--- title: Getting Started with Datadog description: Datadog, the leading service for cloud-scale monitoring. breadcrumbs: Docs > Infrastructure > Datadog Resource Catalog --- # aws_sagemaker_hyperparametertuningjob{% #aws_sagemaker_hyperparametertuningjob %} ## `account_id`{% #account_id %} **Type**: `STRING` ## `autotune`{% #autotune %} **Type**: `STRUCT`**Provider name**: `Autotune`**Description**: A flag to indicate if autotune is enabled for the hyperparameter tuning job. - `mode`**Type**: `STRING`**Provider name**: `Mode`**Description**: Set `Mode` to `Enabled` if you want to use Autotune. ## `best_training_job`{% #best_training_job %} **Type**: `STRUCT`**Provider name**: `BestTrainingJob`**Description**: A [TrainingJobSummary](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TrainingJobSummary.html) object that describes the training job that completed with the best current [HyperParameterTuningJobObjective](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTuningJobObjective.html). - `creation_time`**Type**: `TIMESTAMP`**Provider name**: `CreationTime`**Description**: The date and time that the training job was created. - `failure_reason`**Type**: `STRING`**Provider name**: `FailureReason`**Description**: The reason that the training job failed. - `final_hyper_parameter_tuning_job_objective_metric`**Type**: `STRUCT`**Provider name**: `FinalHyperParameterTuningJobObjectiveMetric`**Description**: The [FinalHyperParameterTuningJobObjectiveMetric](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_FinalHyperParameterTuningJobObjectiveMetric.html) object that specifies the value of the objective metric of the tuning job that launched this training job. - `metric_name`**Type**: `STRING`**Provider name**: `MetricName`**Description**: The name of the objective metric. For SageMaker built-in algorithms, metrics are defined per algorithm. See the [metrics for XGBoost](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost-tuning.html) as an example. You can also use a custom algorithm for training and define your own metrics. For more information, see [Define metrics and environment variables](https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-metrics-variables.html). - `type`**Type**: `STRING`**Provider name**: `Type`**Description**: Select if you want to minimize or maximize the objective metric during hyperparameter tuning. - `value`**Type**: `FLOAT`**Provider name**: `Value`**Description**: The value of the objective metric. - `objective_status`**Type**: `STRING`**Provider name**: `ObjectiveStatus`**Description**: The status of the objective metric for the training job: - Succeeded: The final objective metric for the training job was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process. - Pending: The training job is in progress and evaluation of its final objective metric is pending. - Failed: The final objective metric for the training job was not evaluated, and was not used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric. - `training_end_time`**Type**: `TIMESTAMP`**Provider name**: `TrainingEndTime`**Description**: Specifies the time when the training job ends on training instances. You are billed for the time interval between the value of `TrainingStartTime` and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure. - `training_job_arn`**Type**: `STRING`**Provider name**: `TrainingJobArn`**Description**: The Amazon Resource Name (ARN) of the training job. - `training_job_definition_name`**Type**: `STRING`**Provider name**: `TrainingJobDefinitionName`**Description**: The training job definition name. - `training_job_name`**Type**: `STRING`**Provider name**: `TrainingJobName`**Description**: The name of the training job. - `training_job_status`**Type**: `STRING`**Provider name**: `TrainingJobStatus`**Description**: The status of the training job. - `training_start_time`**Type**: `TIMESTAMP`**Provider name**: `TrainingStartTime`**Description**: The date and time that the training job started. - `tuned_hyper_parameters`**Type**: `MAP_STRING_STRING`**Provider name**: `TunedHyperParameters`**Description**: A list of the hyperparameters for which you specified ranges to search. - `tuning_job_name`**Type**: `STRING`**Provider name**: `TuningJobName`**Description**: The HyperParameter tuning job that launched the training job. ## `consumed_resources`{% #consumed_resources %} **Type**: `STRUCT`**Provider name**: `ConsumedResources` - `runtime_in_seconds`**Type**: `INT32`**Provider name**: `RuntimeInSeconds`**Description**: The wall clock runtime in seconds used by your hyperparameter tuning job. ## `creation_time`{% #creation_time %} **Type**: `TIMESTAMP`**Provider name**: `CreationTime`**Description**: The date and time that the tuning job started. ## `failure_reason`{% #failure_reason %} **Type**: `STRING`**Provider name**: `FailureReason`**Description**: If the tuning job failed, the reason it failed. ## `hyper_parameter_tuning_end_time`{% #hyper_parameter_tuning_end_time %} **Type**: `TIMESTAMP`**Provider name**: `HyperParameterTuningEndTime`**Description**: The date and time that the tuning job ended. ## `hyper_parameter_tuning_job_arn`{% #hyper_parameter_tuning_job_arn %} **Type**: `STRING`**Provider name**: `HyperParameterTuningJobArn`**Description**: The Amazon Resource Name (ARN) of the tuning job. ## `hyper_parameter_tuning_job_config`{% #hyper_parameter_tuning_job_config %} **Type**: `STRUCT`**Provider name**: `HyperParameterTuningJobConfig`**Description**: The [HyperParameterTuningJobConfig](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTuningJobConfig.html) object that specifies the configuration of the tuning job. - `hyper_parameter_tuning_job_objective`**Type**: `STRUCT`**Provider name**: `HyperParameterTuningJobObjective`**Description**: The [HyperParameterTuningJobObjective](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTuningJobObjective.html) specifies the objective metric used to evaluate the performance of training jobs launched by this tuning job. - `metric_name`**Type**: `STRING`**Provider name**: `MetricName`**Description**: The name of the metric to use for the objective metric. - `type`**Type**: `STRING`**Provider name**: `Type`**Description**: Whether to minimize or maximize the objective metric. - `parameter_ranges`**Type**: `STRUCT`**Provider name**: `ParameterRanges`**Description**: The [ParameterRanges](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ParameterRanges.html) object that specifies the ranges of hyperparameters that this tuning job searches over to find the optimal configuration for the highest model performance against your chosen objective metric. - `auto_parameters`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `AutoParameters`**Description**: A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job. - `name`**Type**: `STRING`**Provider name**: `Name`**Description**: The name of the hyperparameter to optimize using Autotune. - `value_hint`**Type**: `STRING`**Provider name**: `ValueHint`**Description**: An example value of the hyperparameter to optimize using Autotune. - `categorical_parameter_ranges`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `CategoricalParameterRanges`**Description**: The array of [CategoricalParameterRange](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CategoricalParameterRange.html) objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches. - `name`**Type**: `STRING`**Provider name**: `Name`**Description**: The name of the categorical hyperparameter to tune. - `values`**Type**: `UNORDERED_LIST_STRING`**Provider name**: `Values`**Description**: A list of the categories for the hyperparameter. - `continuous_parameter_ranges`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `ContinuousParameterRanges`**Description**: The array of [ContinuousParameterRange](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ContinuousParameterRange.html) objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches. - `max_value`**Type**: `STRING`**Provider name**: `MaxValue`**Description**: The maximum value for the hyperparameter. The tuning job uses floating-point values between `MinValue` value and this value for tuning. - `min_value`**Type**: `STRING`**Provider name**: `MinValue`**Description**: The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and `MaxValue`for tuning. - `name`**Type**: `STRING`**Provider name**: `Name`**Description**: The name of the continuous hyperparameter to tune. - `scaling_type`**Type**: `STRING`**Provider name**: `ScalingType`**Description**: The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see [Hyperparameter Scaling](https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-ranges.html#scaling-type). One of the following values: {% dl %} {% dt %} Auto {% /dt %} {% dd %} SageMaker hyperparameter tuning chooses the best scale for the hyperparameter. {% /dd %} {% dt %} Linear {% /dt %} {% dd %} Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale. {% /dd %} {% dt %} Logarithmic {% /dt %} {% dd %} Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale. Logarithmic scaling works only for ranges that have only values greater than 0. {% /dd %} {% dt %} ReverseLogarithmic {% /dt %} {% dd %} Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale. Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0. {% /dd %} {% /dl %} - `integer_parameter_ranges`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `IntegerParameterRanges`**Description**: The array of [IntegerParameterRange](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_IntegerParameterRange.html) objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches. - `max_value`**Type**: `STRING`**Provider name**: `MaxValue`**Description**: The maximum value of the hyperparameter to search. - `min_value`**Type**: `STRING`**Provider name**: `MinValue`**Description**: The minimum value of the hyperparameter to search. - `name`**Type**: `STRING`**Provider name**: `Name`**Description**: The name of the hyperparameter to search. - `scaling_type`**Type**: `STRING`**Provider name**: `ScalingType`**Description**: The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see [Hyperparameter Scaling](https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-ranges.html#scaling-type). One of the following values: {% dl %} {% dt %} Auto {% /dt %} {% dd %} SageMaker hyperparameter tuning chooses the best scale for the hyperparameter. {% /dd %} {% dt %} Linear {% /dt %} {% dd %} Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale. {% /dd %} {% dt %} Logarithmic {% /dt %} {% dd %} Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale. Logarithmic scaling works only for ranges that have only values greater than 0. {% /dd %} {% /dl %} - `random_seed`**Type**: `INT32`**Provider name**: `RandomSeed`**Description**: A value used to initialize a pseudo-random number generator. Setting a random seed and using the same seed later for the same tuning job will allow hyperparameter optimization to find more a consistent hyperparameter configuration between the two runs. - `resource_limits`**Type**: `STRUCT`**Provider name**: `ResourceLimits`**Description**: The [ResourceLimits](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ResourceLimits.html) object that specifies the maximum number of training and parallel training jobs that can be used for this hyperparameter tuning job. - `max_number_of_training_jobs`**Type**: `INT32`**Provider name**: `MaxNumberOfTrainingJobs`**Description**: The maximum number of training jobs that a hyperparameter tuning job can launch. - `max_parallel_training_jobs`**Type**: `INT32`**Provider name**: `MaxParallelTrainingJobs`**Description**: The maximum number of concurrent training jobs that a hyperparameter tuning job can launch. - `max_runtime_in_seconds`**Type**: `INT32`**Provider name**: `MaxRuntimeInSeconds`**Description**: The maximum time in seconds that a hyperparameter tuning job can run. - `strategy`**Type**: `STRING`**Provider name**: `Strategy`**Description**: Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job it launches. For information about search strategies, see [How Hyperparameter Tuning Works](https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-how-it-works.html). - `strategy_config`**Type**: `STRUCT`**Provider name**: `StrategyConfig`**Description**: The configuration for the `Hyperband` optimization strategy. This parameter should be provided only if `Hyperband` is selected as the strategy for `HyperParameterTuningJobConfig`. - `hyperband_strategy_config`**Type**: `STRUCT`**Provider name**: `HyperbandStrategyConfig`**Description**: The configuration for the object that specifies the `Hyperband` strategy. This parameter is only supported for the `Hyperband` selection for `Strategy` within the `HyperParameterTuningJobConfig` API. - `max_resource`**Type**: `INT32`**Provider name**: `MaxResource`**Description**: The maximum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job. Once a job reaches the `MaxResource` value, it is stopped. If a value for `MaxResource` is not provided, and `Hyperband` is selected as the hyperparameter tuning strategy, `HyperbandTraining` attempts to infer `MaxResource` from the following keys (if present) in [StaticsHyperParameters](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTrainingJobDefinition.html#sagemaker-Type-HyperParameterTrainingJobDefinition-StaticHyperParameters): - `epochs` - `numepochs` - `n-epochs` - `n_epochs` - `num_epochs` If `HyperbandStrategyConfig` is unable to infer a value for `MaxResource`, it generates a validation error. The maximum value is 20,000 epochs. All metrics that correspond to an objective metric are used to derive [early stopping decisions](https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-early-stopping.html). For [distributed](https://docs.aws.amazon.com/sagemaker/latest/dg/distributed-training.html) training jobs, ensure that duplicate metrics are not printed in the logs across the individual nodes in a training job. If multiple nodes are publishing duplicate or incorrect metrics, training jobs may make an incorrect stopping decision and stop the job prematurely. - `min_resource`**Type**: `INT32`**Provider name**: `MinResource`**Description**: The minimum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job. If the value for `MinResource` has not been reached, the training job is not stopped by `Hyperband`. - `training_job_early_stopping_type`**Type**: `STRING`**Provider name**: `TrainingJobEarlyStoppingType`**Description**: Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job. Because the `Hyperband` strategy has its own advanced internal early stopping mechanism, `TrainingJobEarlyStoppingType` must be `OFF` to use `Hyperband`. This parameter can take on one of the following values (the default value is `OFF`): {% dl %} {% dt %} OFF {% /dt %} {% dd %} Training jobs launched by the hyperparameter tuning job do not use early stopping. {% /dd %} {% dt %} AUTO {% /dt %} {% dd %} SageMaker stops training jobs launched by the hyperparameter tuning job when they are unlikely to perform better than previously completed training jobs. For more information, see [Stop Training Jobs Early](https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-early-stopping.html). {% /dd %} {% /dl %} - `tuning_job_completion_criteria`**Type**: `STRUCT`**Provider name**: `TuningJobCompletionCriteria`**Description**: The tuning job's completion criteria. - `best_objective_not_improving`**Type**: `STRUCT`**Provider name**: `BestObjectiveNotImproving`**Description**: A flag to stop your hyperparameter tuning job if model performance fails to improve as evaluated against an objective function. - `max_number_of_training_jobs_not_improving`**Type**: `INT32`**Provider name**: `MaxNumberOfTrainingJobsNotImproving`**Description**: The number of training jobs that have failed to improve model performance by 1% or greater over prior training jobs as evaluated against an objective function. - `convergence_detected`**Type**: `STRUCT`**Provider name**: `ConvergenceDetected`**Description**: A flag to top your hyperparameter tuning job if automatic model tuning (AMT) has detected that your model has converged as evaluated against your objective function. - `complete_on_convergence`**Type**: `STRING`**Provider name**: `CompleteOnConvergence`**Description**: A flag to stop a tuning job once AMT has detected that the job has converged. - `target_objective_metric_value`**Type**: `FLOAT`**Provider name**: `TargetObjectiveMetricValue`**Description**: The value of the objective metric. ## `hyper_parameter_tuning_job_name`{% #hyper_parameter_tuning_job_name %} **Type**: `STRING`**Provider name**: `HyperParameterTuningJobName`**Description**: The name of the hyperparameter tuning job. ## `hyper_parameter_tuning_job_status`{% #hyper_parameter_tuning_job_status %} **Type**: `STRING`**Provider name**: `HyperParameterTuningJobStatus`**Description**: The status of the tuning job. ## `last_modified_time`{% #last_modified_time %} **Type**: `TIMESTAMP`**Provider name**: `LastModifiedTime`**Description**: The date and time that the status of the tuning job was modified. ## `objective_status_counters`{% #objective_status_counters %} **Type**: `STRUCT`**Provider name**: `ObjectiveStatusCounters`**Description**: The [ObjectiveStatusCounters](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ObjectiveStatusCounters.html) object that specifies the number of training jobs, categorized by the status of their final objective metric, that this tuning job launched. - `failed`**Type**: `INT32`**Provider name**: `Failed`**Description**: The number of training jobs whose final objective metric was not evaluated and used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric. - `pending`**Type**: `INT32`**Provider name**: `Pending`**Description**: The number of training jobs that are in progress and pending evaluation of their final objective metric. - `succeeded`**Type**: `INT32`**Provider name**: `Succeeded`**Description**: The number of training jobs whose final objective metric was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process. ## `overall_best_training_job`{% #overall_best_training_job %} **Type**: `STRUCT`**Provider name**: `OverallBestTrainingJob`**Description**: If the hyperparameter tuning job is an warm start tuning job with a `WarmStartType` of `IDENTICAL_DATA_AND_ALGORITHM`, this is the [TrainingJobSummary](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TrainingJobSummary.html) for the training job with the best objective metric value of all training jobs launched by this tuning job and all parent jobs specified for the warm start tuning job. - `creation_time`**Type**: `TIMESTAMP`**Provider name**: `CreationTime`**Description**: The date and time that the training job was created. - `failure_reason`**Type**: `STRING`**Provider name**: `FailureReason`**Description**: The reason that the training job failed. - `final_hyper_parameter_tuning_job_objective_metric`**Type**: `STRUCT`**Provider name**: `FinalHyperParameterTuningJobObjectiveMetric`**Description**: The [FinalHyperParameterTuningJobObjectiveMetric](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_FinalHyperParameterTuningJobObjectiveMetric.html) object that specifies the value of the objective metric of the tuning job that launched this training job. - `metric_name`**Type**: `STRING`**Provider name**: `MetricName`**Description**: The name of the objective metric. For SageMaker built-in algorithms, metrics are defined per algorithm. See the [metrics for XGBoost](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost-tuning.html) as an example. You can also use a custom algorithm for training and define your own metrics. For more information, see [Define metrics and environment variables](https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-metrics-variables.html). - `type`**Type**: `STRING`**Provider name**: `Type`**Description**: Select if you want to minimize or maximize the objective metric during hyperparameter tuning. - `value`**Type**: `FLOAT`**Provider name**: `Value`**Description**: The value of the objective metric. - `objective_status`**Type**: `STRING`**Provider name**: `ObjectiveStatus`**Description**: The status of the objective metric for the training job: - Succeeded: The final objective metric for the training job was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process. - Pending: The training job is in progress and evaluation of its final objective metric is pending. - Failed: The final objective metric for the training job was not evaluated, and was not used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric. - `training_end_time`**Type**: `TIMESTAMP`**Provider name**: `TrainingEndTime`**Description**: Specifies the time when the training job ends on training instances. You are billed for the time interval between the value of `TrainingStartTime` and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure. - `training_job_arn`**Type**: `STRING`**Provider name**: `TrainingJobArn`**Description**: The Amazon Resource Name (ARN) of the training job. - `training_job_definition_name`**Type**: `STRING`**Provider name**: `TrainingJobDefinitionName`**Description**: The training job definition name. - `training_job_name`**Type**: `STRING`**Provider name**: `TrainingJobName`**Description**: The name of the training job. - `training_job_status`**Type**: `STRING`**Provider name**: `TrainingJobStatus`**Description**: The status of the training