HyperParameterTuningJob

sagemaker.services.k8s.aws/v1alpha1

Metadata#

Spec#

autotune: 
  mode: string
hyperParameterTuningJobConfig: 
  hyperParameterTuningJobObjective: 
    metricName: string
    type_: string
  parameterRanges: 
    autoParameters:
    - name: string
      valueHint: string
    categoricalParameterRanges:
    - name: string
      values:
      - string
    continuousParameterRanges:
    - maxValue: string
      minValue: string
      name: string
      scalingType: string
    integerParameterRanges:
    - maxValue: string
      minValue: string
      name: string
      scalingType: string
  resourceLimits: 
    maxNumberOfTrainingJobs: integer
    maxParallelTrainingJobs: integer
  strategy: string
  trainingJobEarlyStoppingType: string
  tuningJobCompletionCriteria: 
    targetObjectiveMetricValue: number
hyperParameterTuningJobName: string
tags:
- key: string
  value: string
trainingJobDefinition: 
  algorithmSpecification: 
    algorithmName: string
    metricDefinitions:
    - name: string
      regex: string
    trainingImage: string
    trainingInputMode: string
  checkpointConfig: 
    localPath: string
    s3URI: string
  definitionName: string
  enableInterContainerTrafficEncryption: boolean
  enableManagedSpotTraining: boolean
  enableNetworkIsolation: boolean
  hyperParameterRanges: 
    autoParameters:
    - name: string
      valueHint: string
    categoricalParameterRanges:
    - name: string
      values:
      - string
    continuousParameterRanges:
    - maxValue: string
      minValue: string
      name: string
      scalingType: string
    integerParameterRanges:
    - maxValue: string
      minValue: string
      name: string
      scalingType: string
  inputDataConfig:
  - channelName: string
    compressionType: string
    contentType: string
    dataSource: 
      fileSystemDataSource: 
        directoryPath: string
        fileSystemAccessMode: string
        fileSystemID: string
        fileSystemType: string
      s3DataSource: 
        attributeNames:
        - string
        instanceGroupNames:
        - string
        s3DataDistributionType: string
        s3DataType: string
        s3URI: string
    inputMode: string
    recordWrapperType: string
    shuffleConfig: 
      seed: integer
  outputDataConfig: 
    compressionType: string
    kmsKeyID: string
    s3OutputPath: string
  resourceConfig: 
    instanceCount: integer
    instanceGroups:
    - instanceCount: integer
      instanceGroupName: string
      instanceType: string
    instanceType: string
    keepAlivePeriodInSeconds: integer
    volumeKMSKeyID: string
    volumeSizeInGB: integer
  retryStrategy: 
    maximumRetryAttempts: integer
  roleARN: string
  staticHyperParameters: {}
  stoppingCondition: 
    maxPendingTimeInSeconds: integer
    maxRuntimeInSeconds: integer
    maxWaitTimeInSeconds: integer
  tuningObjective: 
    metricName: string
    type_: string
  vpcConfig: 
    securityGroupIDs:
    - string
    subnets:
    - string
trainingJobDefinitions:
  algorithmSpecification: 
    algorithmName: string
    metricDefinitions:
    - name: string
      regex: string
    trainingImage: string
    trainingInputMode: string
  checkpointConfig: 
    localPath: string
    s3URI: string
  definitionName: string
  enableInterContainerTrafficEncryption: boolean
  enableManagedSpotTraining: boolean
  enableNetworkIsolation: boolean
  hyperParameterRanges: 
    autoParameters:
    - name: string
      valueHint: string
    categoricalParameterRanges:
    - name: string
      values:
      - string
    continuousParameterRanges:
    - maxValue: string
      minValue: string
      name: string
      scalingType: string
    integerParameterRanges:
    - maxValue: string
      minValue: string
      name: string
      scalingType: string
  inputDataConfig:
  - channelName: string
    compressionType: string
    contentType: string
    dataSource: 
      fileSystemDataSource: 
        directoryPath: string
        fileSystemAccessMode: string
        fileSystemID: string
        fileSystemType: string
      s3DataSource: 
        attributeNames:
        - string
        instanceGroupNames:
        - string
        s3DataDistributionType: string
        s3DataType: string
        s3URI: string
    inputMode: string
    recordWrapperType: string
    shuffleConfig: 
      seed: integer
  outputDataConfig: 
    compressionType: string
    kmsKeyID: string
    s3OutputPath: string
  resourceConfig: 
    instanceCount: integer
    instanceGroups:
    - instanceCount: integer
      instanceGroupName: string
      instanceType: string
    instanceType: string
    keepAlivePeriodInSeconds: integer
    volumeKMSKeyID: string
    volumeSizeInGB: integer
  retryStrategy: 
    maximumRetryAttempts: integer
  roleARN: string
  staticHyperParameters: {}
  stoppingCondition: 
    maxPendingTimeInSeconds: integer
    maxRuntimeInSeconds: integer
    maxWaitTimeInSeconds: integer
  tuningObjective: 
    metricName: string
    type_: string
  vpcConfig: 
    securityGroupIDs:
    - string
    subnets:
    - string
warmStartConfig: 
  parentHyperParameterTuningJobs:
  - hyperParameterTuningJobName: string
  warmStartType: string

You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AddTags.html).

