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aws_sagemaker_trainingjob

account_id

Type: STRING

algorithm_specification

Type: STRUCT
Provider name: AlgorithmSpecification
Description: Information about the algorithm used for training, and algorithm metadata.

  • algorithm_name
    Type: STRING
    Provider name: AlgorithmName
    Description: The name of the algorithm resource to use for the training job. This must be an algorithm resource that you created or subscribe to on Amazon Web Services Marketplace. You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter. Note that the AlgorithmName parameter is mutually exclusive with the TrainingImage parameter. If you specify a value for the AlgorithmName parameter, you can’t specify a value for TrainingImage, and vice versa. If you specify values for both parameters, the training job might break; if you don’t specify any value for both parameters, the training job might raise a null error.
  • container_arguments
    Type: UNORDERED_LIST_STRING
    Provider name: ContainerArguments
    Description: The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.
  • container_entrypoint
    Type: UNORDERED_LIST_STRING
    Provider name: ContainerEntrypoint
    Description: The entrypoint script for a Docker container used to run a training job. This script takes precedence over the default train processing instructions. See How Amazon SageMaker Runs Your Training Image for more information.
  • enable_sage_maker_metrics_time_series
    Type: BOOLEAN
    Provider name: EnableSageMakerMetricsTimeSeries
    Description: To generate and save time-series metrics during training, set to true. The default is false and time-series metrics aren’t generated except in the following cases:
  • metric_definitions
    Type: UNORDERED_LIST_STRUCT
    Provider name: MetricDefinitions
    Description: A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. SageMaker publishes each metric to Amazon CloudWatch.
    • 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.
  • 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 SageMaker built-in algorithms, see Docker Registry Paths and Example Code in the Amazon SageMaker developer guide. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information about using your custom training container, see Using Your Own Algorithms with Amazon SageMaker. You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter. For more information, see the note in the AlgorithmName parameter description.
  • training_image_config
    Type: STRUCT
    Provider name: TrainingImageConfig
    Description: The configuration to use an image from a private Docker registry for a training job.
    • training_repository_access_mode
      Type: STRING
      Provider name: TrainingRepositoryAccessMode
      Description: The method that your training job will use to gain access to the images in your private Docker registry. For access to an image in a private Docker registry, set to Vpc.
    • training_repository_auth_config
      Type: STRUCT
      Provider name: TrainingRepositoryAuthConfig
      Description: An object containing authentication information for a private Docker registry containing your training images.
      • training_repository_credentials_provider_arn
        Type: STRING
        Provider name: TrainingRepositoryCredentialsProviderArn
        Description: The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function used to give SageMaker access credentials to your private Docker registry.
  • training_input_mode
    Type: STRING
    Provider name: TrainingInputMode

auto_ml_job_arn

Type: STRING
Provider name: AutoMLJobArn
Description: The Amazon Resource Name (ARN) of an AutoML job.

billable_time_in_seconds

Type: INT32
Provider name: BillableTimeInSeconds
Description: The billable time in seconds. Billable time refers to the absolute wall-clock time. Multiply BillableTimeInSeconds by the number of instances (InstanceCount) in your training cluster to get the total compute time SageMaker bills you if you run distributed training. The formula is as follows: BillableTimeInSeconds * InstanceCount . You can calculate the savings from using managed spot training using the formula (1 - BillableTimeInSeconds / TrainingTimeInSeconds) * 100. For example, if BillableTimeInSeconds is 100 and TrainingTimeInSeconds is 500, the savings is 80%.

