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New-SMTrainingJob-TrainingJobName <String>-AlgorithmSpecification <AlgorithmSpecification>-DebugHookConfig_CollectionConfiguration <CollectionConfiguration[]>-DebugRuleConfiguration <DebugRuleConfiguration[]>-ProfilerConfig_DisableProfiler <Boolean>-EnableInterContainerTrafficEncryption <Boolean>-EnableManagedSpotTraining <Boolean>-EnableNetworkIsolation <Boolean>-Environment <Hashtable>-ExperimentConfig_ExperimentName <String>-DebugHookConfig_HookParameter <Hashtable>-HyperParameter <Hashtable>-InputDataConfig <Channel[]>-CheckpointConfig_LocalPath <String>-DebugHookConfig_LocalPath <String>-TensorBoardOutputConfig_LocalPath <String>-RetryStrategy_MaximumRetryAttempt <Int32>-StoppingCondition_MaxRuntimeInSecond <Int32>-StoppingCondition_MaxWaitTimeInSecond <Int32>-OutputDataConfig <OutputDataConfig>-ProfilerRuleConfiguration <ProfilerRuleConfiguration[]>-ProfilerConfig_ProfilingIntervalInMillisecond <Int64>-ProfilerConfig_ProfilingParameter <Hashtable>-ResourceConfig <ResourceConfig>-RoleArn <String>-ExperimentConfig_RunName <String>-DebugHookConfig_S3OutputPath <String>-ProfilerConfig_S3OutputPath <String>-TensorBoardOutputConfig_S3OutputPath <String>-CheckpointConfig_S3Uri <String>-VpcConfig_SecurityGroupId <String[]>-VpcConfig_Subnet <String[]>-Tag <Tag[]>-ExperimentConfig_TrialComponentDisplayName <String>-ExperimentConfig_TrialName <String>-Select <String>-PassThru <SwitchParameter>-Force <SwitchParameter>-ClientConfig <AmazonSageMakerConfig>
AlgorithmSpecification
- Identifies the training algorithm to use. HyperParameters
- Specify these algorithm-specific parameters to enable the estimation of model parameters during training. Hyperparameters can be tuned to optimize this learning process. For a list of hyperparameters for each training algorithm provided by SageMaker, see Algorithms.
InputDataConfig
- Describes the input required by the training job and the Amazon S3, EFS, or FSx location where it is stored. OutputDataConfig
- Identifies the Amazon S3 bucket where you want SageMaker to save the results of model training. ResourceConfig
- Identifies the resources, ML compute instances, and ML storage volumes to deploy for model training. In distributed training, you specify more than one instance. EnableManagedSpotTraining
- Optimize the cost of training machine learning models by up to 80% by using Amazon EC2 Spot instances. For more information, see Managed Spot Training. RoleArn
- The Amazon Resource Name (ARN) that SageMaker assumes to perform tasks on your behalf during model training. You must grant this role the necessary permissions so that SageMaker can successfully complete model training. StoppingCondition
- To help cap training costs, use MaxRuntimeInSeconds
to set a time limit for training. Use MaxWaitTimeInSeconds
to specify how long a managed spot training job has to complete. Environment
- The environment variables to set in the Docker container. RetryStrategy
- The number of times to retry the job when the job fails due to an InternalServerError
. Required? | True |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
/opt/ml/checkpoints/
. Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
s3://bucket-name/key-name-prefix
. Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
CollectionConfiguration
parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job. Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | DebugHookConfig_CollectionConfigurations |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | DebugHookConfig_HookParameters |
/opt/ml/output/tensors/
. Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | DebugRuleConfigurations |
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. Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
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. Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Length Constraint
. Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | HyperParameters |
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, 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 are available as input streams. They do not need to be downloaded.Your input must be in the same Amazon Web Services region as your training job. Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Required? | True |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
True
. Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | ProfilerConfig_ProfilingIntervalInMilliseconds |
DetailedProfilingConfig
, PythonProfilingConfig
, and DataLoaderProfilingConfig
. The following codes are configuration structures for the ProfilingParameters
parameter. To learn more about how to configure the ProfilingParameters
parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job. Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | ProfilerConfig_ProfilingParameters |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | ProfilerRuleConfigurations |
File
as the TrainingInputMode
in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1. Required? | True |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
SecondaryStatus
is changed to STARTING
. Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | RetryStrategy_MaximumRetryAttempts |
iam:PassRole
permission. Required? | True |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
TimeOut
error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy
is specified in the job request, MaxRuntimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.The maximum time that a TrainingJob
can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days. Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | StoppingCondition_MaxRuntimeInSeconds |
MaxRuntimeInSeconds
. If the job does not complete during this time, SageMaker ends the job.When RetryStrategy
is specified in the job request, MaxWaitTimeInSeconds
specifies the maximum time for all of the attempts in total, not each individual attempt. Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | StoppingCondition_MaxWaitTimeInSeconds |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | Tags |
/opt/ml/output/tensorboard
. Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Required? | True |
Position? | 1 |
Accept pipeline input? | True (ByValue, ByPropertyName) |
Subnets
field. Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | VpcConfig_SecurityGroupIds |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | VpcConfig_Subnets |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | AK |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByValue, ByPropertyName) |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByValue, ByPropertyName) |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | AWSProfilesLocation, ProfilesLocation |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | StoredCredentials, AWSProfileName |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | RegionToCall |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | SK, SecretAccessKey |
Required? | False |
Position? | Named |
Accept pipeline input? | True (ByPropertyName) |
Aliases | ST |
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