For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources (https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html). For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy (https://d1.awsstatic.com/whitepapers/aws-tagging-best-practices.pdf). || tags.[].key
Optional | string
| | tags.[].value
Optional | string
| | trainingJobDefinition
Optional | object
The HyperParameterTrainingJobDefinition (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTrainingJobDefinition.html)
object that describes the training jobs that this tuning job launches, including
static hyperparameters, input data configuration, output data configuration,
resource configuration, and stopping condition. | | trainingJobDefinition.algorithmSpecification
Optional | object
Specifies which training algorithm to use for training jobs that a hyperparameter
tuning job launches and the metrics to monitor. | | trainingJobDefinition.algorithmSpecification.algorithmName
Optional | string
| | trainingJobDefinition.algorithmSpecification.metricDefinitions
Optional | array
| | trainingJobDefinition.algorithmSpecification.metricDefinitions.[]
Required | object
Specifies a metric that the training algorithm writes to stderr or stdout. You can view these logs to understand how your training job performs and check for any errors encountered during training. SageMaker hyperparameter tuning captures all defined metrics. Specify one of the defined metrics to use as an objective metric using the TuningObjective (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTrainingJobDefinition.html#sagemaker-Type-HyperParameterTrainingJobDefinition-TuningObjective) parameter in the HyperParameterTrainingJobDefinition API to evaluate job performance during hyperparameter tuning. || trainingJobDefinition.algorithmSpecification.metricDefinitions.[].name
Optional | string
| | trainingJobDefinition.algorithmSpecification.metricDefinitions.[].regex
Optional | string
| | trainingJobDefinition.algorithmSpecification.trainingImage
Optional | string
| | trainingJobDefinition.algorithmSpecification.trainingInputMode
Optional | string
The training input mode that the algorithm supports. For more information
about input modes, see Algorithms (https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html).


Pipe mode


If an algorithm supports Pipe mode, Amazon SageMaker streams data directly
from Amazon S3 to the container.


File mode


If an algorithm supports File mode, SageMaker downloads the training data
from S3 to the provisioned ML storage volume, and mounts the directory to
the Docker volume for the training container.


You must provision the ML storage volume with sufficient capacity to accommodate
the data downloaded from S3. In addition to the training data, the ML storage
volume also stores the output model. The algorithm container uses the ML
storage volume to also store intermediate information, if any.


For distributed algorithms, training data is distributed uniformly. Your
training duration is predictable if the input data objects sizes are approximately
the same. SageMaker does not split the files any further for model training.
If the object sizes are skewed, training won’t be optimal as the data distribution
is also skewed when one host in a training cluster is overloaded, thus becoming
a bottleneck in training.


FastFile mode


If an algorithm supports FastFile mode, SageMaker streams data directly from
S3 to the container with no code changes, and provides file system access
to the data. Users can author their training script to interact with these
files as if they were stored on disk.


FastFile mode works best when the data is read sequentially. Augmented manifest
files aren’t supported. The startup time is lower when there are fewer files
in the S3 bucket provided. | | trainingJobDefinition.checkpointConfig
Optional | object
Contains information about the output location for managed spot training
checkpoint data. | | trainingJobDefinition.checkpointConfig.localPath
Optional | string
| | trainingJobDefinition.checkpointConfig.s3URI
Optional | string
| | trainingJobDefinition.definitionName
Optional | string
| | trainingJobDefinition.enableInterContainerTrafficEncryption
Optional | boolean
| | trainingJobDefinition.enableManagedSpotTraining
Optional | boolean
| | trainingJobDefinition.enableNetworkIsolation
Optional | boolean
| | trainingJobDefinition.hyperParameterRanges
Optional | object
Specifies ranges of integer, continuous, and categorical hyperparameters
that a hyperparameter tuning job searches. The hyperparameter tuning job
launches training jobs with hyperparameter values within these ranges to
find the combination of values that result in the training job with the best
performance as measured by the objective metric of the hyperparameter tuning
job.