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.

creation_time

Type: TIMESTAMP
Provider name: CreationTime
Description: A timestamp that indicates when the training job was created.

debug_hook_config

Type: STRUCT
Provider name: DebugHookConfig

  • collection_configurations
    Type: UNORDERED_LIST_STRUCT
    Provider name: CollectionConfigurations
    Description: Configuration information for Amazon SageMaker Debugger tensor collections. To learn more about how to configure the CollectionConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.
    • collection_name
      Type: STRING
      Provider name: CollectionName
      Description: The name of the tensor collection. The name must be unique relative to other rule configuration names.
    • collection_parameters
      Type: MAP_STRING_STRING
      Provider name: CollectionParameters
      Description: Parameter values for the tensor collection. The allowed parameters are “name”, “include_regex”, “reduction_config”, “save_config”, “tensor_names”, and “save_histogram”.
  • hook_parameters
    Type: MAP_STRING_STRING
    Provider name: HookParameters
    Description: Configuration information for the Amazon SageMaker Debugger hook parameters.
  • local_path
    Type: STRING
    Provider name: LocalPath
    Description: Path to local storage location for metrics and tensors. Defaults to /opt/ml/output/tensors/.
  • s3_output_path
    Type: STRING
    Provider name: S3OutputPath
    Description: Path to Amazon S3 storage location for metrics and tensors.

debug_rule_configurations

Type: UNORDERED_LIST_STRUCT
Provider name: DebugRuleConfigurations
Description: Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.

  • instance_type
    Type: STRING
    Provider name: InstanceType
    Description: The instance type to deploy a custom rule for debugging a training job.
  • local_path
    Type: STRING
    Provider name: LocalPath
    Description: Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/.
  • rule_configuration_name
    Type: STRING
    Provider name: RuleConfigurationName
    Description: The name of the rule configuration. It must be unique relative to other rule configuration names.
  • rule_evaluator_image
    Type: STRING
    Provider name: RuleEvaluatorImage
    Description: The Amazon Elastic Container (ECR) Image for the managed rule evaluation.
  • rule_parameters
    Type: MAP_STRING_STRING
    Provider name: RuleParameters
    Description: Runtime configuration for rule container.
  • s3_output_path
    Type: STRING
    Provider name: S3OutputPath
    Description: Path to Amazon S3 storage location for rules.
  • volume_size_in_gb
    Type: INT32
    Provider name: VolumeSizeInGB
    Description: The size, in GB, of the ML storage volume attached to the processing instance.

debug_rule_evaluation_statuses

Type: UNORDERED_LIST_STRUCT
Provider name: DebugRuleEvaluationStatuses
Description: Evaluation status of Amazon SageMaker Debugger rules for debugging on a training job.

  • last_modified_time
    Type: TIMESTAMP
    Provider name: LastModifiedTime
    Description: Timestamp when the rule evaluation status was last modified.
  • rule_configuration_name
    Type: STRING
    Provider name: RuleConfigurationName
    Description: The name of the rule configuration.
  • rule_evaluation_job_arn
    Type: STRING
    Provider name: RuleEvaluationJobArn
    Description: The Amazon Resource Name (ARN) of the rule evaluation job.
  • rule_evaluation_status
    Type: STRING
    Provider name: RuleEvaluationStatus
    Description: Status of the rule evaluation.
  • status_details
    Type: STRING
    Provider name: StatusDetails
    Description: Details from the rule evaluation.

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 algorithms 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: If you want to allow inbound or outbound network calls, except for calls between peers within a training cluster for distributed training, choose True. If you enable network isolation 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: The environment variables to set in the Docker container. Do not include any security-sensitive information including account access IDs, secrets, or tokens in any environment fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by security-sensitive information included in the request environment variable or plain text fields.

experiment_config

Type: STRUCT
Provider name: ExperimentConfig

  • experiment_name
    Type: STRING
    Provider name: ExperimentName
    Description: The name of an existing experiment to associate with the trial component.
  • run_name
    Type: STRING
    Provider name: RunName
    Description: The name of the experiment run to associate with the trial component.
  • trial_component_display_name
    Type: STRING
    Provider name: TrialComponentDisplayName
    Description: The display name for the trial component. If this key isn’t specified, the display name is the trial component name.
  • trial_name
    Type: STRING
    Provider name: TrialName
    Description: The name of an existing trial to associate the trial component with. If not specified, a new trial is created.

failure_reason

Type: STRING
Provider name: FailureReason
Description: If the training job failed, the reason it failed.

final_metric_data_list

Type: UNORDERED_LIST_STRUCT
Provider name: FinalMetricDataList
Description: A collection of MetricData objects that specify the names, values, and dates and times that the training algorithm emitted to Amazon CloudWatch.

