search¶

Description¶

Finds Amazon SageMaker resources that match a search query. Matching resources are returned as a list of SearchRecord objects in the response. You can sort the search results by any resource property in a ascending or descending order.

You can query against the following value types: numeric, text, Boolean, and timestamp.

Synopsis¶

  search
--resource <value>
[--search-expression <value>]
[--sort-by <value>]
[--sort-order <value>]
[--cli-input-json | --cli-input-yaml]
[--starting-token <value>]
[--page-size <value>]
[--max-items <value>]
[--generate-cli-skeleton <value>]
[--cli-auto-prompt <value>]

Options¶

--resource (string)

The name of the Amazon SageMaker resource to search for.

Possible values:

TrainingJob

Experiment

ExperimentTrial

ExperimentTrialComponent

--search-expression (structure)

A Boolean conditional statement. Resources must satisfy this condition to be included in search results. You must provide at least one subexpression, filter, or nested filter. The maximum number of recursive SubExpressions , NestedFilters , and Filters that can be included in a SearchExpression object is 50.

Filters -> (list)

A list of filter objects.

(structure)

A conditional statement for a search expression that includes a resource property, a Boolean operator, and a value. Resources that match the statement are returned in the results from the Search API.

If you specify a Value , but not an Operator , Amazon SageMaker uses the equals operator.

In search, there are several property types:

Metrics

To define a metric filter, enter a value using the form "Metrics.<name>" , where <name> is a metric name. For example, the following filter searches for training jobs with an "accuracy" metric greater than "0.9" :

{

"Name": "Metrics.accuracy",

"Operator": "GreaterThan",

"Value": "0.9"

}

HyperParameters

To define a hyperparameter filter, enter a value with the form "HyperParameters.<name>" . Decimal hyperparameter values are treated as a decimal in a comparison if the specified Value is also a decimal value. If the specified Value is an integer, the decimal hyperparameter values are treated as integers. For example, the following filter is satisfied by training jobs with a "learning_rate" hyperparameter that is less than "0.5" :

{

"Name": "HyperParameters.learning_rate",

"Operator": "LessThan",

"Value": "0.5"

}

Tags

To define a tag filter, enter a value with the form Tags.<key> .

Name -> (string)

A resource property name. For example, TrainingJobName . For valid property names, see SearchRecord . You must specify a valid property for the resource.

Operator -> (string)

A Boolean binary operator that is used to evaluate the filter. The operator field contains one of the following values:

Equals

The value of Name equals Value .

NotEquals

The value of Name doesn’t equal Value .

Exists

The Name property exists.

NotExists

The Name property does not exist.

GreaterThan

The value of Name is greater than Value . Not supported for text properties.

GreaterThanOrEqualTo

The value of Name is greater than or equal to Value . Not supported for text properties.

LessThan

The value of Name is less than Value . Not supported for text properties.

LessThanOrEqualTo

The value of Name is less than or equal to Value . Not supported for text properties.

In

The value of Name is one of the comma delimited strings in Value . Only supported for text properties.

Contains

The value of Name contains the string Value . Only supported for text properties.

A SearchExpression can include the Contains operator multiple times when the value of Name is one of the following:

Experiment.DisplayName

Experiment.ExperimentName

Experiment.Tags

Trial.DisplayName

Trial.TrialName

Trial.Tags

TrialComponent.DisplayName

TrialComponent.TrialComponentName

TrialComponent.Tags

TrialComponent.InputArtifacts

TrialComponent.OutputArtifacts

A SearchExpression can include only one Contains operator for all other values of Name . In these cases, if you include multiple Contains operators in the SearchExpression , the result is the following error message: “'CONTAINS' operator usage limit of 1 exceeded. “

Value -> (string)

A value used with Name and Operator to determine which resources satisfy the filter’s condition. For numerical properties, Value must be an integer or floating-point decimal. For timestamp properties, Value must be an ISO 8601 date-time string of the following format: YYYY-mm-dd'T'HH:MM:SS .

NestedFilters -> (list)

A list of nested filter objects.

(structure)

A list of nested Filter objects. A resource must satisfy the conditions of all filters to be included in the results returned from the Search API.

For example, to filter on a training job’s InputDataConfig property with a specific channel name and S3Uri prefix, define the following filters:

'{Name:"InputDataConfig.ChannelName", "Operator":"Equals", "Value":"train"}',

'{Name:"InputDataConfig.DataSource.S3DataSource.S3Uri", "Operator":"Contains", "Value":"mybucket/catdata"}'

NestedPropertyName -> (string)

The name of the property to use in the nested filters. The value must match a listed property name, such as InputDataConfig .

Filters -> (list)

A list of filters. Each filter acts on a property. Filters must contain at least one Filters value. For example, a NestedFilters call might include a filter on the PropertyName parameter of the InputDataConfig property: InputDataConfig.DataSource.S3DataSource.S3Uri .

(structure)

A conditional statement for a search expression that includes a resource property, a Boolean operator, and a value. Resources that match the statement are returned in the results from the Search API.

If you specify a Value , but not an Operator , Amazon SageMaker uses the equals operator.

