName', # min: 1, max: 170; OPTIONAL
EnableSageMakerMetricsTimeSeries => 1, # OPTIONAL
MetricDefinitions => [
{
Name => 'MyMetricName', # min: 1, max: 255
Regex => 'MyMetricRegex', # min: 1, max: 500
},
...
], # max: 40; OPTIONAL
TrainingImage => 'MyAlgorithmImage', # max: 255; OPTIONAL
},
OutputDataConfig => {
S3OutputPath => 'MyS3Uri', # max: 1024
KmsKeyId => 'MyKmsKeyId', # max: 2048; OPTIONAL
},
ResourceConfig => {
InstanceCount => 1, # min: 1
InstanceType => 'ml.m4.xlarge'
, # values: ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p3dn.24xlarge, ml.p4d.24xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5n.xlarge, ml.c5n.2xlarge, ml.c5n.4xlarge, ml.c5n.9xlarge, ml.c5n.18xlarge
VolumeSizeInGB => 1, # min: 1
VolumeKmsKeyId => 'MyKmsKeyId', # max: 2048; OPTIONAL
},
RoleArn => 'MyRoleArn',
StoppingCondition => {
MaxRuntimeInSeconds => 1, # min: 1; OPTIONAL
MaxWaitTimeInSeconds => 1, # min: 1; OPTIONAL
},
TrainingJobName => 'MyTrainingJobName',
CheckpointConfig => {
S3Uri => 'MyS3Uri', # max: 1024
LocalPath => 'MyDirectoryPath', # max: 4096; OPTIONAL
}, # OPTIONAL
DebugHookConfig => {
S3OutputPath => 'MyS3Uri', # max: 1024
CollectionConfigurations => [
{
CollectionName => 'MyCollectionName', # min: 1, max: 256; OPTIONAL
CollectionParameters => {
'MyConfigKey' =>
'MyConfigValue', # key: min: 1, max: 256, value: max: 256
}, # max: 20; OPTIONAL
},
...
], # max: 20; OPTIONAL
HookParameters => {
'MyConfigKey' =>
'MyConfigValue', # key: min: 1, max: 256, value: max: 256
}, # max: 20; OPTIONAL
LocalPath => 'MyDirectoryPath', # max: 4096; OPTIONAL
}, # OPTIONAL
DebugRuleConfigurations => [
{
RuleConfigurationName => 'MyRuleConfigurationName', # min: 1, max: 256
RuleEvaluatorImage => 'MyAlgorithmImage', # max: 255; OPTIONAL
InstanceType => 'ml.t3.medium'
, # values: ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge; OPTIONAL
LocalPath => 'MyDirectoryPath', # max: 4096; OPTIONAL
RuleParameters => {
'MyConfigKey' =>
'MyConfigValue', # key: min: 1, max: 256, value: max: 256
}, # max: 100; OPTIONAL
S3OutputPath => 'MyS3Uri', # max: 1024
VolumeSizeInGB => 1, # OPTIONAL
},
...
], # OPTIONAL
EnableInterContainerTrafficEncryption => 1, # OPTIONAL
EnableManagedSpotTraining => 1, # OPTIONAL
EnableNetworkIsolation => 1, # OPTIONAL
Environment => {
'MyTrainingEnvironmentKey' =>
'MyTrainingEnvironmentValue', # key: max: 512, value: max: 512
}, # OPTIONAL
ExperimentConfig => {
ExperimentName => 'MyExperimentEntityName', # min: 1, max: 120; OPTIONAL
TrialComponentDisplayName =>
'MyExperimentEntityName', # min: 1, max: 120; OPTIONAL
TrialName => 'MyExperimentEntityName', # min: 1, max: 120; OPTIONAL
}, # OPTIONAL
HyperParameters => {
'MyHyperParameterKey' =>
'MyHyperParameterValue', # key: max: 256, value: max: 2500
}, # OPTIONAL
InputDataConfig => [
{
ChannelName => 'MyChannelName', # min: 1, max: 64
DataSource => {
FileSystemDataSource => {
DirectoryPath => 'MyDirectoryPath', # max: 4096; OPTIONAL
FileSystemAccessMode => 'rw', # values: rw, ro
FileSystemId => 'MyFileSystemId', # min: 11
FileSystemType => 'EFS', # values: EFS, FSxLustre
}, # OPTIONAL
S3DataSource => {
S3DataType => 'ManifestFile'
, # values: ManifestFile, S3Prefix, AugmentedManifestFile
S3Uri => 'MyS3Uri', # max: 1024
AttributeNames => [
'MyAttributeName', ... # min: 1, max: 256
], # max: 16; OPTIONAL
S3DataDistributionType => 'FullyReplicated'
, # values: FullyReplicated, ShardedByS3Key; OPTIONAL
}, # OPTIONAL
},
CompressionType => 'None', # values: None, Gzip; OPTIONAL
ContentType => 'MyContentType', # max: 256; OPTIONAL
InputMode => 'Pipe', # values: Pipe, File
RecordWrapperType => 'None', # values: None, RecordIO; OPTIONAL
ShuffleConfig => {
Seed => 1,
}, # OPTIONAL
},
...