The maximum number of items specified for Array Members refers to the maximum
number of hyperparameters for each range and also the maximum for the hyperparameter
tuning job itself. That is, the sum of the number of hyperparameters for
all the ranges can’t exceed the maximum number specified. | | trainingJobDefinition.hyperParameterRanges.autoParameters
Optional | array
| | trainingJobDefinition.hyperParameterRanges.autoParameters.[]
Required | object
The name and an example value of the hyperparameter that you want to use in Autotune. If Automatic model tuning (AMT) determines that your hyperparameter is eligible for Autotune, an optimal hyperparameter range is selected for you. || trainingJobDefinition.hyperParameterRanges.autoParameters.[].name
Optional | string
| | trainingJobDefinition.hyperParameterRanges.autoParameters.[].valueHint
Optional | string
| | trainingJobDefinition.hyperParameterRanges.categoricalParameterRanges
Optional | array
| | trainingJobDefinition.hyperParameterRanges.categoricalParameterRanges.[]
Required | object
A list of categorical hyperparameters to tune. || trainingJobDefinition.hyperParameterRanges.categoricalParameterRanges.[].name
Optional | string
| | trainingJobDefinition.hyperParameterRanges.categoricalParameterRanges.[].values
Optional | array
| | trainingJobDefinition.hyperParameterRanges.categoricalParameterRanges.[].values.[]
Required | string
|| trainingJobDefinition.hyperParameterRanges.continuousParameterRanges
Optional | array
| | trainingJobDefinition.hyperParameterRanges.continuousParameterRanges.[]
Required | object
A list of continuous hyperparameters to tune. || trainingJobDefinition.hyperParameterRanges.continuousParameterRanges.[].maxValue
Optional | string
| | trainingJobDefinition.hyperParameterRanges.continuousParameterRanges.[].minValue
Optional | string
| | trainingJobDefinition.hyperParameterRanges.continuousParameterRanges.[].name
Optional | string
| | trainingJobDefinition.hyperParameterRanges.continuousParameterRanges.[].scalingType
Optional | string
| | trainingJobDefinition.hyperParameterRanges.integerParameterRanges
Optional | array
| | trainingJobDefinition.hyperParameterRanges.integerParameterRanges.[]
Required | object
For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches. || trainingJobDefinition.hyperParameterRanges.integerParameterRanges.[].maxValue
Optional | string
| | trainingJobDefinition.hyperParameterRanges.integerParameterRanges.[].minValue
Optional | string
| | trainingJobDefinition.hyperParameterRanges.integerParameterRanges.[].name
Optional | string
| | trainingJobDefinition.hyperParameterRanges.integerParameterRanges.[].scalingType
Optional | string
| | trainingJobDefinition.inputDataConfig
Optional | array
| | trainingJobDefinition.inputDataConfig.[]
Required | object
A channel is a named input source that training algorithms can consume. || trainingJobDefinition.inputDataConfig.[].channelName
Optional | string
| | trainingJobDefinition.inputDataConfig.[].compressionType
Optional | string
| | trainingJobDefinition.inputDataConfig.[].contentType
Optional | string
| | trainingJobDefinition.inputDataConfig.[].dataSource
Optional | object
Describes the location of the channel data. | | trainingJobDefinition.inputDataConfig.[].dataSource.fileSystemDataSource
Optional | object
Specifies a file system data source for a channel. | | trainingJobDefinition.inputDataConfig.[].dataSource.fileSystemDataSource.directoryPath
Optional | string
| | trainingJobDefinition.inputDataConfig.[].dataSource.fileSystemDataSource.fileSystemAccessMode
Optional | string
| | trainingJobDefinition.inputDataConfig.[].dataSource.fileSystemDataSource.fileSystemID
Optional | string
| | trainingJobDefinition.inputDataConfig.[].dataSource.fileSystemDataSource.fileSystemType
Optional | string
| | trainingJobDefinition.inputDataConfig.[].dataSource.s3DataSource
Optional | object
Describes the S3 data source.


Your input bucket must be in the same Amazon Web Services region as your
training job. | | trainingJobDefinition.inputDataConfig.[].dataSource.s3DataSource.attributeNames
Optional | array
| | trainingJobDefinition.inputDataConfig.[].dataSource.s3DataSource.attributeNames.[]
Required | string
|| trainingJobDefinition.inputDataConfig.[].dataSource.s3DataSource.instanceGroupNames
Optional | array
| | trainingJobDefinition.inputDataConfig.[].dataSource.s3DataSource.instanceGroupNames.[]
Required | string
|| trainingJobDefinition.inputDataConfig.[].dataSource.s3DataSource.s3DataDistributionType
Optional | string
| | trainingJobDefinition.inputDataConfig.[].dataSource.s3DataSource.s3DataType
Optional | string
| | trainingJobDefinition.inputDataConfig.[].dataSource.s3DataSource.s3URI
Optional | string
| | trainingJobDefinition.inputDataConfig.[].inputMode
Optional | string
The training input mode that the algorithm supports. For more information
about input modes, see Algorithms (https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html).


Pipe mode


If an algorithm supports Pipe mode, Amazon SageMaker streams data directly
from Amazon S3 to the container.


File mode


If an algorithm supports File mode, SageMaker downloads the training data
from S3 to the provisioned ML storage volume, and mounts the directory to
the Docker volume for the training container.


You must provision the ML storage volume with sufficient capacity to accommodate
the data downloaded from S3. In addition to the training data, the ML storage
volume also stores the output model. The algorithm container uses the ML
storage volume to also store intermediate information, if any.


For distributed algorithms, training data is distributed uniformly. Your
training duration is predictable if the input data objects sizes are approximately
the same. SageMaker does not split the files any further for model training.
If the object sizes are skewed, training won’t be optimal as the data distribution
is also skewed when one host in a training cluster is overloaded, thus becoming
a bottleneck in training.