job. - `training_start_time`**Type**: `TIMESTAMP`**Provider name**: `TrainingStartTime`**Description**: The date and time that the training job started. - `tuned_hyper_parameters`**Type**: `MAP_STRING_STRING`**Provider name**: `TunedHyperParameters`**Description**: A list of the hyperparameters for which you specified ranges to search. - `tuning_job_name`**Type**: `STRING`**Provider name**: `TuningJobName`**Description**: The HyperParameter tuning job that launched the training job. ## `tags`{% #tags %} **Type**: `UNORDERED_LIST_STRING` ## `training_job_definition`{% #training_job_definition %} **Type**: `STRUCT`**Provider name**: `TrainingJobDefinition`**Description**: The [HyperParameterTrainingJobDefinition](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTrainingJobDefinition.html) object that specifies the definition of the training jobs that this tuning job launches. - `algorithm_specification`**Type**: `STRUCT`**Provider name**: `AlgorithmSpecification`**Description**: The [HyperParameterAlgorithmSpecification](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterAlgorithmSpecification.html) object that specifies the resource algorithm to use for the training jobs that the tuning job launches. - `algorithm_name`**Type**: `STRING`**Provider name**: `AlgorithmName`**Description**: The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for `TrainingImage`. - `metric_definitions`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `MetricDefinitions`**Description**: An array of [MetricDefinition](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_MetricDefinition.html) objects that specify the metrics that the algorithm emits. - `name`**Type**: `STRING`**Provider name**: `Name`**Description**: The name of the metric. - `regex`**Type**: `STRING`**Provider name**: `Regex`**Description**: A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see [Defining metrics and environment variables](https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-metrics-variables.html). - `training_image`**Type**: `STRING`**Provider name**: `TrainingImage`**Description**: The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see [Algorithms Provided by Amazon SageMaker: Common Parameters](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html). SageMaker supports both `registry/repository[:tag]` and `registry/repository[@digest]` image path formats. For more information, see [Using Your Own Algorithms with Amazon SageMaker](https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html). - `training_input_mode`**Type**: `STRING`**Provider name**: `TrainingInputMode` - `checkpoint_config`**Type**: `STRUCT`**Provider name**: `CheckpointConfig` - `local_path`**Type**: `STRING`**Provider name**: `LocalPath`**Description**: (Optional) The local directory where checkpoints are written. The default directory is `/opt/ml/checkpoints/`. - `s3_uri`**Type**: `STRING`**Provider name**: `S3Uri`**Description**: Identifies the S3 path where you want SageMaker to store checkpoints. For example, `s3://bucket-name/key-name-prefix`. - `definition_name`**Type**: `STRING`**Provider name**: `DefinitionName`**Description**: The job definition name. - `enable_inter_container_traffic_encryption`**Type**: `BOOLEAN`**Provider name**: `EnableInterContainerTrafficEncryption`**Description**: 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. - `enable_managed_spot_training`**Type**: `BOOLEAN`**Provider name**: `EnableManagedSpotTraining`**Description**: A Boolean indicating whether managed spot training is enabled (`True`) or not (`False`). - `enable_network_isolation`**Type**: `BOOLEAN`**Provider name**: `EnableNetworkIsolation`**Description**: 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, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access. - `environment`**Type**: `MAP_STRING_STRING`**Provider name**: `Environment`**Description**: An environment variable that you can pass into the SageMaker [CreateTrainingJob](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html) API. You can use an existing [environment variable from the training container](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html#sagemaker-CreateTrainingJob-request-Environment) or use your own. See [Define metrics and variables](https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-metrics-variables.html) for more information.The maximum number of items specified for `Map Entries` refers to the maximum number of environment variables for each `TrainingJobDefinition` and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of environment variables for all the training job definitions can't exceed the maximum number specified. - `hyper_parameter_ranges`**Type**: `STRUCT`**Provider name**: `HyperParameterRanges` - `auto_parameters`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `AutoParameters`**Description**: A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job. - `name`**Type**: `STRING`**Provider name**: `Name`**Description**: The name of the hyperparameter to optimize using Autotune. - `value_hint`**Type**: `STRING`**Provider name**: `ValueHint`**Description**: An example value of the hyperparameter to optimize using Autotune. - `categorical_parameter_ranges`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `CategoricalParameterRanges`**Description**: The array of [CategoricalParameterRange](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CategoricalParameterRange.html) objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches. - `name`**Type**: `STRING`**Provider name**: `Name`**Description**: The name of the categorical hyperparameter to tune. - `values`**Type**: `UNORDERED_LIST_STRING`**Provider name**: `Values`**Description**: A list of the categories for the hyperparameter. - `continuous_parameter_ranges`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `ContinuousParameterRanges`**Description**: The array of [ContinuousParameterRange](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ContinuousParameterRange.html) objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches. - `max_value`**Type**: `STRING`**Provider name**: `MaxValue`**Description**: The maximum value for the hyperparameter. The tuning job uses floating-point values between `MinValue` value and this value for tuning. - `min_value`**Type**: `STRING`**Provider name**: `MinValue`**Description**: The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and `MaxValue`for tuning. - `name`**Type**: `STRING`**Provider name**: `Name`**Description**: The name of the continuous hyperparameter to tune. - `scaling_type`**Type**: `STRING`**Provider name**: `ScalingType`**Description**: The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see [Hyperparameter Scaling](https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-ranges.html#scaling-type). One of the following values: {% dl %} {% dt %} Auto {% /dt %} {% dd %} SageMaker hyperparameter tuning chooses the best scale for the hyperparameter. {% /dd %} {% dt %} Linear {% /dt %} {% dd %} Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale. {% /dd %} {% dt %} Logarithmic {% /dt %} {% dd %} Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale. Logarithmic scaling works only for ranges that have only values greater than 0. {% /dd %} {% dt %} ReverseLogarithmic {% /dt %} {% dd %} Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale. Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0. {% /dd %} {% /dl %} - `integer_parameter_ranges`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `IntegerParameterRanges`**Description**: The array of [IntegerParameterRange](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_IntegerParameterRange.html) objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches. - `max_value`**Type**: `STRING`**Provider name**: `MaxValue`**Description**: The maximum value of the hyperparameter to search. - `min_value`**Type**: `STRING`**Provider name**: `MinValue`**Description**: The minimum value of the hyperparameter to search. - `name`**Type**: `STRING`**Provider name**: `Name`**Description**: The name of the hyperparameter to search. - `scaling_type`**Type**: `STRING`**Provider name**: `ScalingType`**Description**: The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see [Hyperparameter Scaling](https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-ranges.html#scaling-type). One of the following values: {% dl %} {% dt %} Auto {% /dt %} {% dd %} SageMaker hyperparameter tuning chooses the best scale for the hyperparameter. {% /dd %} {% dt %} Linear {% /dt %} {% dd %} Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale. {% /dd %} {% dt %} Logarithmic {% /dt %} {% dd %} Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale. Logarithmic scaling works only for ranges that have only values greater than 0. {% /dd %} {% /dl %} - `hyper_parameter_tuning_resource_config`**Type**: `STRUCT`**Provider name**: `HyperParameterTuningResourceConfig`**Description**: The configuration for the hyperparameter tuning resources, including the compute instances and storage volumes, used for training jobs launched by the tuning job. By default, storage volumes hold model artifacts and incremental states. Choose `File` for `TrainingInputMode` in the `AlgorithmSpecification` parameter to additionally store training data in the storage volume (optional). - `allocation_strategy`**Type**: `STRING`**Provider name**: `AllocationStrategy`**Description**: The strategy that determines the order of preference for resources specified in `InstanceConfigs` used in hyperparameter optimization. - `instance_configs`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `InstanceConfigs`**Description**: A list containing the configuration(s) for one or more resources for processing hyperparameter jobs. These resources include compute instances and storage volumes to use in model training jobs launched by hyperparameter tuning jobs. The `AllocationStrategy` controls the order in which multiple configurations provided in `InstanceConfigs` are used.If you only want to use a single instance configuration inside the `HyperParameterTuningResourceConfig` API, do not provide a value for `InstanceConfigs`. Instead, use `InstanceType`, `VolumeSizeInGB` and `InstanceCount`. If you use `InstanceConfigs`, do not provide values for `InstanceType`, `VolumeSizeInGB` or `InstanceCount`. - `instance_count`**Type**: `INT32`**Provider name**: `InstanceCount`**Description**: The number of instances of the type specified by `InstanceType`. Choose an instance count larger than 1 for distributed training algorithms. See [Step 2: Launch a SageMaker Distributed Training Job Using the SageMaker Python SDK](https://docs.aws.amazon.com/sagemaker/latest/dg/data-parallel-use-api.html) for more