| Column Name | Column Data Type |
|---|
training_job_name Required Input Column
Name of the model training job. | VARCHAR |
_aws_profile Input Column
The AWS profile defines the AWS identity used. It can be defined via credentials or by assuming a IAM role. | STRUCT(
"type" VARCHAR,
"name" VARCHAR,
"account_id" VARCHAR,
"via_profile_name" VARCHAR,
"assumed_role_arn" VARCHAR,
"organization" STRUCT(
"account_name" VARCHAR,
"id" VARCHAR,
"tags" STRUCT(
"key" VARCHAR,
"value" VARCHAR
)[],
"master_account" STRUCT(
"id" VARCHAR,
"email" VARCHAR
),
"parents" STRUCT(
"type" VARCHAR,
"id" VARCHAR,
"name" VARCHAR,
"tags" STRUCT(
"key" VARCHAR,
"value" VARCHAR
)[]
)[]
)
) |
Show child fields- _aws_profile.account_id
The AWS account id - _aws_profile.assumed_role_arn
The ARN of the assumed role - _aws_profile.name
The unique name of the profile. - _aws_profile.organization
Information about this profile's membership in the AWS organization. Show child fields- _aws_profile.organization.account_name
The name of account speciifed by the organization - _aws_profile.organization.id
The organization id - _aws_profile.organization.master_account
Show child fields- _aws_profile.organization.master_account.email
The organization master account email address - _aws_profile.organization.master_account.id
The organization master account id
- _aws_profile.organization.parents[]
Show child fields- _aws_profile.organization.parents[].id
The id of the parent - _aws_profile.organization.parents[].name
The name of the parent - _aws_profile.organization.parents[].tags[]
Show child fields- _aws_profile.organization.parents[].tags[].key
- _aws_profile.organization.parents[].tags[].value
- _aws_profile.organization.parents[].type
The type of parent can be an organization unit or a root
- _aws_profile.organization.tags[]
Show child fields- _aws_profile.organization.tags[].key
- _aws_profile.organization.tags[].value
- _aws_profile.type
The type of profile, either 'credentials' or 'assumed_role' - _aws_profile.via_profile_name
This IAM role for this profile is assumed by first utilizing another profile with this name to obtain credentials.
|
algorithm_specification
Information about the algorithm used for training, and algorithm metadata. | STRUCT(
"training_image" VARCHAR,
"algorithm_name" VARCHAR,
"training_input_mode" VARCHAR,
"metric_definitions" STRUCT(
"name" VARCHAR,
"regex" VARCHAR
)[],
"enable_sage_maker_metrics_time_series" BOOLEAN,
"container_entrypoint" VARCHAR[],
"container_arguments" VARCHAR[],
"training_image_config" STRUCT(
"training_repository_access_mode" VARCHAR,
"training_repository_auth_config" STRUCT(
"training_repository_credentials_provider_arn" VARCHAR
)
)
) |
Show child fields- algorithm_specification.algorithm_name
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. - algorithm_specification.container_arguments[]
- algorithm_specification.container_entrypoint[]
- algorithm_specification.enable_sage_maker_metrics_time_series
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: - algorithm_specification.metric_definitions[]
Show child fields- algorithm_specification.metric_definitions[].name
The name of the metric. - algorithm_specification.metric_definitions[].regex
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.
- algorithm_specification.training_image
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. - algorithm_specification.training_image_config
The configuration to use an image from a private Docker registry for a training job. Show child fields- algorithm_specification.training_image_config.training_repository_access_mode
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. - algorithm_specification.training_image_config.training_repository_auth_config
An object containing authentication information for a private Docker registry containing your training images. Show child fields- algorithm_specification.training_image_config.training_repository_auth_config.training_repository_credentials_provider_arn
The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function used to give SageMaker access credentials to your private Docker registry.