FastFile mode


If an algorithm supports FastFile mode, SageMaker streams data directly from
S3 to the container with no code changes, and provides file system access
to the data. Users can author their training script to interact with these
files as if they were stored on disk.


FastFile mode works best when the data is read sequentially. Augmented manifest
files aren’t supported. The startup time is lower when there are fewer files
in the S3 bucket provided. | | trainingJobDefinition.inputDataConfig.[].recordWrapperType
Optional | string
| | trainingJobDefinition.inputDataConfig.[].shuffleConfig
Optional | object
A configuration for a shuffle option for input data in a channel. If you
use S3Prefix for S3DataType, the results of the S3 key prefix matches are
shuffled. If you use ManifestFile, the order of the S3 object references
in the ManifestFile is shuffled. If you use AugmentedManifestFile, the order
of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling
order is determined using the Seed value.


For Pipe input mode, when ShuffleConfig is specified shuffling is done at
the start of every epoch. With large datasets, this ensures that the order
of the training data is different for each epoch, and it helps reduce bias
and possible overfitting. In a multi-node training job when ShuffleConfig
is combined with S3DataDistributionType of ShardedByS3Key, the data is shuffled
across nodes so that the content sent to a particular node on the first epoch
might be sent to a different node on the second epoch. | | trainingJobDefinition.inputDataConfig.[].shuffleConfig.seed
Optional | integer
| | trainingJobDefinition.outputDataConfig
Optional | object
Provides information about how to store model training results (model artifacts). | | trainingJobDefinition.outputDataConfig.compressionType
Optional | string
| | trainingJobDefinition.outputDataConfig.kmsKeyID
Optional | string
| | trainingJobDefinition.outputDataConfig.s3OutputPath
Optional | string
| | trainingJobDefinition.resourceConfig
Optional | object
Describes the resources, including machine learning (ML) compute instances
and ML storage volumes, to use for model training. | | trainingJobDefinition.resourceConfig.instanceCount
Optional | integer
| | trainingJobDefinition.resourceConfig.instanceGroups
Optional | array
| | trainingJobDefinition.resourceConfig.instanceGroups.[]
Required | object
Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html) API, you can configure multiple instance groups . || trainingJobDefinition.resourceConfig.instanceGroups.[].instanceCount
Optional | integer
| | trainingJobDefinition.resourceConfig.instanceGroups.[].instanceGroupName
Optional | string
| | trainingJobDefinition.resourceConfig.instanceGroups.[].instanceType
Optional | string
| | trainingJobDefinition.resourceConfig.instanceType
Optional | string
| | trainingJobDefinition.resourceConfig.keepAlivePeriodInSeconds
Optional | integer
Optional. Customer requested period in seconds for which the Training cluster
is kept alive after the job is finished. | | trainingJobDefinition.resourceConfig.volumeKMSKeyID
Optional | string
| | trainingJobDefinition.resourceConfig.volumeSizeInGB
Optional | integer
| | trainingJobDefinition.retryStrategy
Optional | object
The retry strategy to use when a training job fails due to an InternalServerError.
RetryStrategy is specified as part of the CreateTrainingJob and CreateHyperParameterTuningJob
requests. You can add the StoppingCondition parameter to the request to limit
the training time for the complete job. | | trainingJobDefinition.retryStrategy.maximumRetryAttempts
Optional | integer
| | trainingJobDefinition.roleARN
Optional | string
| | trainingJobDefinition.staticHyperParameters
Optional | object
| | trainingJobDefinition.stoppingCondition
Optional | object
Specifies a limit to how long a model training job or model compilation job
can run. It also specifies how long a managed spot training job has to complete.
When the job reaches the time limit, SageMaker ends the training or compilation
job. Use this API to cap model training costs.


To stop a training job, 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.


The training algorithms provided by SageMaker automatically save the intermediate
results of a model training job when possible. This attempt to save artifacts
is only a best effort case as model might not be in a state from which it
can be saved. For example, if training has just started, the model might
not be ready to save. When saved, this intermediate data is a valid model
artifact. You can use it to create a model with CreateModel.