information. - `instance_type`**Type**: `STRING`**Provider name**: `InstanceType`**Description**: The instance type used for processing of hyperparameter optimization jobs. Choose from general purpose (no GPUs) instance types: ml.m5.xlarge, ml.m5.2xlarge, and ml.m5.4xlarge or compute optimized (no GPUs) instance types: ml.c5.xlarge and ml.c5.2xlarge. For more information about instance types, see [instance type descriptions](https://docs.aws.amazon.com/sagemaker/latest/dg/notebooks-available-instance-types.html). - `volume_size_in_gb`**Type**: `INT32`**Provider name**: `VolumeSizeInGB`**Description**: The volume size in GB of the data to be processed for hyperparameter optimization (optional). - `instance_count`**Type**: `INT32`**Provider name**: `InstanceCount`**Description**: The number of compute instances of type `InstanceType` to use. For [distributed training](https://docs.aws.amazon.com/sagemaker/latest/dg/data-parallel-use-api.html), select a value greater than 1. - `instance_type`**Type**: `STRING`**Provider name**: `InstanceType`**Description**: The instance type used to run hyperparameter optimization tuning jobs. See [descriptions of instance types](https://docs.aws.amazon.com/sagemaker/latest/dg/notebooks-available-instance-types.html) for more information. - `volume_kms_key_id`**Type**: `STRING`**Provider name**: `VolumeKmsKeyId`**Description**: A key used by Amazon Web Services Key Management Service to encrypt data on the storage volume attached to the compute instances used to run the training job. You can use either of the following formats to specify a key. KMS Key ID: `"1234abcd-12ab-34cd-56ef-1234567890ab"` Amazon Resource Name (ARN) of a KMS key: `"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"` Some instances use local storage, which use a [hardware module to encrypt](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ssd-instance-store.html) storage volumes. If you choose one of these instance types, you cannot request a `VolumeKmsKeyId`. For a list of instance types that use local storage, see [instance store volumes](http://aws.amazon.com/releasenotes/host-instance-storage-volumes-table/). For more information about Amazon Web Services Key Management Service, see [KMS encryption](https://docs.aws.amazon.com/sagemaker/latest/dg/sms-security-kms-permissions.html) for more information. - `volume_size_in_gb`**Type**: `INT32`**Provider name**: `VolumeSizeInGB`**Description**: The volume size in GB for the storage volume to be used in processing hyperparameter optimization jobs (optional). These volumes store model artifacts, incremental states and optionally, scratch space for training algorithms. Do not provide a value for this parameter if a value for `InstanceConfigs` is also specified. Some instance types have a fixed total local storage size. If you select one of these instances for training, `VolumeSizeInGB` cannot be greater than this total size. For a list of instance types with local instance storage and their sizes, see [instance store volumes](http://aws.amazon.com/releasenotes/host-instance-storage-volumes-table/).SageMaker supports only the [General Purpose SSD (gp2)](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ebs-volume-types.html) storage volume type. - `input_data_config`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `InputDataConfig`**Description**: An array of [Channel](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_Channel.html) objects that specify the input for the training jobs that the tuning job launches. - `channel_name`**Type**: `STRING`**Provider name**: `ChannelName`**Description**: The name of the channel. - `compression_type`**Type**: `STRING`**Provider name**: `CompressionType`**Description**: If training data is compressed, the compression type. The default value is `None`. `CompressionType` is used only in Pipe input mode. In File mode, leave this field unset or set it to None. - `content_type`**Type**: `STRING`**Provider name**: `ContentType`**Description**: The MIME type of the data. - `data_source`**Type**: `STRUCT`**Provider name**: `DataSource`**Description**: The location of the channel data. - `file_system_data_source`**Type**: `STRUCT`**Provider name**: `FileSystemDataSource`**Description**: The file system that is associated with a channel. - `directory_path`**Type**: `STRING`**Provider name**: `DirectoryPath`**Description**: The full path to the directory to associate with the channel. - `file_system_access_mode`**Type**: `STRING`**Provider name**: `FileSystemAccessMode`**Description**: The access mode of the mount of the directory associated with the channel. A directory can be mounted either in `ro` (read-only) or `rw` (read-write) mode. - `file_system_id`**Type**: `STRING`**Provider name**: `FileSystemId`**Description**: The file system id. - `file_system_type`**Type**: `STRING`**Provider name**: `FileSystemType`**Description**: The file system type. - `s3_data_source`**Type**: `STRUCT`**Provider name**: `S3DataSource`**Description**: The S3 location of the data source that is associated with a channel. - `attribute_names`**Type**: `UNORDERED_LIST_STRING`**Provider name**: `AttributeNames`**Description**: A list of one or more attribute names to use that are found in a specified augmented manifest file. - `hub_access_config`**Type**: `STRUCT`**Provider name**: `HubAccessConfig`**Description**: The configuration for a private hub model reference that points to a SageMaker JumpStart public hub model. - `hub_content_arn`**Type**: `STRING`**Provider name**: `HubContentArn`**Description**: The ARN of your private model hub content. This should be a `ModelReference` resource type that points to a SageMaker JumpStart public hub model. - `instance_group_names`**Type**: `UNORDERED_LIST_STRING`**Provider name**: `InstanceGroupNames`**Description**: A list of names of instance groups that get data from the S3 data source. - `model_access_config`**Type**: `STRUCT`**Provider name**: `ModelAccessConfig` - `accept_eula`**Type**: `BOOLEAN`**Provider name**: `AcceptEula`**Description**: Specifies agreement to the model end-user license agreement (EULA). The `AcceptEula` value must be explicitly defined as `True` in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model. - `s3_data_distribution_type`**Type**: `STRING`**Provider name**: `S3DataDistributionType`**Description**: If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify `FullyReplicated`. If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify `ShardedByS3Key`. If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data. Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms. In distributed training, where you use multiple ML compute EC2 instances, you might choose `ShardedByS3Key`. If the algorithm requires copying training data to the ML storage volume (when `TrainingInputMode` is set to `File`), this copies 1/n of the number of objects. - `s3_data_type`**Type**: `STRING`**Provider name**: `S3DataType`**Description**: If you choose `S3Prefix`, `S3Uri` identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training. If you choose `ManifestFile`, `S3Uri` identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training. If you choose `AugmentedManifestFile`, `S3Uri` identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. `AugmentedManifestFile` can only be used if the Channel's input mode is `Pipe`. - `s3_uri`**Type**: `STRING`**Provider name**: `S3Uri`**Description**: Depending on the value specified for the `S3DataType`, identifies either a key name prefix or a manifest. For example: - A key name prefix might look like this: `s3://bucketname/exampleprefix/` - A manifest might look like this: `s3://bucketname/example.manifest` A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of `S3Uri`. Note that the prefix must be a valid non-empty `S3Uri` that precludes users from specifying a manifest whose individual `S3Uri` is sourced from different S3 buckets. The following code example shows a valid manifest format: `[ {"prefix": "s3://customer_bucket/some/prefix/"},` `"relative/path/to/custdata-1",` `"relative/path/custdata-2",` `…` `"relative/path/custdata-N"` `]` This JSON is equivalent to the following `S3Uri` list: `s3://customer_bucket/some/prefix/relative/path/to/custdata-1` `s3://customer_bucket/some/prefix/relative/path/custdata-2` `…` `s3://customer_bucket/some/prefix/relative/path/custdata-N` The complete set of `S3Uri` in this manifest is the input data for the channel for this data source. The object that each `S3Uri` points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf. Your input bucket must be located in same Amazon Web Services region as your training job. - `input_mode`**Type**: `STRING`**Provider name**: `InputMode`**Description**: (Optional) The input mode to use for the data channel in a training job. If you don't set a value for `InputMode`, SageMaker uses the value set for `TrainingInputMode`. Use this parameter to override the `TrainingInputMode` setting in a [AlgorithmSpecification](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AlgorithmSpecification.html) request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use `File` input mode. To stream data directly from Amazon S3 to the container, choose `Pipe` input mode. To use a model for incremental training, choose `File` input model. - `record_wrapper_type`**Type**: `STRING`**Provider name**: `RecordWrapperType`**Description**: Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see [Create a Dataset Using RecordIO](https://mxnet.apache.org/api/architecture/note_data_loading#data-format). In File mode, leave this field unset or set it to None. - `shuffle_config`**Type**: `STRUCT`**Provider name**: `ShuffleConfig`**Description**: A configuration for a shuffle option for input data in a channel. If you use `S3Prefix` for `S3DataType`, this shuffles the results of the S3 key prefix matches. 