  • metric_name
    Type: STRING
    Provider name: MetricName
    Description: The name of the metric.
  • timestamp
    Type: TIMESTAMP
    Provider name: Timestamp
    Description: The date and time that the algorithm emitted the metric.
  • value
    Type: FLOAT
    Provider name: Value
    Description: The value of the metric.

hyper_parameters

Type: MAP_STRING_STRING
Provider name: HyperParameters
Description: Algorithm-specific parameters.

infra_check_config

Type: STRUCT
Provider name: InfraCheckConfig
Description: Contains information about the infrastructure health check configuration for the training job.

  • enable_infra_check
    Type: BOOLEAN
    Provider name: EnableInfraCheck
    Description: Enables an infrastructure health check.

input_data_config

Type: UNORDERED_LIST_STRUCT
Provider name: InputDataConfig
Description: An array of Channel objects that describes each data input channel.

  • 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 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. 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.

labeling_job_arn

Type: STRING
Provider name: LabelingJobArn
Description: The Amazon Resource Name (ARN) of the SageMaker Ground Truth labeling job that created the transform or training job.

last_modified_time

Type: TIMESTAMP
Provider name: LastModifiedTime
Description: A timestamp that indicates when the status of the training job was last modified.

model_artifacts

Type: STRUCT
Provider name: ModelArtifacts
Description: Information about the Amazon S3 location that is configured for storing model artifacts.

  • s3_model_artifacts
    Type: STRING
    Provider name: S3ModelArtifacts
    Description: The path of the S3 object that contains the model artifacts. For example, s3://bucket-name/keynameprefix/model.tar.gz.

output_data_config

Type: STRUCT
Provider name: OutputDataConfig
Description: The S3 path where model artifacts that you configured when creating the job are stored. SageMaker creates subfolders for model artifacts.

  • 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 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 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.

profiler_config

Type: STRUCT
Provider name: ProfilerConfig

  • disable_profiler
    Type: BOOLEAN
    Provider name: DisableProfiler
    Description: Configuration to turn off Amazon SageMaker Debugger’s system monitoring and profiling functionality. To turn it off, set to True.
  • profiling_interval_in_milliseconds
    Type: INT64
    Provider name: ProfilingIntervalInMilliseconds
    Description: A time interval for capturing system metrics in milliseconds. Available values are 100, 200, 500, 1000 (1 second), 5000 (5 seconds), and 60000 (1 minute) milliseconds. The default value is 500 milliseconds.
  • profiling_parameters
    Type: MAP_STRING_STRING
    Provider name: ProfilingParameters
    Description: Configuration information for capturing framework metrics. Available key strings for different profiling options are DetailedProfilingConfig, PythonProfilingConfig, and DataLoaderProfilingConfig. The following codes are configuration structures for the ProfilingParameters parameter. To learn more about how to configure the ProfilingParameters parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.
  • s3_output_path
    Type: STRING
    Provider name: S3OutputPath
    Description: Path to Amazon S3 storage location for system and framework metrics.

profiler_rule_configurations

Type: UNORDERED_LIST_STRUCT
Provider name: ProfilerRuleConfigurations
Description: Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.

  • instance_type
    Type: STRING
    Provider name: InstanceType
    Description: The instance type to deploy a custom rule for profiling a training job.
  • local_path
    Type: STRING
    Provider name: LocalPath
    Description: Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/.
  • rule_configuration_name
    Type: STRING
    Provider name: RuleConfigurationName
    Description: The name of the rule configuration. It must be unique relative to other rule configuration names.
  • rule_evaluator_image
    Type: STRING
    Provider name: RuleEvaluatorImage
    Description: The Amazon Elastic Container Registry Image for the managed rule evaluation.
  • rule_parameters
    Type: MAP_STRING_STRING
    Provider name: RuleParameters
    Description: Runtime configuration for rule container.
  • s3_output_path
    Type: STRING
    Provider name: S3OutputPath
    Description: Path to Amazon S3 storage location for rules.
  • volume_size_in_gb
    Type: INT32
    Provider name: VolumeSizeInGB
    Description: The size, in GB, of the ML storage volume attached to the processing instance.

profiler_rule_evaluation_statuses

Type: UNORDERED_LIST_STRUCT
Provider name: ProfilerRuleEvaluationStatuses
Description: Evaluation status of Amazon SageMaker Debugger rules for profiling on a training job.