- algorithm_specification.training_input_mode
The training input mode that the algorithm supports. For more information about input modes, see Algorithms. Pipe mode If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container. File mode If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container. You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any. For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training. FastFile mode If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk. FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.
|
auto_ml_job_arn
The Amazon Resource Name (ARN) of an AutoML job. | VARCHAR |
billable_time_in_seconds
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%. | BIGINT |
checkpoint_config
Contains information about the output location for managed spot training checkpoint data. | STRUCT(
"s3_uri" VARCHAR,
"local_path" VARCHAR
) |
Show child fields- checkpoint_config.local_path
(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/. - checkpoint_config.s3_uri
Identifies the S3 path where you want SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix.
|
creation_time
A timestamp that indicates when the training job was created. | TIMESTAMP_S |
debug_hook_config
Configuration information for the Amazon SageMaker Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the DebugHookConfig parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job. | STRUCT(
"local_path" VARCHAR,
"s3_output_path" VARCHAR,
"hook_parameters" MAP(VARCHAR, VARCHAR),
"collection_configurations" STRUCT(
"collection_name" VARCHAR,
"collection_parameters" MAP(VARCHAR, VARCHAR)
)[]
) |
Show child fields- debug_hook_config.collection_configurations[]
Show child fields- debug_hook_config.collection_configurations[].collection_name
The name of the tensor collection. The name must be unique relative to other rule configuration names. - debug_hook_config.collection_configurations[].collection_parameters
Parameter values for the tensor collection. The allowed parameters are "name", "include_regex", "reduction_config", "save_config", "tensor_names", and "save_histogram".
- debug_hook_config.hook_parameters
Configuration information for the Amazon SageMaker Debugger hook parameters. - debug_hook_config.local_path
Path to local storage location for metrics and tensors. Defaults to /opt/ml/output/tensors/. - debug_hook_config.s3_output_path
Path to Amazon S3 storage location for metrics and tensors.
|
debug_rule_configurations
Configuration information for Amazon SageMaker Debugger rules for debugging output tensors. | STRUCT(
"rule_configuration_name" VARCHAR,
"local_path" VARCHAR,
"s3_output_path" VARCHAR,
"rule_evaluator_image" VARCHAR,
"instance_type" VARCHAR,
"volume_size_in_gb" BIGINT,
"rule_parameters" MAP(VARCHAR, VARCHAR)
)[] |
Show child fields- debug_rule_configurations[]
Show child fields- debug_rule_configurations[].instance_type
The instance type to deploy a custom rule for debugging a training job. - debug_rule_configurations[].local_path
Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/. - debug_rule_configurations[].rule_configuration_name
The name of the rule configuration. It must be unique relative to other rule configuration names. - debug_rule_configurations[].rule_evaluator_image
The Amazon Elastic Container (ECR) Image for the managed rule evaluation. - debug_rule_configurations[].rule_parameters
Runtime configuration for rule container. - debug_rule_configurations[].s3_output_path
Path to Amazon S3 storage location for rules. - debug_rule_configurations[].volume_size_in_gb
The size, in GB, of the ML storage volume attached to the processing instance.
|
debug_rule_evaluation_statuses
Evaluation status of Amazon SageMaker Debugger rules for debugging on a training job. | STRUCT(
"rule_configuration_name" VARCHAR,
"rule_evaluation_job_arn" VARCHAR,
"rule_evaluation_status" VARCHAR,
"status_details" VARCHAR,
"last_modified_time" TIMESTAMP_S
)[] |
Show child fields- debug_rule_evaluation_statuses[]
Show child fields- debug_rule_evaluation_statuses[].last_modified_time
Timestamp when the rule evaluation status was last modified. - debug_rule_evaluation_statuses[].rule_configuration_name
The name of the rule configuration. - debug_rule_evaluation_statuses[].rule_evaluation_job_arn
The Amazon Resource Name (ARN) of the rule evaluation job. - debug_rule_evaluation_statuses[].rule_evaluation_status
Status of the rule evaluation. - debug_rule_evaluation_statuses[].status_details
Details from the rule evaluation.