The Neural Topic Model (NTM) currently does not support saving intermediate
model artifacts. When training NTMs, make sure that the maximum runtime is
sufficient for the training job to complete. | | trainingJobDefinition.stoppingCondition.maxPendingTimeInSeconds
Optional | integer
Maximum job scheduler pending time in seconds. | | trainingJobDefinition.stoppingCondition.maxRuntimeInSeconds
Optional | integer
| | trainingJobDefinition.stoppingCondition.maxWaitTimeInSeconds
Optional | integer
| | trainingJobDefinition.tuningObjective
Optional | object
Defines the objective metric for a hyperparameter tuning job. Hyperparameter
tuning uses the value of this metric to evaluate the training jobs it launches,
and returns the training job that results in either the highest or lowest
value for this metric, depending on the value you specify for the Type parameter.
If you want to define a custom objective metric, see Define metrics and environment
variables (https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-metrics-variables.html). | | trainingJobDefinition.tuningObjective.metricName
Optional | string
| | **trainingJobDefinition.tuningObjective.type_**
Optional | **string**
| | **trainingJobDefinition.vpcConfig**
Optional | **object**
Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs,
hosted models, and compute resources have access to. You can control access
to and from your resources by configuring a VPC. For more information, see
Give SageMaker Access to Resources in your Amazon VPC (https://docs.aws.amazon.com/sagemaker/latest/dg/infrastructure-give-access.html). | | **trainingJobDefinition.vpcConfig.securityGroupIDs**
Optional | **array**
| | **trainingJobDefinition.vpcConfig.securityGroupIDs.[]**
Required | **string**
|| **trainingJobDefinition.vpcConfig.subnets**
Optional | **array**
| | **trainingJobDefinition.vpcConfig.subnets.[]**
Required | **string**
|| **trainingJobDefinitions**
Optional | **array**
A list of the HyperParameterTrainingJobDefinition (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTrainingJobDefinition.html)
objects launched for this tuning job. | | **trainingJobDefinitions.[]**
Required | **object**
Defines the training jobs launched by a hyperparameter tuning job. || **trainingJobDefinitions.[].algorithmSpecification**
Optional | **object**
Specifies which training algorithm to use for training jobs that a hyperparameter
tuning job launches and the metrics to monitor. | | **trainingJobDefinitions.[].algorithmSpecification.algorithmName**
Optional | **string**
| | **trainingJobDefinitions.[].algorithmSpecification.metricDefinitions**
Optional | **array**
| | **trainingJobDefinitions.[].algorithmSpecification.metricDefinitions.[]**
Required | **object**
Specifies a metric that the training algorithm writes to stderr or stdout. You can view these logs to understand how your training job performs and check for any errors encountered during training. SageMaker hyperparameter tuning captures all defined metrics. Specify one of the defined metrics to use as an objective metric using the TuningObjective (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTrainingJobDefinition.html#sagemaker-Type-HyperParameterTrainingJobDefinition-TuningObjective) parameter in the HyperParameterTrainingJobDefinition API to evaluate job performance during hyperparameter tuning. || **trainingJobDefinitions.[].algorithmSpecification.metricDefinitions.[].name**
Optional | **string**
| | **trainingJobDefinitions.[].algorithmSpecification.metricDefinitions.[].regex**
Optional | **string**
| | **trainingJobDefinitions.[].algorithmSpecification.trainingImage**
Optional | **string**
| | **trainingJobDefinitions.[].algorithmSpecification.trainingInputMode**
Optional | **string**
The training input mode that the algorithm supports. For more information
about input modes, see Algorithms (https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html).


Pipe mode


If an algorithm supports Pipe mode, Amazon SageMaker streams data directly
from Amazon S3 to the container.


File mode


If an algorithm supports File mode, SageMaker downloads the training data
from S3 to the provisioned ML storage volume, and mounts the directory to
the Docker volume for the training container.


You must provision the ML storage volume with sufficient capacity to accommodate
the data downloaded from S3. In addition to the training data, the ML storage
volume also stores the output model. The algorithm container uses the ML
storage volume to also store intermediate information, if any.


For distributed algorithms, training data is distributed uniformly. Your
training duration is predictable if the input data objects sizes are approximately
the same. SageMaker does not split the files any further for model training.
If the object sizes are skewed, training won’t be optimal as the data distribution
is also skewed when one host in a training cluster is overloaded, thus becoming
a bottleneck in training.


FastFile mode


If an algorithm supports FastFile mode, SageMaker streams data directly from
S3 to the container with no code changes, and provides file system access
to the data. Users can author their training script to interact with these
files as if they were stored on disk.


FastFile mode works best when the data is read sequentially. Augmented manifest
files aren’t supported. The startup time is lower when there are fewer files
in the S3 bucket provided. | | **trainingJobDefinitions.[].checkpointConfig**
Optional | **object**
Contains information about the output location for managed spot training
checkpoint data. | | **trainingJobDefinitions.[].checkpointConfig.localPath**
Optional | **string**
| | **trainingJobDefinitions.[].checkpointConfig.s3URI**
Optional | **string**
| | **trainingJobDefinitions.[].definitionName**
Optional | **string**
| | **trainingJobDefinitions.[].enableInterContainerTrafficEncryption**
Optional | **boolean**
| | **trainingJobDefinitions.[].enableManagedSpotTraining**
Optional | **boolean**
| | **trainingJobDefinitions.[].enableNetworkIsolation**
Optional | **boolean**
| | **trainingJobDefinitions.[].hyperParameterRanges**
Optional | **object**
Specifies ranges of integer, continuous, and categorical hyperparameters
that a hyperparameter tuning job searches. The hyperparameter tuning job
launches training jobs with hyperparameter values within these ranges to
find the combination of values that result in the training job with the best
performance as measured by the objective metric of the hyperparameter tuning
job.