  • last_modified_time
    Type: TIMESTAMP
    Provider name: LastModifiedTime
    Description: Timestamp when the rule evaluation status was last modified.
  • rule_configuration_name
    Type: STRING
    Provider name: RuleConfigurationName
    Description: The name of the rule configuration.
  • rule_evaluation_job_arn
    Type: STRING
    Provider name: RuleEvaluationJobArn
    Description: The Amazon Resource Name (ARN) of the rule evaluation job.
  • rule_evaluation_status
    Type: STRING
    Provider name: RuleEvaluationStatus
    Description: Status of the rule evaluation.
  • status_details
    Type: STRING
    Provider name: StatusDetails
    Description: Details from the rule evaluation.

profiling_status

Type: STRING
Provider name: ProfilingStatus
Description: Profiling status of a training job.

remote_debug_config

Type: STRUCT
Provider name: RemoteDebugConfig
Description: Configuration for remote debugging. To learn more about the remote debugging functionality of SageMaker, see Access a training container through Amazon Web Services Systems Manager (SSM) for remote debugging.

  • enable_remote_debug
    Type: BOOLEAN
    Provider name: EnableRemoteDebug
    Description: If set to True, enables remote debugging.

resource_config

Type: STRUCT
Provider name: ResourceConfig
Description: Resources, including ML compute instances and ML storage volumes, that are configured for model training.

  • 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 (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. For more information about local instance storage encryption, see SSD Instance Store Volumes. 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, 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. 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.

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 Web Services Identity and Access Management (IAM) role configured for the training job.

secondary_status

Type: STRING
Provider name: SecondaryStatus
Description: Provides detailed information about the state of the training job. For detailed information on the secondary status of the training job, see StatusMessage under SecondaryStatusTransition. SageMaker provides primary statuses and secondary statuses that apply to each of them:

InProgress
  • Starting - Starting the training job.
  • Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.
  • Training - Training is in progress.
  • Interrupted - The job stopped because the managed spot training instances were interrupted.
  • Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.
Completed
  • Completed - The training job has completed.
Failed
  • Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse.
Stopped
  • MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
  • MaxWaitTimeExceeded - The job stopped because it exceeded the maximum allowed wait time.
  • Stopped - The training job has stopped.
Stopping
  • Stopping - Stopping the training job.
Valid values for SecondaryStatus are subject to change. We no longer support the following secondary statuses:
  • LaunchingMLInstances
  • PreparingTraining
  • DownloadingTrainingImage

secondary_status_transitions

Type: UNORDERED_LIST_STRUCT
Provider name: SecondaryStatusTransitions
Description: A history of all of the secondary statuses that the training job has transitioned through.

