[ aws . sagemaker ]

describe-training-job

Description

Returns information about a training job.

Some of the attributes below only appear if the training job successfully starts. If the training job fails, TrainingJobStatus is Failed and, depending on the FailureReason , attributes like TrainingStartTime , TrainingTimeInSeconds , TrainingEndTime , and BillableTimeInSeconds may not be present in the response.

See also: AWS API Documentation

See ‘aws help’ for descriptions of global parameters.

Synopsis

  describe-training-job
--training-job-name <value>
[--cli-input-json | --cli-input-yaml]
[--generate-cli-skeleton <value>]

Options

--training-job-name (string)

The name of the training job.

--cli-input-json | --cli-input-yaml (string) Reads arguments from the JSON string provided. The JSON string follows the format provided by --generate-cli-skeleton. If other arguments are provided on the command line, those values will override the JSON-provided values. It is not possible to pass arbitrary binary values using a JSON-provided value as the string will be taken literally. This may not be specified along with --cli-input-yaml.

--generate-cli-skeleton (string) Prints a JSON skeleton to standard output without sending an API request. If provided with no value or the value input, prints a sample input JSON that can be used as an argument for --cli-input-json. Similarly, if provided yaml-input it will print a sample input YAML that can be used with --cli-input-yaml. If provided with the value output, it validates the command inputs and returns a sample output JSON for that command.

See ‘aws help’ for descriptions of global parameters.

Output

TrainingJobName -> (string)

Name of the model training job.

TrainingJobArn -> (string)

The Amazon Resource Name (ARN) of the training job.

TuningJobArn -> (string)

The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.

LabelingJobArn -> (string)

The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.

AutoMLJobArn -> (string)

The Amazon Resource Name (ARN) of an AutoML job.

ModelArtifacts -> (structure)

Information about the Amazon S3 location that is configured for storing model artifacts.

S3ModelArtifacts -> (string)

The path of the S3 object that contains the model artifacts. For example, s3://bucket-name/keynameprefix/model.tar.gz .

TrainingJobStatus -> (string)

The status of the training job.

Amazon 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 .

SecondaryStatus -> (string)

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 .

Amazon 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.

Warning

Valid values for SecondaryStatus are subject to change.

We no longer support the following secondary statuses:

• LaunchingMLInstances

• PreparingTraining

• DownloadingTrainingImage

FailureReason -> (string)

If the training job failed, the reason it failed.

HyperParameters -> (map)

Algorithm-specific parameters.

key -> (string)

value -> (string)

AlgorithmSpecification -> (structure)

Information about the algorithm used for training, and algorithm metadata.

TrainingImage -> (string)

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 . Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .

AlgorithmName -> (string)

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. If you specify a value for this parameter, you can’t specify a value for TrainingImage .

TrainingInputMode -> (string)

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.

MetricDefinitions -> (list)

A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. Amazon SageMaker publishes each metric to Amazon CloudWatch.

(structure)

Specifies a metric that the training algorithm writes to stderr or stdout . Amazon SageMakerhyperparameter tuning captures all defined metrics. You specify one metric that a hyperparameter tuning job uses as its objective metric to choose the best training job.

Name -> (string)

The name of the metric.

Regex -> (string)

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 Objective Metrics .

EnableSageMakerMetricsTimeSeries -> (boolean)

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:

• You use one of the Amazon SageMaker built-in algorithms

• You use one of the following Prebuilt Amazon SageMaker Docker Images :

  • Tensorflow (version >= 1.15)

  • MXNet (version >= 1.6)

  • PyTorch (version >= 1.3)

• You specify at least one MetricDefinition

RoleArn -> (string)

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

InputDataConfig -> (list)

An array of Channel objects that describes each data input channel.

(structure)

A channel is a named input source that training algorithms can consume.

ChannelName -> (string)

The name of the channel.

DataSource -> (structure)

The location of the channel data.

S3DataSource -> (structure)

The S3 location of the data source that is associated with a channel.

S3DataType -> (string)

If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon 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 Amazon 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 .

S3Uri -> (string)

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 Amazon SageMaker uses to perform tasks on your behalf.

S3DataDistributionType -> (string)

If you want Amazon SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .

If you want Amazon 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.

AttributeNames -> (list)

A list of one or more attribute names to use that are found in a specified augmented manifest file.

(string)

FileSystemDataSource -> (structure)

The file system that is associated with a channel.

FileSystemId -> (string)

The file system id.

FileSystemAccessMode -> (string)

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.

FileSystemType -> (string)

The file system type.

DirectoryPath -> (string)

The full path to the directory to associate with the channel.

ContentType -> (string)

The MIME type of the data.

CompressionType -> (string)

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.