|
enable_inter_container_traffic_encryption
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. | BOOLEAN |
enable_managed_spot_training
A Boolean indicating whether managed spot training is enabled (True) or not (False). | BOOLEAN |
enable_network_isolation
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. | BOOLEAN |
environment
The environment variables to set in the Docker container. | MAP(VARCHAR, VARCHAR) |
experiment_config
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs: | STRUCT(
"experiment_name" VARCHAR,
"trial_name" VARCHAR,
"trial_component_display_name" VARCHAR,
"run_name" VARCHAR
) |
Show child fields- experiment_config.experiment_name
The name of an existing experiment to associate with the trial component. - experiment_config.run_name
The name of the experiment run to associate with the trial component. - experiment_config.trial_component_display_name
The display name for the trial component. If this key isn't specified, the display name is the trial component name. - experiment_config.trial_name
The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
|
failure_reason
If the training job failed, the reason it failed. | VARCHAR |
final_metric_data_list
A collection of MetricData objects that specify the names, values, and dates and times that the training algorithm emitted to Amazon CloudWatch. | STRUCT(
"metric_name" VARCHAR,
"value" DOUBLE,
"timestamp" TIMESTAMP_S
)[] |
Show child fields- final_metric_data_list[]
Show child fields- final_metric_data_list[].metric_name
The name of the metric. - final_metric_data_list[].timestamp
The date and time that the algorithm emitted the metric. - final_metric_data_list[].value
The value of the metric.
|
hyper_parameters
Algorithm-specific parameters. | MAP(VARCHAR, VARCHAR) |
infra_check_config
Contains information about the infrastructure health check configuration for the training job. | STRUCT(
"enable_infra_check" BOOLEAN
) |
Show child fields- infra_check_config.enable_infra_check
Enables an infrastructure health check.
|
input_data_config
An array of Channel objects that describes each data input channel. | STRUCT(
"channel_name" VARCHAR,
"data_source" STRUCT(
"s3_data_source" STRUCT(
"s3_data_type" VARCHAR,
"s3_uri" VARCHAR,
"s3_data_distribution_type" VARCHAR,
"attribute_names" VARCHAR[],
"instance_group_names" VARCHAR[]
),
"file_system_data_source" STRUCT(
"file_system_id" VARCHAR,
"file_system_access_mode" VARCHAR,
"file_system_type" VARCHAR,
"directory_path" VARCHAR
)
),
"content_type" VARCHAR,
"compression_type" VARCHAR,
"record_wrapper_type" VARCHAR,
"input_mode" VARCHAR,
"shuffle_config" STRUCT(
"seed" BIGINT
)
)[] |
Show child fields- input_data_config[]
Show child fields- input_data_config[].channel_name
The name of the channel. - input_data_config[].compression_type
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. - input_data_config[].content_type
The MIME type of the data. - input_data_config[].data_source
The location of the channel data. Show child fields- input_data_config[].data_source.file_system_data_source
The file system that is associated with a channel. Show child fields- input_data_config[].data_source.file_system_data_source.directory_path
The full path to the directory to associate with the channel. - input_data_config[].data_source.file_system_data_source.file_system_access_mode
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. - input_data_config[].data_source.file_system_data_source.file_system_id
The file system id. - input_data_config[].data_source.file_system_data_source.file_system_type
The file system type.
- input_data_config[].data_source.s3_data_source
The S3 location of the data source that is associated with a channel. Show child fields- input_data_config[].data_source.s3_data_source.attribute_names[]
- input_data_config[].data_source.s3_data_source.instance_group_names[]
- input_data_config[].data_source.s3_data_source.s3_data_distribution_type
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. - input_data_config[].data_source.s3_data_source.s3_data_type
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. - input_data_config[].data_source.s3_data_source.s3_uri
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_data_config[].input_mode
(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. - input_data_config[].record_wrapper_type
-
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.