  • end_time
    Type: TIMESTAMP
    Provider name: EndTime
    Description: A timestamp that shows when the training job transitioned out of this secondary status state into another secondary status state or when the training job has ended.
  • start_time
    Type: TIMESTAMP
    Provider name: StartTime
    Description: A timestamp that shows when the training job transitioned to the current secondary status state.
  • status
    Type: STRING
    Provider name: Status
    Description: Contains a secondary status information from a training job. Status might be one of the following secondary statuses:
    InProgress
    • Starting - Starting the training job.
    • Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.
    • Training - Training is in progress.
    • Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.
    Completed
    • Completed - The training job has completed.
    Failed
    • Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse.
    Stopped
    • MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
    • Stopped - The training job has stopped.
    Stopping
    • Stopping - Stopping the training job.
    We no longer support the following secondary statuses:
    • LaunchingMLInstances
    • PreparingTrainingStack
    • DownloadingTrainingImage
  • status_message
    Type: STRING
    Provider name: StatusMessage
    Description: A detailed description of the progress within a secondary status. SageMaker provides secondary statuses and status messages that apply to each of them:
    Starting
    • Starting the training job.
    • Launching requested ML instances.
    • Insufficient capacity error from EC2 while launching instances, retrying!
    • Launched instance was unhealthy, replacing it!
    • Preparing the instances for training.
    Training
    • Training image download completed. Training in progress.
    Status messages are subject to change. Therefore, we recommend not including them in code that programmatically initiates actions. For examples, don’t use status messages in if statements. To have an overview of your training job’s progress, view TrainingJobStatus and SecondaryStatus in DescribeTrainingJob, and StatusMessage together. For example, at the start of a training job, you might see the following:
    • TrainingJobStatus - InProgress
    • SecondaryStatus - Training
    • StatusMessage - Downloading the training image

stopping_condition

Type: STRUCT
Provider name: StoppingCondition
Description: Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs. To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.

  • 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, 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.

tags

Type: UNORDERED_LIST_STRING

tensor_board_output_config

Type: STRUCT
Provider name: TensorBoardOutputConfig

  • local_path
    Type: STRING
    Provider name: LocalPath
    Description: Path to local storage location for tensorBoard output. Defaults to /opt/ml/output/tensorboard.
  • s3_output_path
    Type: STRING
    Provider name: S3OutputPath
    Description: Path to Amazon S3 storage location for TensorBoard output.

training_end_time

Type: TIMESTAMP
Provider name: TrainingEndTime
Description: Indicates 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_name

Type: STRING
Provider name: TrainingJobName
Description: Name of the model training job.

training_job_status

Type: STRING
Provider name: TrainingJobStatus
Description: The status of the training job. SageMaker provides the following training job statuses:

  • InProgress - The training is in progress.
  • Completed - The training job has completed.
  • Failed - The training job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTrainingJobResponse call.
  • Stopping - The training job is stopping.
  • Stopped - The training job has stopped.
For more detailed information, see SecondaryStatus.

training_start_time

Type: TIMESTAMP
Provider name: TrainingStartTime
Description: Indicates the time when the training job starts on training instances. You are billed for the time interval between this time and the value of TrainingEndTime. The start time in CloudWatch Logs might be later than this time. The difference is due to the time it takes to download the training data and to the size of the training container.

training_time_in_seconds

Type: INT32
Provider name: TrainingTimeInSeconds
Description: The training time in seconds.

tuning_job_arn

Type: STRING
Provider name: TuningJobArn
Description: The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.

vpc_config

Type: STRUCT
Provider name: VpcConfig
Description: A VpcConfig object that specifies the VPC that this training job has access to. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.

  • 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.

warm_pool_status

Type: STRUCT
Provider name: WarmPoolStatus
Description: The status of the warm pool associated with the training job.

  • resource_retained_billable_time_in_seconds
    Type: INT32
    Provider name: ResourceRetainedBillableTimeInSeconds
    Description: The billable time in seconds used by the warm pool. Billable time refers to the absolute wall-clock time. Multiply ResourceRetainedBillableTimeInSeconds by the number of instances (InstanceCount) in your training cluster to get the total compute time SageMaker bills you if you run warm pool training. The formula is as follows: ResourceRetainedBillableTimeInSeconds * InstanceCount.
  • reused_by_job
    Type: STRING
    Provider name: ReusedByJob
    Description: The name of the matching training job that reused the warm pool.
  • status
    Type: STRING
    Provider name: Status
    Description: The status of the warm pool.
    • InUse: The warm pool is in use for the training job.
    • Available: The warm pool is available to reuse for a matching training job.
    • Reused: The warm pool moved to a matching training job for reuse.
    • Terminated: The warm pool is no longer available. Warm pools are unavailable if they are terminated by a user, terminated for a patch update, or terminated for exceeding the specified KeepAlivePeriodInSeconds.