RecordWrapperType -> (string)

Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, Amazon 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.

InputMode -> (string)

(Optional) The input mode to use for the data channel in a training job. If you don’t set a value for InputMode , Amazon 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.

ShuffleConfig -> (structure)

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 -> (long)

Determines the shuffling order in ShuffleConfig value.

OutputDataConfig -> (structure)

The S3 path where model artifacts that you configured when creating the job are stored. Amazon SageMaker creates subfolders for model artifacts.

KmsKeyId -> (string)

The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon 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 Amazon SageMaker execution role must include permissions to call kms:Encrypt . If you don’t provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role’s account. Amazon SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.

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 .

S3OutputPath -> (string)

Identifies the S3 path where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .

ResourceConfig -> (structure)

Resources, including ML compute instances and ML storage volumes, that are configured for model training.

InstanceType -> (string)

The ML compute instance type.

InstanceCount -> (integer)

The number of ML compute instances to use. For distributed training, provide a value greater than 1.

VolumeSizeInGB -> (integer)

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.

You must specify sufficient ML storage for your scenario.

Note

Amazon SageMaker supports only the General Purpose SSD (gp2) ML storage volume type.

Note

Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for training, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can’t request a VolumeSizeInGB greater than the total size of the local instance storage.

For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes .

VolumeKmsKeyId -> (string)

The Amazon Web Services KMS key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.

Note

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"

VpcConfig -> (structure)

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 .

SecurityGroupIds -> (list)

The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.

(string)

Subnets -> (list)

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 .

(string)

StoppingCondition -> (structure)

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, Amazon SageMaker ends the training job. Use this API to cap model training costs.

To stop a job, Amazon 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.

MaxRuntimeInSeconds -> (integer)

The maximum length of time, in seconds, that a training or compilation job can run.

For compilation jobs, if the job does not complete during this time, you will receive a TimeOut error. We recommend starting with 900 seconds and increase as necessary based on your model.

For all other jobs, if the job does not complete during this time, Amazon 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.

MaxWaitTimeInSeconds -> (integer)

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, Amazon 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.

CreationTime -> (timestamp)

A timestamp that indicates when the training job was created.

TrainingStartTime -> (timestamp)

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.

TrainingEndTime -> (timestamp)

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 Amazon SageMaker detects a job failure.

LastModifiedTime -> (timestamp)

A timestamp that indicates when the status of the training job was last modified.

SecondaryStatusTransitions -> (list)

A history of all of the secondary statuses that the training job has transitioned through.

(structure)

An array element of DescribeTrainingJobResponse$SecondaryStatusTransitions . It provides additional details about a status that the training job has transitioned through. A training job can be in one of several states, for example, starting, downloading, training, or uploading. Within each state, there are a number of intermediate states. For example, within the starting state, Amazon SageMaker could be starting the training job or launching the ML instances. These transitional states are referred to as the job’s secondary status.

Status -> (string)

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

StartTime -> (timestamp)

A timestamp that shows when the training job transitioned to the current secondary status state.

EndTime -> (timestamp)

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.

StatusMessage -> (string)

A detailed description of the progress within a secondary status.

Amazon 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

• Downloading the training image.

• Training image download completed. Training in progress.

Warning

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

FinalMetricDataList -> (list)

A collection of MetricData objects that specify the names, values, and dates and times that the training algorithm emitted to Amazon CloudWatch.

(structure)

The name, value, and date and time of a metric that was emitted to Amazon CloudWatch.

MetricName -> (string)

The name of the metric.

Value -> (float)

The value of the metric.

Timestamp -> (timestamp)

The date and time that the algorithm emitted the metric.

EnableNetworkIsolation -> (boolean)

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, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

EnableInterContainerTrafficEncryption -> (boolean)

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.

EnableManagedSpotTraining -> (boolean)

A Boolean indicating whether managed spot training is enabled (True ) or not (False ).

CheckpointConfig -> (structure)

Contains information about the output location for managed spot training checkpoint data.

S3Uri -> (string)

Identifies the S3 path where you want Amazon SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix .

LocalPath -> (string)

(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/ .

TrainingTimeInSeconds -> (integer)

The training time in seconds.

BillableTimeInSeconds -> (integer)

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 will bill 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%.

DebugHookConfig -> (structure)

Configuration information for the 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 .

LocalPath -> (string)

Path to local storage location for metrics and tensors. Defaults to /opt/ml/output/tensors/ .

S3OutputPath -> (string)

Path to Amazon S3 storage location for metrics and tensors.

HookParameters -> (map)

Configuration information for the Debugger hook parameters.

key -> (string)

value -> (string)

CollectionConfigurations -> (list)

Configuration information for 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 .