- input_data_config[].shuffle_config
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. Show child fields- input_data_config[].shuffle_config.seed
Determines the shuffling order in ShuffleConfig value.
|
labeling_job_arn
The Amazon Resource Name (ARN) of the SageMaker Ground Truth labeling job that created the transform or training job. | VARCHAR |
last_modified_time
A timestamp that indicates when the status of the training job was last modified. | TIMESTAMP_S |
model_artifacts
Information about the Amazon S3 location that is configured for storing model artifacts. | STRUCT(
"s3_model_artifacts" VARCHAR
) |
Show child fields- model_artifacts.s3_model_artifacts
The path of the S3 object that contains the model artifacts. For example, s3://bucket-name/keynameprefix/model.tar.gz.
|
output_data_config
The S3 path where model artifacts that you configured when creating the job are stored. SageMaker creates subfolders for model artifacts. | STRUCT(
"kms_key_id" VARCHAR,
"s3_output_path" VARCHAR,
"compression_type" VARCHAR
) |
Show child fields- output_data_config.compression_type
The model output compression type. Select None to output an uncompressed model, recommended for large model outputs. Defaults to gzip. - output_data_config.kms_key_id
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. - output_data_config.s3_output_path
Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix.
|
profiler_config
Configuration information for Amazon SageMaker Debugger system monitoring, framework profiling, and storage paths. | STRUCT(
"s3_output_path" VARCHAR,
"profiling_interval_in_milliseconds" BIGINT,
"profiling_parameters" MAP(VARCHAR, VARCHAR),
"disable_profiler" BOOLEAN
) |
Show child fields- profiler_config.disable_profiler
Configuration to turn off Amazon SageMaker Debugger's system monitoring and profiling functionality. To turn it off, set to True. - profiler_config.profiling_interval_in_milliseconds
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. - profiler_config.profiling_parameters
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. - profiler_config.s3_output_path
Path to Amazon S3 storage location for system and framework metrics.
|
profiler_rule_configurations
Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics. | STRUCT(
"rule_configuration_name" VARCHAR,
"local_path" VARCHAR,
"s3_output_path" VARCHAR,
"rule_evaluator_image" VARCHAR,
"instance_type" VARCHAR,
"volume_size_in_gb" BIGINT,
"rule_parameters" MAP(VARCHAR, VARCHAR)
)[] |
Show child fields- profiler_rule_configurations[]
Show child fields- profiler_rule_configurations[].instance_type
The instance type to deploy a custom rule for profiling a training job. - profiler_rule_configurations[].local_path
Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/. - profiler_rule_configurations[].rule_configuration_name
The name of the rule configuration. It must be unique relative to other rule configuration names. - profiler_rule_configurations[].rule_evaluator_image
The Amazon Elastic Container Registry Image for the managed rule evaluation. - profiler_rule_configurations[].rule_parameters
Runtime configuration for rule container. - profiler_rule_configurations[].s3_output_path
Path to Amazon S3 storage location for rules. - profiler_rule_configurations[].volume_size_in_gb
The size, in GB, of the ML storage volume attached to the processing instance.
|
profiler_rule_evaluation_statuses
Evaluation status of Amazon SageMaker Debugger rules for profiling on a training job. | STRUCT(
"rule_configuration_name" VARCHAR,
"rule_evaluation_job_arn" VARCHAR,
"rule_evaluation_status" VARCHAR,
"status_details" VARCHAR,
"last_modified_time" TIMESTAMP_S
)[] |
Show child fields- profiler_rule_evaluation_statuses[]
Show child fields- profiler_rule_evaluation_statuses[].last_modified_time
Timestamp when the rule evaluation status was last modified. - profiler_rule_evaluation_statuses[].rule_configuration_name
The name of the rule configuration. - profiler_rule_evaluation_statuses[].rule_evaluation_job_arn
The Amazon Resource Name (ARN) of the rule evaluation job. - profiler_rule_evaluation_statuses[].rule_evaluation_status
Status of the rule evaluation. - profiler_rule_evaluation_statuses[].status_details
Details from the rule evaluation.
|
profiling_status
Profiling status of a training job. | VARCHAR |
remote_debug_config
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. | STRUCT(
"enable_remote_debug" BOOLEAN
) |
Show child fields- remote_debug_config.enable_remote_debug
If set to True, enables remote debugging.