], # OPTIONAL
ProfilerConfig => {
S3OutputPath => 'MyS3Uri', # max: 1024
ProfilingIntervalInMilliseconds => 1, # OPTIONAL
ProfilingParameters => {
'MyConfigKey' =>
'MyConfigValue', # key: min: 1, max: 256, value: max: 256
}, # max: 20; OPTIONAL
}, # OPTIONAL
ProfilerRuleConfigurations => [
{
RuleConfigurationName => 'MyRuleConfigurationName', # min: 1, max: 256
RuleEvaluatorImage => 'MyAlgorithmImage', # max: 255; OPTIONAL
InstanceType => 'ml.t3.medium'
, # values: ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge; OPTIONAL
LocalPath => 'MyDirectoryPath', # max: 4096; OPTIONAL
RuleParameters => {
'MyConfigKey' =>
'MyConfigValue', # key: min: 1, max: 256, value: max: 256
}, # max: 100; OPTIONAL
S3OutputPath => 'MyS3Uri', # max: 1024
VolumeSizeInGB => 1, # OPTIONAL
},
...
], # OPTIONAL
RetryStrategy => {
MaximumRetryAttempts => 1, # min: 1, max: 30
}, # OPTIONAL
Tags => [
{
Key => 'MyTagKey', # min: 1, max: 128
Value => 'MyTagValue', # max: 256
},
...
], # OPTIONAL
TensorBoardOutputConfig => {
S3OutputPath => 'MyS3Uri', # max: 1024
LocalPath => 'MyDirectoryPath', # max: 4096; OPTIONAL
}, # OPTIONAL
VpcConfig => {
SecurityGroupIds => [
'MySecurityGroupId', ... # max: 32
], # min: 1, max: 5
Subnets => [
'MySubnetId', ... # max: 32
], # min: 1, max: 16
}, # OPTIONAL
);
# Results:
my $TrainingJobArn = $CreateTrainingJobResponse->TrainingJobArn;
# Returns a L<Paws::SageMaker::CreateTrainingJobResponse> object.Values for attributes that are native types (Int, String, Float, etc) can passed as-is (scalar values). Values for complex Types (objects) can be passed as a HashRef. The keys and values of the hashref will be used to instance the underlying object. For the AWS API documentation, see https://docs.aws.amazon.com/goto/WebAPI/api.sagemaker/CreateTrainingJob
ATTRIBUTES
REQUIRED AlgorithmSpecification => Paws::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 (https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html). For information about providing your own algorithms, see Using Your Own Algorithms with Amazon SageMaker (https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html).
CheckpointConfig => Paws::SageMaker::CheckpointConfig
Contains information about the output location for managed spot training checkpoint data.
DebugHookConfig => Paws::SageMaker::DebugHookConfig
DebugRuleConfigurations => ArrayRef[Paws::SageMaker::DebugRuleConfiguration]
Configuration information for Debugger rules for debugging output tensors.
EnableInterContainerTrafficEncryption => Bool
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 (https://docs.aws.amazon.com/sagemaker/latest/dg/train-encrypt.html).
EnableManagedSpotTraining => Bool
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.
EnableNetworkIsolation => Bool
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, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
Environment => Paws::SageMaker::TrainingEnvironmentMap
The environment variables to set in the Docker container.
ExperimentConfig => Paws::SageMaker::ExperimentConfig
HyperParameters => Paws::SageMaker::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 (https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html).
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.
InputDataConfig => ArrayRef[Paws::SageMaker::Channel]
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, 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. For example, if you specify an EFS location, input data files will be made available as input streams. They do not need to be downloaded.
REQUIRED OutputDataConfig => Paws::SageMaker::OutputDataConfig
Specifies the path to the S3 location where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.
ProfilerConfig => Paws::SageMaker::ProfilerConfig
ProfilerRuleConfigurations => ArrayRef[Paws::SageMaker::ProfilerRuleConfiguration]
Configuration information for Debugger rules for profiling system and framework metrics.
REQUIRED ResourceConfig => Paws::SageMaker::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.
RetryStrategy => Paws::SageMaker::RetryStrategy
The number of times to retry the job when the job fails due to an InternalServerError.
REQUIRED RoleArn => Str
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 (https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html).
To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.
REQUIRED StoppingCondition => Paws::SageMaker::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, Amazon SageMaker ends the training job. Use this API 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 can use this 120-second window to save the model artifacts, so the results of training are not lost.
Tags => ArrayRef[Paws::SageMaker::Tag]
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 (https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html).
TensorBoardOutputConfig => Paws::SageMaker::TensorBoardOutputConfig
REQUIRED TrainingJobName => Str
The name of the training job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.
VpcConfig => Paws::SageMaker::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 (https://docs.aws.amazon.com/sagemaker/latest/dg/train-vpc.html).
SEE ALSO
This class forms part of Paws, documenting arguments for method CreateTrainingJob in Paws::SageMaker
BUGS and CONTRIBUTIONS
The source code is located here: https://github.com/pplu/aws-sdk-perl
Please report bugs to: https://github.com/pplu/aws-sdk-perl/issues