(structure)

Configuration information for the Debugger output tensor collections.

CollectionName -> (string)

The name of the tensor collection. The name must be unique relative to other rule configuration names.

CollectionParameters -> (map)

Parameter values for the tensor collection. The allowed parameters are "name" , "include_regex" , "reduction_config" , "save_config" , "tensor_names" , and "save_histogram" .

key -> (string)

value -> (string)

ExperimentConfig -> (structure)

Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:

• CreateProcessingJob

• CreateTrainingJob

• CreateTransformJob

ExperimentName -> (string)

The name of an existing experiment to associate the trial component with.

TrialName -> (string)

The name of an existing trial to associate the trial component with. If not specified, a new trial is created.

TrialComponentDisplayName -> (string)

The display name for the trial component. If this key isn’t specified, the display name is the trial component name.

DebugRuleConfigurations -> (list)

Configuration information for Debugger rules for debugging output tensors.

(structure)

Configuration information for SageMaker Debugger rules for debugging. To learn more about how to configure the DebugRuleConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .

RuleConfigurationName -> (string)

The name of the rule configuration. It must be unique relative to other rule configuration names.

LocalPath -> (string)

Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/ .

S3OutputPath -> (string)

Path to Amazon S3 storage location for rules.

RuleEvaluatorImage -> (string)

The Amazon Elastic Container (ECR) Image for the managed rule evaluation.

InstanceType -> (string)

The instance type to deploy a Debugger custom rule for debugging a training job.

VolumeSizeInGB -> (integer)

The size, in GB, of the ML storage volume attached to the processing instance.

RuleParameters -> (map)

Runtime configuration for rule container.

key -> (string)

value -> (string)

TensorBoardOutputConfig -> (structure)

Configuration of storage locations for the Debugger TensorBoard output data.

LocalPath -> (string)

Path to local storage location for tensorBoard output. Defaults to /opt/ml/output/tensorboard .

S3OutputPath -> (string)

Path to Amazon S3 storage location for TensorBoard output.

DebugRuleEvaluationStatuses -> (list)

Evaluation status of Debugger rules for debugging on a training job.

(structure)

Information about the status of the rule evaluation.

RuleConfigurationName -> (string)

The name of the rule configuration.

RuleEvaluationJobArn -> (string)

The Amazon Resource Name (ARN) of the rule evaluation job.

RuleEvaluationStatus -> (string)

Status of the rule evaluation.

StatusDetails -> (string)

Details from the rule evaluation.

LastModifiedTime -> (timestamp)

Timestamp when the rule evaluation status was last modified.

ProfilerConfig -> (structure)

Configuration information for Debugger system monitoring, framework profiling, and storage paths.

S3OutputPath -> (string)

Path to Amazon S3 storage location for system and framework metrics.

ProfilingIntervalInMilliseconds -> (long)

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.

ProfilingParameters -> (map)

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 .

key -> (string)

value -> (string)

ProfilerRuleConfigurations -> (list)

Configuration information for Debugger rules for profiling system and framework metrics.

(structure)

Configuration information for profiling rules.

RuleConfigurationName -> (string)

The name of the rule configuration. It must be unique relative to other rule configuration names.

LocalPath -> (string)

Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/ .

S3OutputPath -> (string)

Path to Amazon S3 storage location for rules.

RuleEvaluatorImage -> (string)

The Amazon Elastic Container (ECR) Image for the managed rule evaluation.

InstanceType -> (string)

The instance type to deploy a Debugger custom rule for profiling a training job.

VolumeSizeInGB -> (integer)

The size, in GB, of the ML storage volume attached to the processing instance.

RuleParameters -> (map)

Runtime configuration for rule container.

key -> (string)

value -> (string)

ProfilerRuleEvaluationStatuses -> (list)

Evaluation status of Debugger rules for profiling on a training job.

(structure)

Information about the status of the rule evaluation.

RuleConfigurationName -> (string)

The name of the rule configuration.

RuleEvaluationJobArn -> (string)

The Amazon Resource Name (ARN) of the rule evaluation job.

RuleEvaluationStatus -> (string)

Status of the rule evaluation.

StatusDetails -> (string)

Details from the rule evaluation.

LastModifiedTime -> (timestamp)

Timestamp when the rule evaluation status was last modified.

ProfilingStatus -> (string)

Profiling status of a training job.

RetryStrategy -> (structure)

The number of times to retry the job when the job fails due to an InternalServerError .

MaximumRetryAttempts -> (integer)

The number of times to retry the job. When the job is retried, it’s SecondaryStatus is changed to STARTING .

Environment -> (map)

The environment variables to set in the Docker container.

key -> (string)

value -> (string)