|
resource_config
Resources, including ML compute instances and ML storage volumes, that are configured for model training. | STRUCT(
"instance_type" VARCHAR,
"instance_count" BIGINT,
"volume_size_in_gb" BIGINT,
"volume_kms_key_id" VARCHAR,
"keep_alive_period_in_seconds" BIGINT,
"instance_groups" STRUCT(
"instance_type" VARCHAR,
"instance_count" BIGINT,
"instance_group_name" VARCHAR
)[]
) |
Show child fields- resource_config.instance_count
The number of ML compute instances to use. For distributed training, provide a value greater than 1. - resource_config.instance_groups[]
Show child fields- resource_config.instance_groups[].instance_count
Specifies the number of instances of the instance group. - resource_config.instance_groups[].instance_group_name
Specifies the name of the instance group. - resource_config.instance_groups[].instance_type
Specifies the instance type of the instance group.
- resource_config.instance_type
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. To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team. - resource_config.keep_alive_period_in_seconds
The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs. - resource_config.volume_kms_key_id
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" - resource_config.volume_size_in_gb
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.
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retry_strategy
The number of times to retry the job when the job fails due to an InternalServerError. | STRUCT(
"maximum_retry_attempts" BIGINT
) |
Show child fields- retry_strategy.maximum_retry_attempts
The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING.
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role_arn
The Amazon Web Services Identity and Access Management (IAM) role configured for the training job. | VARCHAR |
secondary_status
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
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- Failed
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- 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
-
Valid values for SecondaryStatus are subject to change. We no longer support the following secondary statuses: -
LaunchingMLInstances -
PreparingTraining -
DownloadingTrainingImage | VARCHAR |
secondary_status_transitions
A history of all of the secondary statuses that the training job has transitioned through. | STRUCT(
"status" VARCHAR,
"start_time" TIMESTAMP_S,
"end_time" TIMESTAMP_S,
"status_message" VARCHAR
)[] |
Show child fields- secondary_status_transitions[]
Show child fields- secondary_status_transitions[].end_time
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. - secondary_status_transitions[].start_time
A timestamp that shows when the training job transitioned to the current secondary status state. - secondary_status_transitions[].status
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
-
- Failed
-
- Stopped
-
- Stopping
-
We no longer support the following secondary statuses: -
LaunchingMLInstances -
PreparingTrainingStack -
DownloadingTrainingImage - secondary_status_transitions[].status_message
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
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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
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stopping_condition
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. | STRUCT(
"max_runtime_in_seconds" BIGINT,
"max_wait_time_in_seconds" BIGINT,
"max_pending_time_in_seconds" BIGINT
) |
Show child fields- stopping_condition.max_pending_time_in_seconds
The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped. - stopping_condition.max_runtime_in_seconds
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. - stopping_condition.max_wait_time_in_seconds
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.
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tensor_board_output_config
Configuration of storage locations for the Amazon SageMaker Debugger TensorBoard output data. | STRUCT(
"local_path" VARCHAR,
"s3_output_path" VARCHAR
) |
Show child fields- tensor_board_output_config.local_path
Path to local storage location for tensorBoard output. Defaults to /opt/ml/output/tensorboard. - tensor_board_output_config.s3_output_path
Path to Amazon S3 storage location for TensorBoard output.
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training_end_time
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. | TIMESTAMP_S |
training_job_arn
The Amazon Resource Name (ARN) of the training job. | VARCHAR |
training_job_status
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. | VARCHAR |
training_start_time
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. | TIMESTAMP_S |
training_time_in_seconds
The training time in seconds. | BIGINT |
tuning_job_arn
The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job. | VARCHAR |
vpc_config
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. | STRUCT(
"security_group_ids" VARCHAR[],
"subnets" VARCHAR[]
) |
Show child fields- vpc_config.security_group_ids[]
- vpc_config.subnets[]
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warm_pool_status
The status of the warm pool associated with the training job. | STRUCT(
"status" VARCHAR,
"resource_retained_billable_time_in_seconds" BIGINT,
"reused_by_job" VARCHAR
) |
Show child fields- warm_pool_status.resource_retained_billable_time_in_seconds
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. - warm_pool_status.reused_by_job
The name of the matching training job that reused the warm pool. - warm_pool_status.status
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.
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