The maximum number of items specified for Array Members refers to the maximum
number of hyperparameters for each range and also the maximum for the hyperparameter
tuning job itself. That is, the sum of the number of hyperparameters for
all the ranges can’t exceed the maximum number specified. | | **trainingJobDefinitions.[].hyperParameterRanges.autoParameters**
Optional | **array**
| | **trainingJobDefinitions.[].hyperParameterRanges.autoParameters.[]**
Required | **object**
The name and an example value of the hyperparameter that you want to use in Autotune. If Automatic model tuning (AMT) determines that your hyperparameter is eligible for Autotune, an optimal hyperparameter range is selected for you. || **trainingJobDefinitions.[].hyperParameterRanges.autoParameters.[].name**
Optional | **string**
| | **trainingJobDefinitions.[].hyperParameterRanges.autoParameters.[].valueHint**
Optional | **string**
| | **trainingJobDefinitions.[].hyperParameterRanges.categoricalParameterRanges**
Optional | **array**
| | **trainingJobDefinitions.[].hyperParameterRanges.categoricalParameterRanges.[]**
Required | **object**
A list of categorical hyperparameters to tune. || **trainingJobDefinitions.[].hyperParameterRanges.categoricalParameterRanges.[].name**
Optional | **string**
| | **trainingJobDefinitions.[].hyperParameterRanges.categoricalParameterRanges.[].values**
Optional | **array**
| | **trainingJobDefinitions.[].hyperParameterRanges.categoricalParameterRanges.[].values.[]**
Required | **string**
|| **trainingJobDefinitions.[].hyperParameterRanges.continuousParameterRanges**
Optional | **array**
| | **trainingJobDefinitions.[].hyperParameterRanges.continuousParameterRanges.[]**
Required | **object**
A list of continuous hyperparameters to tune. || **trainingJobDefinitions.[].hyperParameterRanges.continuousParameterRanges.[].maxValue**
Optional | **string**
| | **trainingJobDefinitions.[].hyperParameterRanges.continuousParameterRanges.[].minValue**
Optional | **string**
| | **trainingJobDefinitions.[].hyperParameterRanges.continuousParameterRanges.[].name**
Optional | **string**
| | **trainingJobDefinitions.[].hyperParameterRanges.continuousParameterRanges.[].scalingType**
Optional | **string**
| | **trainingJobDefinitions.[].hyperParameterRanges.integerParameterRanges**
Optional | **array**
| | **trainingJobDefinitions.[].hyperParameterRanges.integerParameterRanges.[]**
Required | **object**
For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches. || **trainingJobDefinitions.[].hyperParameterRanges.integerParameterRanges.[].maxValue**
Optional | **string**
| | **trainingJobDefinitions.[].hyperParameterRanges.integerParameterRanges.[].minValue**
Optional | **string**
| | **trainingJobDefinitions.[].hyperParameterRanges.integerParameterRanges.[].name**
Optional | **string**
| | **trainingJobDefinitions.[].hyperParameterRanges.integerParameterRanges.[].scalingType**
Optional | **string**
| | **trainingJobDefinitions.[].inputDataConfig**
Optional | **array**
| | **trainingJobDefinitions.[].inputDataConfig.[]**
Required | **object**
A channel is a named input source that training algorithms can consume. || **trainingJobDefinitions.[].inputDataConfig.[].channelName**
Optional | **string**
| | **trainingJobDefinitions.[].inputDataConfig.[].compressionType**
Optional | **string**
| | **trainingJobDefinitions.[].inputDataConfig.[].contentType**
Optional | **string**
| | **trainingJobDefinitions.[].inputDataConfig.[].dataSource**
Optional | **object**
Describes the location of the channel data. | | **trainingJobDefinitions.[].inputDataConfig.[].dataSource.fileSystemDataSource**
Optional | **object**
Specifies a file system data source for a channel. | | **trainingJobDefinitions.[].inputDataConfig.[].dataSource.fileSystemDataSource.directoryPath**
Optional | **string**
| | **trainingJobDefinitions.[].inputDataConfig.[].dataSource.fileSystemDataSource.fileSystemAccessMode**
Optional | **string**
| | **trainingJobDefinitions.[].inputDataConfig.[].dataSource.fileSystemDataSource.fileSystemID**
Optional | **string**
| | **trainingJobDefinitions.[].inputDataConfig.[].dataSource.fileSystemDataSource.fileSystemType**
Optional | **string**
| | **trainingJobDefinitions.[].inputDataConfig.[].dataSource.s3DataSource**
Optional | **object**
Describes the S3 data source.


Your input bucket must be in the same Amazon Web Services region as your
training job. | | **trainingJobDefinitions.[].inputDataConfig.[].dataSource.s3DataSource.attributeNames**
Optional | **array**
| | **trainingJobDefinitions.[].inputDataConfig.[].dataSource.s3DataSource.attributeNames.[]**
Required | **string**
|| **trainingJobDefinitions.[].inputDataConfig.[].dataSource.s3DataSource.instanceGroupNames**
Optional | **array**
| | **trainingJobDefinitions.[].inputDataConfig.[].dataSource.s3DataSource.instanceGroupNames.[]**
Required | **string**
|| **trainingJobDefinitions.[].inputDataConfig.[].dataSource.s3DataSource.s3DataDistributionType**
Optional | **string**
| | **trainingJobDefinitions.[].inputDataConfig.[].dataSource.s3DataSource.s3DataType**
Optional | **string**
| | **trainingJobDefinitions.[].inputDataConfig.[].dataSource.s3DataSource.s3URI**
Optional | **string**
| | **trainingJobDefinitions.[].inputDataConfig.[].inputMode**
Optional | **string**
The training input mode that the algorithm supports. For more information
about input modes, see Algorithms (https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html).