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, 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, 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. - `seed`**Type**: `INT64`**Provider name**: `Seed`**Description**: Determines the shuffling order in `ShuffleConfig` value. - `output_data_config`**Type**: `STRUCT`**Provider name**: `OutputDataConfig`**Description**: Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches. - `compression_type`**Type**: `STRING`**Provider name**: `CompressionType`**Description**: The model output compression type. Select `None` to output an uncompressed model, recommended for large model outputs. Defaults to gzip. - `kms_key_id`**Type**: `STRING`**Provider name**: `KmsKeyId`**Description**: The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The `KmsKeyId` can be any of the following formats: - // KMS Key ID `"1234abcd-12ab-34cd-56ef-1234567890ab"` - // Amazon Resource Name (ARN) of a KMS Key `"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"` - // KMS Key Alias `"alias/ExampleAlias"` - // Amazon Resource Name (ARN) of a KMS Key Alias `"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"` If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call `kms:Encrypt`. If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see [KMS-Managed Encryption Keys](https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingKMSEncryption.html) in the Amazon Simple Storage Service Developer Guide. If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One Zone The KMS key policy must grant permission to the IAM role that you specify in your `CreateTrainingJob`, `CreateTransformJob`, or `CreateHyperParameterTuningJob` requests. For more information, see [Using Key Policies in Amazon Web Services KMS](https://docs.aws.amazon.com/kms/latest/developerguide/key-policies.html) in the Amazon Web Services Key Management Service Developer Guide. - `s3_output_path`**Type**: `STRING`**Provider name**: `S3OutputPath`**Description**: Identifies the S3 path where you want SageMaker to store the model artifacts. For example, `s3://bucket-name/key-name-prefix`. - `resource_config`**Type**: `STRUCT`**Provider name**: `ResourceConfig`**Description**: The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches. Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want SageMaker to use the 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.If you want to use hyperparameter optimization with instance type flexibility, use `HyperParameterTuningResourceConfig` instead. - `instance_count`**Type**: `INT32`**Provider name**: `InstanceCount`**Description**: The number of ML compute instances to use. For distributed training, provide a value greater than 1. - `instance_groups`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `InstanceGroups`**Description**: The configuration of a heterogeneous cluster in JSON format. - `instance_count`**Type**: `INT32`**Provider name**: `InstanceCount`**Description**: Specifies the number of instances of the instance group. - `instance_group_name`**Type**: `STRING`**Provider name**: `InstanceGroupName`**Description**: Specifies the name of the instance group. - `instance_type`**Type**: `STRING`**Provider name**: `InstanceType`**Description**: Specifies the instance type of the instance group. - `instance_type`**Type**: `STRING`**Provider name**: `InstanceType`**Description**: The ML compute instance type.SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022. [Amazon EC2 P4de instances](http://aws.amazon.com/ec2/instance-types/p4/) (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (`ml.p4de.24xlarge`) to reduce model training time. The `ml.p4de.24xlarge` instances are available in the following Amazon Web Services Regions. - US East (N. Virginia) (us-east-1) - US West (Oregon) (us-west-2) To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team. - `keep_alive_period_in_seconds`**Type**: `INT32`**Provider name**: `KeepAlivePeriodInSeconds`**Description**: The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs. - `training_plan_arn`**Type**: `STRING`**Provider name**: `TrainingPlanArn`**Description**: The Amazon Resource Name (ARN); of the training plan to use for this resource configuration. - `volume_kms_key_id`**Type**: `STRING`**Provider name**: `VolumeKmsKeyId`**Description**: The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a `VolumeKmsKeyId` when using an instance type with local storage. For a list of instance types that support local instance storage, see [Instance Store Volumes](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/InstanceStorage.html#instance-store-volumes). For more information about local instance storage encryption, see [SSD Instance Store Volumes](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ssd-instance-store.html).The `VolumeKmsKeyId` can be in any of the following formats: - // KMS Key ID `"1234abcd-12ab-34cd-56ef-1234567890ab"` - // Amazon Resource Name (ARN) of a KMS Key `"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"` - `volume_size_in_gb`**Type**: `INT32`**Provider name**: `VolumeSizeInGB`**Description**: The size of the ML storage volume that you want to provision. ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose `File` as the `TrainingInputMode` in the algorithm specification. When using an ML instance with [NVMe SSD volumes](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ssd-instance-store.html#nvme-ssd-volumes), SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include `ml.p4d`, `ml.g4dn`, and `ml.g5`. When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through `VolumeSizeInGB` in the `ResourceConfig` API. For example, ML instance families that use EBS volumes include `ml.c5` and `ml.p2`. To look up instance types and their instance storage types and volumes, see [Amazon EC2 Instance Types](http://aws.amazon.com/ec2/instance-types/). To find the default local paths defined by the SageMaker training platform, see [Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs](https://docs.aws.amazon.com/sagemaker/latest/dg/model-train-storage.html). - `retry_strategy`**Type**: `STRUCT`**Provider name**: `RetryStrategy`**Description**: The number of times to retry the job when the job fails due to an `InternalServerError`. - `maximum_retry_attempts`**Type**: `INT32`**Provider name**: `MaximumRetryAttempts`**Description**: The number of times to retry the job. When the job is retried, it's `SecondaryStatus` is changed to `STARTING`. - `role_arn`**Type**: `STRING`**Provider name**: `RoleArn`**Description**: The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches. - `static_hyper_parameters`**Type**: `MAP_STRING_STRING`**Provider name**: `StaticHyperParameters`**Description**: Specifies the values of hyperparameters that do not change for the tuning job. - `stopping_condition`**Type**: `STRUCT`**Provider name**: `StoppingCondition`**Description**: Specifies a limit to how long a model hyperparameter training 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 job. Use this API to cap model training costs. - `max_pending_time_in_seconds`**Type**: `INT32`**Provider name**: `MaxPendingTimeInSeconds`**Description**: The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.When working with training jobs that use capacity from [training plans](https://docs.aws.amazon.com/sagemaker/latest/dg/reserve-capacity-with-training-plans.html), not all `Pending` job states count against the `MaxPendingTimeInSeconds` limit. The following scenarios do not increment the `MaxPendingTimeInSeconds` counter: - The plan is in a `Scheduled` state: Jobs queued (in `Pending` status) before a plan's start date (waiting for scheduled start time) - Between capacity reservations: Jobs temporarily back to `Pending` status between two capacity reservation periods `MaxPendingTimeInSeconds` only increments when jobs are actively waiting for capacity in an `Active` plan. - `max_runtime_in_seconds`**Type**: `INT32`**Provider name**: `MaxRuntimeInSeconds`**Description**: The maximum length of time, in seconds, that a training or compilation job can run before it is stopped. For compilation jobs, if the job does not complete during this time, a `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. - `max_wait_time_in_seconds`**Type**: `INT32`**Provider name**: `MaxWaitTimeInSeconds`**Description**: The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than `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. - `tuning_objective`**Type**: `STRUCT`**Provider name**: `TuningObjective` - `metric_name`**Type**: `STRING`**Provider name**: `MetricName`**Description**: The name of the metric to use for the objective metric. - `type`**Type**: `STRING`**Provider name**: `Type`**Description**: Whether to minimize or maximize the objective metric. - `vpc_config`**Type**: `STRUCT`**Provider name**: `VpcConfig`**Description**: The [VpcConfig](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_VpcConfig.html) object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches 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). - `security_group_ids`**Type**: `UNORDERED_LIST_STRING`**Provider name**: `SecurityGroupIds`**Description**: The VPC security group IDs, in the form `sg-xxxxxxxx`. Specify the security groups for the VPC that is specified in the `Subnets` field. - `subnets`**Type**: `UNORDERED_LIST_STRING`**Provider name**: `Subnets`**Description**: The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see [Supported Instance Types and Availability Zones](https://docs.aws.amazon.com/sagemaker/latest/dg/instance-types-az.html). ## `training_job_definitions`{% #training_job_definitions %} **Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `TrainingJobDefinitions`**Description**: A list of the [HyperParameterTrainingJobDefinition](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTrainingJobDefinition.html) objects launched for this tuning job. - `algorithm_specification`**Type**: `STRUCT`**Provider name**: `AlgorithmSpecification`**Description**: The [HyperParameterAlgorithmSpecification](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterAlgorithmSpecification.html) object that specifies the resource algorithm to use for the training jobs that the tuning job launches. - `algorithm_name`**Type**: `STRING`**Provider name**: `AlgorithmName`**Description**: The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for `TrainingImage`. - `metric_definitions`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `MetricDefinitions`**Description**: An array of [MetricDefinition](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_MetricDefinition.html) objects that specify the metrics that the algorithm emits. - `name`**Type**: `STRING`**Provider name**: `Name`**Description**: The name of the metric. - `regex`**Type**: `STRING`**Provider name**: `Regex`**Description**: A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see [Defining metrics and environment variables](https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-metrics-variables.html). - `training_image`**Type**: `STRING`**Provider name**: `TrainingImage`**Description**: The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see [Algorithms Provided by Amazon SageMaker: Common Parameters](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html). SageMaker supports both `registry/repository[:tag]` and `registry/repository[@digest]` image path formats. For more information, see [Using Your Own Algorithms with Amazon SageMaker](https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html). - `training_input_mode`**Type**: `STRING`**Provider name**: `TrainingInputMode` - `checkpoint_config`**Type**: `STRUCT`**Provider name**: `CheckpointConfig` - `local_path`**Type**: `STRING`**Provider name**: `LocalPath`**Description**: (Optional) The local directory where checkpoints are written. The default directory is `/opt/ml/checkpoints/`. - `s3_uri`**Type**: `STRING`**Provider name**: `S3Uri`**Description**: Identifies the S3 path where you want SageMaker to store checkpoints. For example, `s3://bucket-name/key-name-prefix`. - `definition_name`**Type**: `STRING`**Provider name**: `DefinitionName`**Description**: The job definition name. - `enable_inter_container_traffic_encryption`**Type**: `BOOLEAN`**Provider name**: `EnableInterContainerTrafficEncryption`**Description**: 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. - `enable_managed_spot_training`**Type**: `BOOLEAN`**Provider name**: `EnableManagedSpotTraining`**Description**: A Boolean indicating whether managed spot training is enabled (`True`) or not (`False`). - `enable_network_isolation`**Type**: `BOOLEAN`**Provider name**: `EnableNetworkIsolation`**Description**: 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, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access. - `environment`**Type**: `MAP_STRING_STRING`**Provider name**: `Environment`**Description**: An environment variable that you can pass into the SageMaker [CreateTrainingJob](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html) API. You can use an existing [environment variable from the training container](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html#sagemaker-CreateTrainingJob-request-Environment) or use your own. See [Define metrics and variables](https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-metrics-variables.html) for more information.The maximum number of items specified for `Map Entries` refers to the maximum number of environment variables for each `TrainingJobDefinition` and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of environment variables for all the training job definitions can't exceed the maximum number specified. - `hyper_parameter_ranges`**Type**: `STRUCT`**Provider name**: `HyperParameterRanges` - `auto_parameters`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `AutoParameters`**Description**: A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job. - `name`**Type**: `STRING`**Provider name**: `Name`**Description**: The name of the hyperparameter to optimize using Autotune. - `value_hint`**Type**: `STRING`**Provider name**: `ValueHint`**Description**: An example value of the hyperparameter to optimize using Autotune. - `categorical_parameter_ranges`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `CategoricalParameterRanges`**Description**: The array of [CategoricalParameterRange](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CategoricalParameterRange.html) objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches. - `name`**Type**: `STRING`**Provider name**: `Name`**Description**: The name of the categorical hyperparameter to tune. - `values`**Type**: `UNORDERED_LIST_STRING`**Provider name**: `Values`**Description**: A list of the categories for the hyperparameter. - `continuous_parameter_ranges`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `ContinuousParameterRanges`**Description**: The array of [ContinuousParameterRange](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ContinuousParameterRange.html) objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches. - `max_value`**Type**: `STRING`**Provider name**: `MaxValue`**Description**: The maximum value for the hyperparameter. The tuning job uses floating-point values between `MinValue` value and this value for tuning. - `min_value`**Type**: `STRING`**Provider name**: `MinValue`**Description**: The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and `MaxValue`for tuning. - `name`**Type**: `STRING`**Provider name**: `Name`**Description**: The name of the continuous hyperparameter to tune. - `scaling_type`**Type**: `STRING`**Provider name**: `ScalingType`**Description**: The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see [Hyperparameter Scaling](https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-ranges.html#scaling-type). One of the following values: {% dl %} {% dt %} Auto {% /dt %} {% dd %} SageMaker hyperparameter tuning chooses the best scale for the hyperparameter. {% /dd %} {% dt %} Linear {% /dt %} {% dd %} Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale. {% /dd %} {% dt %} Logarithmic {% /dt %} {% dd %} Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale. Logarithmic scaling works only for ranges that have only values greater than 0. {% /dd %} {% dt %} ReverseLogarithmic {% /dt %} {% dd %} Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale. Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0. {% /dd %} {% /dl %} - `integer_parameter_ranges`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `IntegerParameterRanges`**Description**: The array of [IntegerParameterRange](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_IntegerParameterRange.html) objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches. - `max_value`**Type**: `STRING`**Provider name**: `MaxValue`**Description**: The maximum value of the hyperparameter to search. - `min_value`**Type**: `STRING`**Provider name**: `MinValue`**Description**: The minimum value of the hyperparameter to search. - `name`**Type**: `STRING`**Provider name**: `Name`**Description**: The name of the hyperparameter to search. - `scaling_type`**Type**: `STRING`**Provider name**: `ScalingType`**Description**: The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see [Hyperparameter Scaling](https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-ranges.html#scaling-type). One of the following values: {% dl %} {% dt %} Auto {% /dt %} {% dd %} SageMaker hyperparameter tuning chooses the best scale for the hyperparameter. {% /dd %} {% dt %} Linear {% /dt %} {% dd %} Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale. {% /dd %} {% dt %} Logarithmic {% /dt %} {% dd %} Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale. Logarithmic scaling works only for ranges that have only values greater than 0. {% /dd %} {% /dl %} - `hyper_parameter_tuning_resource_config`**Type**: `STRUCT`**Provider name**: `HyperParameterTuningResourceConfig`**Description**: The configuration for the hyperparameter tuning resources, including the compute instances and storage volumes, used for training jobs launched by the tuning job. By default, storage volumes hold model artifacts and incremental states. Choose `File` for `TrainingInputMode` in the `AlgorithmSpecification` parameter to additionally store training data in the storage volume (optional). - `allocation_strategy`**Type**: `STRING`**Provider name**: `AllocationStrategy`**Description**: The strategy that determines the order of preference for resources specified in `InstanceConfigs` used in hyperparameter optimization. - `instance_configs`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `InstanceConfigs`**Description**: A list containing the configuration(s) for one or more resources for processing hyperparameter jobs. These resources include compute instances and storage volumes to use in model training jobs launched by hyperparameter tuning jobs. The `AllocationStrategy` controls the order in which multiple configurations provided in `InstanceConfigs` are used.If you only want to use a single instance configuration inside the `HyperParameterTuningResourceConfig` API, do not provide a value for `InstanceConfigs`. Instead, use `InstanceType`, `VolumeSizeInGB` and `InstanceCount`. If you use `InstanceConfigs`, do not provide values for `InstanceType`, `VolumeSizeInGB` or `InstanceCount`. - `instance_count`**Type**: `INT32`**Provider name**: `InstanceCount`**Description**: The number of instances of the type specified by `InstanceType`. Choose an instance count larger than 1 for distributed training algorithms. See [Step 2: Launch a SageMaker Distributed Training Job Using the SageMaker Python SDK](https://docs.aws.amazon.com/sagemaker/latest/dg/data-parallel-use-api.html) for more information. - `instance_type`**Type**: `STRING`**Provider name**: `InstanceType`**Description**: The instance type used for processing of hyperparameter optimization jobs. Choose from general purpose (no GPUs) instance types: ml.m5.xlarge, ml.m5.2xlarge, and ml.m5.4xlarge or compute optimized (no GPUs) instance types: ml.c5.xlarge and ml.c5.2xlarge. For more information about instance types, see [instance type descriptions](https://docs.aws.amazon.com/sagemaker/latest/dg/notebooks-available-instance-types.html). - `volume_size_in_gb`**Type**: `INT32`**Provider name**: `VolumeSizeInGB`**Description**: The volume size in GB of the data to be processed for hyperparameter optimization (optional). - `instance_count`**Type**: `INT32`**Provider name**: `InstanceCount`**Description**: The number of compute instances of type `InstanceType` to use. For [distributed training](https://docs.aws.amazon.com/sagemaker/latest/dg/data-parallel-use-api.html), select a value greater than 1. - `instance_type`**Type**: `STRING`**Provider name**: `InstanceType`**Description**: The instance type used to run hyperparameter optimization tuning jobs. See [descriptions of instance types](https://docs.aws.amazon.com/sagemaker/latest/dg/notebooks-available-instance-types.html) for more information. - `volume_kms_key_id`**Type**: `STRING`**Provider name**: `VolumeKmsKeyId`**Description**: A key used by Amazon Web Services Key Management Service to encrypt data on the storage volume attached to the compute instances used to run the training job. You can use either of the following formats to specify a key. KMS Key ID: `"1234abcd-12ab-34cd-56ef-1234567890ab"` Amazon Resource Name (ARN) of a KMS key: `"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"` Some instances use local storage, which use a [hardware module to encrypt](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ssd-instance-store.html) storage volumes. If you choose one of these instance types, you cannot request a `VolumeKmsKeyId`. For a list of instance types that use local storage, see [instance store volumes](http://aws.amazon.com/releasenotes/host-instance-storage-volumes-table/). For more information about Amazon Web Services Key Management Service, see [KMS encryption](https://docs.aws.amazon.com/sagemaker/latest/dg/sms-security-kms-permissions.html) for more information. - `volume_size_in_gb`**Type**: `INT32`**Provider name**: `VolumeSizeInGB`**Description**: The volume size in GB for the storage volume to be used in processing hyperparameter optimization jobs (optional). These volumes store model