Pipe mode


If an algorithm supports Pipe mode, Amazon SageMaker streams data directly
from Amazon S3 to the container.


File mode


If an algorithm supports File mode, SageMaker downloads the training data
from S3 to the provisioned ML storage volume, and mounts the directory to
the Docker volume for the training container.


You must provision the ML storage volume with sufficient capacity to accommodate
the data downloaded from S3. In addition to the training data, the ML storage
volume also stores the output model. The algorithm container uses the ML
storage volume to also store intermediate information, if any.


For distributed algorithms, training data is distributed uniformly. Your
training duration is predictable if the input data objects sizes are approximately
the same. SageMaker does not split the files any further for model training.
If the object sizes are skewed, training won’t be optimal as the data distribution
is also skewed when one host in a training cluster is overloaded, thus becoming
a bottleneck in training.


FastFile mode


If an algorithm supports FastFile mode, SageMaker streams data directly from
S3 to the container with no code changes, and provides file system access
to the data. Users can author their training script to interact with these
files as if they were stored on disk.


FastFile mode works best when the data is read sequentially. Augmented manifest
files aren’t supported. The startup time is lower when there are fewer files
in the S3 bucket provided. | | **trainingJobDefinitions.[].inputDataConfig.[].recordWrapperType**
Optional | **string**
| | **trainingJobDefinitions.[].inputDataConfig.[].shuffleConfig**
Optional | **object**
A configuration for a shuffle option for input data in a channel. If you
use S3Prefix for S3DataType, the results of the S3 key prefix matches are
shuffled. If you use ManifestFile, the order of the S3 object references
in the ManifestFile is shuffled. If you use AugmentedManifestFile, the order
of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling
order is determined using the Seed value.


For Pipe input mode, when ShuffleConfig is specified shuffling is done at
the start of every epoch. With large datasets, this ensures that the order
of the training data is different for each epoch, and it helps reduce bias
and possible overfitting. In a multi-node training job when ShuffleConfig
is combined with S3DataDistributionType of ShardedByS3Key, the data is shuffled
across nodes so that the content sent to a particular node on the first epoch
might be sent to a different node on the second epoch. | | **trainingJobDefinitions.[].inputDataConfig.[].shuffleConfig.seed**
Optional | **integer**
| | **trainingJobDefinitions.[].outputDataConfig**
Optional | **object**
Provides information about how to store model training results (model artifacts). | | **trainingJobDefinitions.[].outputDataConfig.compressionType**
Optional | **string**
| | **trainingJobDefinitions.[].outputDataConfig.kmsKeyID**
Optional | **string**
| | **trainingJobDefinitions.[].outputDataConfig.s3OutputPath**
Optional | **string**
| | **trainingJobDefinitions.[].resourceConfig**
Optional | **object**
Describes the resources, including machine learning (ML) compute instances
and ML storage volumes, to use for model training. | | **trainingJobDefinitions.[].resourceConfig.instanceCount**
Optional | **integer**
| | **trainingJobDefinitions.[].resourceConfig.instanceGroups**
Optional | **array**
| | **trainingJobDefinitions.[].resourceConfig.instanceGroups.[]**
Required | **object**
Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html) API, you can configure multiple instance groups . || **trainingJobDefinitions.[].resourceConfig.instanceGroups.[].instanceCount**
Optional | **integer**
| | **trainingJobDefinitions.[].resourceConfig.instanceGroups.[].instanceGroupName**
Optional | **string**
| | **trainingJobDefinitions.[].resourceConfig.instanceGroups.[].instanceType**
Optional | **string**
| | **trainingJobDefinitions.[].resourceConfig.instanceType**
Optional | **string**
| | **trainingJobDefinitions.[].resourceConfig.keepAlivePeriodInSeconds**
Optional | **integer**
Optional. Customer requested period in seconds for which the Training cluster
is kept alive after the job is finished. | | **trainingJobDefinitions.[].resourceConfig.volumeKMSKeyID**
Optional | **string**
| | **trainingJobDefinitions.[].resourceConfig.volumeSizeInGB**
Optional | **integer**
| | **trainingJobDefinitions.[].retryStrategy**
Optional | **object**
The retry strategy to use when a training job fails due to an InternalServerError.
RetryStrategy is specified as part of the CreateTrainingJob and CreateHyperParameterTuningJob
requests. You can add the StoppingCondition parameter to the request to limit
the training time for the complete job. | | **trainingJobDefinitions.[].retryStrategy.maximumRetryAttempts**
Optional | **integer**
| | **trainingJobDefinitions.[].roleARN**
Optional | **string**
| | **trainingJobDefinitions.[].staticHyperParameters**
Optional | **object**
| | **trainingJobDefinitions.[].stoppingCondition**
Optional | **object**
Specifies a limit to how long a model training job or model compilation job
can run. It also specifies how long a managed spot training job has to complete.
When the job reaches the time limit, SageMaker ends the training or compilation
job. Use this API to cap model training costs.


To stop a training job, 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.


The training algorithms provided by SageMaker automatically save the intermediate
results of a model training job when possible. This attempt to save artifacts
is only a best effort case as model might not be in a state from which it
can be saved. For example, if training has just started, the model might
not be ready to save. When saved, this intermediate data is a valid model
artifact. You can use it to create a model with CreateModel.


The Neural Topic Model (NTM) currently does not support saving intermediate
model artifacts. When training NTMs, make sure that the maximum runtime is
sufficient for the training job to complete. | | **trainingJobDefinitions.[].stoppingCondition.maxPendingTimeInSeconds**
Optional | **integer**
Maximum job scheduler pending time in seconds. | | **trainingJobDefinitions.[].stoppingCondition.maxRuntimeInSeconds**
Optional | **integer**
| | **trainingJobDefinitions.[].stoppingCondition.maxWaitTimeInSeconds**
Optional | **integer**
| | **trainingJobDefinitions.[].tuningObjective**
Optional | **object**
Defines the objective metric for a hyperparameter tuning job. Hyperparameter
tuning uses the value of this metric to evaluate the training jobs it launches,
and returns the training job that results in either the highest or lowest
value for this metric, depending on the value you specify for the Type parameter.
If you want to define a custom objective metric, see Define metrics and environment
variables (https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-metrics-variables.html). | | **trainingJobDefinitions.[].tuningObjective.metricName**
Optional | **string**
| | **trainingJobDefinitions.[].tuningObjective.type_**
Optional | **string**
| | **trainingJobDefinitions.[].vpcConfig**
Optional | **object**
Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs,
hosted models, and compute resources have access to. You can control access
to and from your resources by configuring a VPC. For more information, see
Give SageMaker Access to Resources in your Amazon VPC (https://docs.aws.amazon.com/sagemaker/latest/dg/infrastructure-give-access.html). | | **trainingJobDefinitions.[].vpcConfig.securityGroupIDs**
Optional | **array**
| | **trainingJobDefinitions.[].vpcConfig.securityGroupIDs.[]**
Required | **string**
|| **trainingJobDefinitions.[].vpcConfig.subnets**
Optional | **array**
| | **trainingJobDefinitions.[].vpcConfig.subnets.[]**
Required | **string**
|| **warmStartConfig**
Optional | **object**
Specifies the configuration for starting the hyperparameter tuning job using
one or more previous tuning jobs as a starting point. The results of previous
tuning jobs are used to inform which combinations of hyperparameters to search
over in the new tuning job.


All training jobs launched by the new hyperparameter tuning job are evaluated
by using the objective metric. If you specify IDENTICAL_DATA_AND_ALGORITHM
as the WarmStartType value for the warm start configuration, the training
job that performs the best in the new tuning job is compared to the best
training jobs from the parent tuning jobs. From these, the training job that
performs the best as measured by the objective metric is returned as the
overall best training job.


All training jobs launched by parent hyperparameter tuning jobs and the new
hyperparameter tuning jobs count against the limit of training jobs for the
tuning job. | | **warmStartConfig.parentHyperParameterTuningJobs**
Optional | **array**
| | **warmStartConfig.parentHyperParameterTuningJobs.[]**
Required | **object**
A previously completed or stopped hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job. || **warmStartConfig.parentHyperParameterTuningJobs.[].hyperParameterTuningJobName**
Optional | **string**
| | **warmStartConfig.warmStartType**
Optional | **string**
|

Status#

ackResourceMetadata: 
  arn: string
  ownerAccountID: string
  region: string
bestTrainingJob: 
  creationTime: string
  failureReason: string
  finalHyperParameterTuningJobObjectiveMetric: 
    metricName: string
    type_: string
    value: number
  objectiveStatus: string
  trainingEndTime: string
  trainingJobARN: string
  trainingJobDefinitionName: string
  trainingJobName: string
  trainingJobStatus: string
  trainingStartTime: string
  tunedHyperParameters: {}
  tuningJobName: string
conditions:
- lastTransitionTime: string
  message: string
  reason: string
  status: string
  type: string
failureReason: string
hyperParameterTuningJobStatus: string
overallBestTrainingJob: 
  creationTime: string
  failureReason: string
  finalHyperParameterTuningJobObjectiveMetric: 
    metricName: string
    type_: string
    value: number
  objectiveStatus: string
  trainingEndTime: string
  trainingJobARN: string
  trainingJobDefinitionName: string
  trainingJobName: string
  trainingJobStatus: string
  trainingStartTime: string
  tunedHyperParameters: {}
  tuningJobName: string