artifacts, incremental states and optionally, scratch space for training algorithms. Do not provide a value for this parameter if a value for `InstanceConfigs` is also specified. Some instance types have a fixed total local storage size. If you select one of these instances for training, `VolumeSizeInGB` cannot be greater than this total size. For a list of instance types with local instance storage and their sizes, see [instance store volumes](http://aws.amazon.com/releasenotes/host-instance-storage-volumes-table/).SageMaker supports only the [General Purpose SSD (gp2)](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ebs-volume-types.html) storage volume type. - `input_data_config`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `InputDataConfig`**Description**: An array of [Channel](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_Channel.html) objects that specify the input for the training jobs that the tuning job launches. - `channel_name`**Type**: `STRING`**Provider name**: `ChannelName`**Description**: The name of the channel. - `compression_type`**Type**: `STRING`**Provider name**: `CompressionType`**Description**: If training data is compressed, the compression type. The default value is `None`. `CompressionType` is used only in Pipe input mode. In File mode, leave this field unset or set it to None. - `content_type`**Type**: `STRING`**Provider name**: `ContentType`**Description**: The MIME type of the data. - `data_source`**Type**: `STRUCT`**Provider name**: `DataSource`**Description**: The location of the channel data. - `file_system_data_source`**Type**: `STRUCT`**Provider name**: `FileSystemDataSource`**Description**: The file system that is associated with a channel. - `directory_path`**Type**: `STRING`**Provider name**: `DirectoryPath`**Description**: The full path to the directory to associate with the channel. - `file_system_access_mode`**Type**: `STRING`**Provider name**: `FileSystemAccessMode`**Description**: The access mode of the mount of the directory associated with the channel. A directory can be mounted either in `ro` (read-only) or `rw` (read-write) mode. - `file_system_id`**Type**: `STRING`**Provider name**: `FileSystemId`**Description**: The file system id. - `file_system_type`**Type**: `STRING`**Provider name**: `FileSystemType`**Description**: The file system type. - `s3_data_source`**Type**: `STRUCT`**Provider name**: `S3DataSource`**Description**: The S3 location of the data source that is associated with a channel. - `attribute_names`**Type**: `UNORDERED_LIST_STRING`**Provider name**: `AttributeNames`**Description**: A list of one or more attribute names to use that are found in a specified augmented manifest file. - `hub_access_config`**Type**: `STRUCT`**Provider name**: `HubAccessConfig`**Description**: The configuration for a private hub model reference that points to a SageMaker JumpStart public hub model. - `hub_content_arn`**Type**: `STRING`**Provider name**: `HubContentArn`**Description**: The ARN of your private model hub content. This should be a `ModelReference` resource type that points to a SageMaker JumpStart public hub model. - `instance_group_names`**Type**: `UNORDERED_LIST_STRING`**Provider name**: `InstanceGroupNames`**Description**: A list of names of instance groups that get data from the S3 data source. - `model_access_config`**Type**: `STRUCT`**Provider name**: `ModelAccessConfig` - `accept_eula`**Type**: `BOOLEAN`**Provider name**: `AcceptEula`**Description**: Specifies agreement to the model end-user license agreement (EULA). The `AcceptEula` value must be explicitly defined as `True` in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model. - `s3_data_distribution_type`**Type**: `STRING`**Provider name**: `S3DataDistributionType`**Description**: If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify `FullyReplicated`. If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify `ShardedByS3Key`. If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data. Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms. In distributed training, where you use multiple ML compute EC2 instances, you might choose `ShardedByS3Key`. If the algorithm requires copying training data to the ML storage volume (when `TrainingInputMode` is set to `File`), this copies 1/n of the number of objects. - `s3_data_type`**Type**: `STRING`**Provider name**: `S3DataType`**Description**: If you choose `S3Prefix`, `S3Uri` identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training. If you choose `ManifestFile`, `S3Uri` identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training. If you choose `AugmentedManifestFile`, `S3Uri` identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. `AugmentedManifestFile` can only be used if the Channel's input mode is `Pipe`. - `s3_uri`**Type**: `STRING`**Provider name**: `S3Uri`**Description**: Depending on the value specified for the `S3DataType`, identifies either a key name prefix or a manifest. For example: - A key name prefix might look like this: `s3://bucketname/exampleprefix/` - A manifest might look like this: `s3://bucketname/example.manifest` A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of `S3Uri`. Note that the prefix must be a valid non-empty `S3Uri` that precludes users from specifying a manifest whose individual `S3Uri` is sourced from different S3 buckets. The following code example shows a valid manifest format: `[ {"prefix": "s3://customer_bucket/some/prefix/"},` `"relative/path/to/custdata-1",` `"relative/path/custdata-2",` `…` `"relative/path/custdata-N"` `]` This JSON is equivalent to the following `S3Uri` list: `s3://customer_bucket/some/prefix/relative/path/to/custdata-1` `s3://customer_bucket/some/prefix/relative/path/custdata-2` `…` `s3://customer_bucket/some/prefix/relative/path/custdata-N` The complete set of `S3Uri` in this manifest is the input data for the channel for this data source. The object that each `S3Uri` points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf. Your input bucket must be located in same Amazon Web Services region as your training job. - `input_mode`**Type**: `STRING`**Provider name**: `InputMode`**Description**: (Optional) The input mode to use for the data channel in a training job. If you don't set a value for `InputMode`, SageMaker uses the value set for `TrainingInputMode`. Use this parameter to override the `TrainingInputMode` setting in a [AlgorithmSpecification](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AlgorithmSpecification.html) request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use `File` input mode. To stream data directly from Amazon S3 to the container, choose `Pipe` input mode. To use a model for incremental training, choose `File` input model. - `record_wrapper_type`**Type**: `STRING`**Provider name**: `RecordWrapperType`**Description**: Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see [Create a Dataset Using RecordIO](https://mxnet.apache.org/api/architecture/note_data_loading#data-format). In File mode, leave this field unset or set it to None. - `shuffle_config`**Type**: `STRUCT`**Provider name**: `ShuffleConfig`**Description**: A configuration for a shuffle option for input data in a channel. If you use `S3Prefix` for `S3DataType`, this shuffles the results of the S3 key prefix matches. 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, 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, 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. - `seed`**Type**: `INT64`**Provider name**: `Seed`**Description**: Determines the shuffling order in `ShuffleConfig` value. - `output_data_config`**Type**: `STRUCT`**Provider name**: `OutputDataConfig`**Description**: Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches. - `compression_type`**Type**: `STRING`**Provider name**: `CompressionType`**Description**: The model output compression type. Select `None` to output an uncompressed model, recommended for large model outputs. Defaults to gzip. - `kms_key_id`**Type**: `STRING`**Provider name**: `KmsKeyId`**Description**: The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The `KmsKeyId` can be any of the following formats: - // KMS Key ID `"1234abcd-12ab-34cd-56ef-1234567890ab"` - // Amazon Resource Name (ARN) of a KMS Key `"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"` - // KMS Key Alias `"alias/ExampleAlias"` - // Amazon Resource Name (ARN) of a KMS Key Alias `"arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"` If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call `kms:Encrypt`. If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see [KMS-Managed Encryption Keys](https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingKMSEncryption.html) in the Amazon Simple Storage Service Developer Guide. If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One Zone The KMS key policy must grant permission to the IAM role that you specify in your `CreateTrainingJob`, `CreateTransformJob`, or `CreateHyperParameterTuningJob` requests. For more information, see [Using Key Policies in Amazon Web Services KMS](https://docs.aws.amazon.com/kms/latest/developerguide/key-policies.html) in the Amazon Web Services Key Management Service Developer Guide. - `s3_output_path`**Type**: `STRING`**Provider name**: `S3OutputPath`**Description**: Identifies the S3 path where you want SageMaker to store the model artifacts. For example, `s3://bucket-name/key-name-prefix`. - `resource_config`**Type**: `STRUCT`**Provider name**: `ResourceConfig`**Description**: The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches. Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want SageMaker to use the 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.If you want to use hyperparameter optimization with instance type flexibility, use `HyperParameterTuningResourceConfig` instead. - `instance_count`**Type**: `INT32`**Provider name**: `InstanceCount`**Description**: The number of ML compute instances to use. For distributed training, provide a value greater than 1. - `instance_groups`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `InstanceGroups`**Description**: The configuration of a heterogeneous cluster in JSON format. - `instance_count`**Type**: `INT32`**Provider name**: `InstanceCount`**Description**: Specifies the number of instances of the instance group. - `instance_group_name`**Type**: `STRING`**Provider name**: `InstanceGroupName`**Description**: Specifies the name of the instance group. - `instance_type`**Type**: `STRING`**Provider name**: `InstanceType`**Description**: Specifies the instance type of the instance group. - `instance_type`**Type**: `STRING`**Provider name**: `InstanceType`**Description**: The ML compute instance type.SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022. [Amazon EC2 P4de instances](http://aws.amazon.com/ec2/instance-types/p4/) (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (`ml.p4de.24xlarge`) to reduce model training time. The `ml.p4de.24xlarge` instances are available in the following Amazon Web Services Regions. - US East (N. Virginia) (us-east-1) - US West (Oregon) (us-west-2) To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team. - `keep_alive_period_in_seconds`**Type**: `INT32`**Provider name**: `KeepAlivePeriodInSeconds`**Description**: The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs. - `training_plan_arn`**Type**: `STRING`**Provider name**: `TrainingPlanArn`**Description**: The Amazon Resource Name (ARN); of the training plan to use for this resource configuration. - `volume_kms_key_id`**Type**: `STRING`**Provider name**: `VolumeKmsKeyId`**Description**: The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a `VolumeKmsKeyId` when using an instance type with local storage. For a list of instance types that support local instance storage, see [Instance Store Volumes](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/InstanceStorage.html#instance-store-volumes). For more information about local instance storage encryption, see [SSD Instance Store Volumes](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ssd-instance-store.html).The `VolumeKmsKeyId` can be in any of the following formats: - // KMS Key ID `"1234abcd-12ab-34cd-56ef-1234567890ab"` - // Amazon Resource Name (ARN) of a KMS Key `"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"` - `volume_size_in_gb`**Type**: `INT32`**Provider name**: `VolumeSizeInGB`**Description**: The size of the ML storage volume that you want to provision. ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose `File` as the `TrainingInputMode` in the algorithm specification. When using an ML instance with [NVMe SSD volumes](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ssd-instance-store.html#nvme-ssd-volumes), SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include `ml.p4d`, `ml.g4dn`, and `ml.g5`. When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through `VolumeSizeInGB` in the `ResourceConfig` API. For example, ML instance families that use EBS volumes include `ml.c5` and `ml.p2`. To look up instance types and their instance storage types and volumes, see [Amazon EC2 Instance Types](http://aws.amazon.com/ec2/instance-types/). To find the default local paths defined by the SageMaker training platform, see [Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs](https://docs.aws.amazon.com/sagemaker/latest/dg/model-train-storage.html). - `retry_strategy`**Type**: `STRUCT`**Provider name**: `RetryStrategy`**Description**: The number of times to retry the job when the job fails due to an `InternalServerError`. - `maximum_retry_attempts`**Type**: `INT32`**Provider name**: `MaximumRetryAttempts`**Description**: The number of times to retry the job. When the job is retried, it's `SecondaryStatus` is changed to `STARTING`. - `role_arn`**Type**: `STRING`**Provider name**: `RoleArn`**Description**: The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches. - `static_hyper_parameters`**Type**: `MAP_STRING_STRING`**Provider name**: `StaticHyperParameters`**Description**: Specifies the values of hyperparameters that do not change for the tuning job. - `stopping_condition`**Type**: `STRUCT`**Provider name**: `StoppingCondition`**Description**: Specifies a limit to how long a model hyperparameter training 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 job. Use this API to cap model training costs. - `max_pending_time_in_seconds`**Type**: `INT32`**Provider name**: `MaxPendingTimeInSeconds`**Description**: The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.When working with training jobs that use capacity from [training plans](https://docs.aws.amazon.com/sagemaker/latest/dg/reserve-capacity-with-training-plans.html), not all `Pending` job states count against the `MaxPendingTimeInSeconds` limit. The following scenarios do not increment the `MaxPendingTimeInSeconds` counter: - The plan is in a `Scheduled` state: Jobs queued (in `Pending` status) before a plan's start date (waiting for scheduled start time) - Between capacity reservations: Jobs temporarily back to `Pending` status between two capacity reservation periods `MaxPendingTimeInSeconds` only increments when jobs are actively waiting for capacity in an `Active` plan. - `max_runtime_in_seconds`**Type**: `INT32`**Provider name**: `MaxRuntimeInSeconds`**Description**: The maximum length of time, in seconds, that a training or compilation job can run before it is stopped. For compilation jobs, if the job does not complete during this time, a `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. - `max_wait_time_in_seconds`**Type**: `INT32`**Provider name**: `MaxWaitTimeInSeconds`**Description**: The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than `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. - `tuning_objective`**Type**: `STRUCT`**Provider name**: `TuningObjective` - `metric_name`**Type**: `STRING`**Provider name**: `MetricName`**Description**: The name of the metric to use for the objective metric. - `type`**Type**: `STRING`**Provider name**: `Type`**Description**: Whether to minimize or maximize the objective metric. - `vpc_config`**Type**: `STRUCT`**Provider name**: `VpcConfig`**Description**: The [VpcConfig](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_VpcConfig.html) object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches 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). - `security_group_ids`**Type**: `UNORDERED_LIST_STRING`**Provider name**: `SecurityGroupIds`**Description**: The VPC security group IDs, in the form `sg-xxxxxxxx`. Specify the security groups for the VPC that is specified in the `Subnets` field. - `subnets`**Type**: `UNORDERED_LIST_STRING`**Provider name**: `Subnets`**Description**: The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see [Supported Instance Types and Availability Zones](https://docs.aws.amazon.com/sagemaker/latest/dg/instance-types-az.html). ## `training_job_status_counters`{% #training_job_status_counters %} **Type**: `STRUCT`**Provider name**: `TrainingJobStatusCounters`**Description**: The [TrainingJobStatusCounters](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TrainingJobStatusCounters.html) object that specifies the number of training jobs, categorized by status, that this tuning job launched. - `completed`**Type**: `INT32`**Provider name**: `Completed`**Description**: The number of completed training jobs launched by the hyperparameter tuning job. - `in_progress`**Type**: `INT32`**Provider name**: `InProgress`**Description**: The number of in-progress training jobs launched by a hyperparameter tuning job. - `non_retryable_error`**Type**: `INT32`**Provider name**: `NonRetryableError`**Description**: The number of training jobs that failed and can't be retried. A failed training job can't be retried if it failed because a client error occurred. - `retryable_error`**Type**: `INT32`**Provider name**: `RetryableError`**Description**: The number of training jobs that failed, but can be retried. A failed training job can be retried only if it failed because an internal service error occurred. - `stopped`**Type**: `INT32`**Provider name**: `Stopped`**Description**: The number of training jobs launched by a hyperparameter tuning job that were manually stopped. ## `tuning_job_completion_details`{% #tuning_job_completion_details %} **Type**: `STRUCT`**Provider name**: `TuningJobCompletionDetails`**Description**: Tuning job completion information returned as the response from a hyperparameter tuning job. This information tells if your tuning job has or has not converged. It also includes the number of training jobs that have not improved model performance as evaluated against the objective function. - `convergence_detected_time`**Type**: `TIMESTAMP`**Provider name**: `ConvergenceDetectedTime`**Description**: The time in timestamp format that AMT detected model convergence, as defined by a lack of significant improvement over time based on criteria developed over a wide range of diverse benchmarking tests. - `number_of_training_jobs_objective_not_improving`**Type**: `INT32`**Provider name**: `NumberOfTrainingJobsObjectiveNotImproving`**Description**: The number of training jobs launched by a tuning job that are not improving (1% or less) as measured by model performance evaluated against an objective function. ## `warm_start_config`{% #warm_start_config %} **Type**: `STRUCT`**Provider name**: `WarmStartConfig`**Description**: The configuration for starting the hyperparameter parameter 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. - `parent_hyper_parameter_tuning_jobs`**Type**: `UNORDERED_LIST_STRUCT`**Provider name**: `ParentHyperParameterTuningJobs`**Description**: An array of hyperparameter tuning jobs that are used as the starting point for the new hyperparameter tuning job. For more information about warm starting a hyperparameter tuning job, see [Using a Previous Hyperparameter Tuning Job as a Starting Point](https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-warm-start.html). Hyperparameter tuning jobs created before October 1, 2018 cannot be used as parent jobs for warm start tuning jobs. - `hyper_parameter_tuning_job_name`**Type**: `STRING`**Provider name**: `HyperParameterTuningJobName`**Description**: The name of the hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job. - `warm_start_type`**Type**: `STRING`**Provider name**: `WarmStartType`**Description**: Specifies one of the following: {% dl %} {% dt %} IDENTICAL_DATA_AND_ALGORITHM {% /dt %} {% dd %} The new hyperparameter tuning job uses the same input data and training image as the parent tuning jobs. You can change the hyperparameter ranges to search and the maximum number of training jobs that the hyperparameter tuning job launches. You cannot use a new version of the training algorithm, unless the changes in the new version do not affect the algorithm itself. For example, changes that improve logging or adding support for a different data format are allowed. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs. {% /dd %} {% dt %} TRANSFER_LEARNING {% /dt %} {% dd %} The new hyperparameter tuning job can include input data, hyperparameter ranges, maximum number of concurrent training jobs, and maximum number of training jobs that are different than those of its parent hyperparameter tuning jobs. The training image can also be a different version from the version used in the parent hyperparameter tuning job. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs. {% /dd %} {% /dl %}