In search, there are several property types:

Metrics

To define a metric filter, enter a value using the form "Metrics.<name>" , where <name> is a metric name. For example, the following filter searches for training jobs with an "accuracy" metric greater than "0.9" :

{

"Name": "Metrics.accuracy",

"Operator": "GreaterThan",

"Value": "0.9"

}

HyperParameters

To define a hyperparameter filter, enter a value with the form "HyperParameters.<name>" . Decimal hyperparameter values are treated as a decimal in a comparison if the specified Value is also a decimal value. If the specified Value is an integer, the decimal hyperparameter values are treated as integers. For example, the following filter is satisfied by training jobs with a "learning_rate" hyperparameter that is less than "0.5" :

{

"Name": "HyperParameters.learning_rate",

"Operator": "LessThan",

"Value": "0.5"

}

Tags

To define a tag filter, enter a value with the form Tags.<key> .

Name -> (string)

A resource property name. For example, TrainingJobName . For valid property names, see SearchRecord . You must specify a valid property for the resource.

Operator -> (string)

A Boolean binary operator that is used to evaluate the filter. The operator field contains one of the following values:

Equals

The value of Name equals Value .

NotEquals

The value of Name doesn’t equal Value .

Exists

The Name property exists.

NotExists

The Name property does not exist.

GreaterThan

The value of Name is greater than Value . Not supported for text properties.

GreaterThanOrEqualTo

The value of Name is greater than or equal to Value . Not supported for text properties.

LessThan

The value of Name is less than Value . Not supported for text properties.

LessThanOrEqualTo

The value of Name is less than or equal to Value . Not supported for text properties.

In

The value of Name is one of the comma delimited strings in Value . Only supported for text properties.

Contains

The value of Name contains the string Value . Only supported for text properties.

A SearchExpression can include the Contains operator multiple times when the value of Name is one of the following:

Experiment.DisplayName

Experiment.ExperimentName

Experiment.Tags

Trial.DisplayName

Trial.TrialName

Trial.Tags

TrialComponent.DisplayName

TrialComponent.TrialComponentName

TrialComponent.Tags

TrialComponent.InputArtifacts

TrialComponent.OutputArtifacts

A SearchExpression can include only one Contains operator for all other values of Name . In these cases, if you include multiple Contains operators in the SearchExpression , the result is the following error message: “'CONTAINS' operator usage limit of 1 exceeded. “

Value -> (string)

A value used with Name and Operator to determine which resources satisfy the filter’s condition. For numerical properties, Value must be an integer or floating-point decimal. For timestamp properties, Value must be an ISO 8601 date-time string of the following format: YYYY-mm-dd'T'HH:MM:SS .

SubExpressions -> (list)

A list of search expression objects.

(structure)

A multi-expression that searches for the specified resource or resources in a search. All resource objects that satisfy the expression’s condition are included in the search results. You must specify at least one subexpression, filter, or nested filter. A SearchExpression can contain up to twenty elements.

A SearchExpression contains the following components:

A list of Filter objects. Each filter defines a simple Boolean expression comprised of a resource property name, Boolean operator, and value.

A list of NestedFilter objects. Each nested filter defines a list of Boolean expressions using a list of resource properties. A nested filter is satisfied if a single object in the list satisfies all Boolean expressions.

A list of SearchExpression objects. A search expression object can be nested in a list of search expression objects.

A Boolean operator: And or Or .

Filters -> (list)

A list of filter objects.

(structure)

A conditional statement for a search expression that includes a resource property, a Boolean operator, and a value. Resources that match the statement are returned in the results from the Search API.

If you specify a Value , but not an Operator , Amazon SageMaker uses the equals operator.

In search, there are several property types:

Metrics

To define a metric filter, enter a value using the form "Metrics.<name>" , where <name> is a metric name. For example, the following filter searches for training jobs with an "accuracy" metric greater than "0.9" :

{

"Name": "Metrics.accuracy",

"Operator": "GreaterThan",

"Value": "0.9"

}

HyperParameters

To define a hyperparameter filter, enter a value with the form "HyperParameters.<name>" . Decimal hyperparameter values are treated as a decimal in a comparison if the specified Value is also a decimal value. If the specified Value is an integer, the decimal hyperparameter values are treated as integers. For example, the following filter is satisfied by training jobs with a "learning_rate" hyperparameter that is less than "0.5" :

{

"Name": "HyperParameters.learning_rate",

"Operator": "LessThan",

"Value": "0.5"

}

Tags

To define a tag filter, enter a value with the form Tags.<key> .

Name -> (string)

A resource property name. For example, TrainingJobName . For valid property names, see SearchRecord . You must specify a valid property for the resource.

Operator -> (string)

A Boolean binary operator that is used to evaluate the filter. The operator field contains one of the following values:

Equals

The value of Name equals Value .

NotEquals

The value of Name doesn’t equal Value .

Exists

The Name property exists.

NotExists

The Name property does not exist.

GreaterThan

The value of Name is greater than Value . Not supported for text properties.

GreaterThanOrEqualTo

The value of Name is greater than or equal to Value . Not supported for text properties.

LessThan

The value of Name is less than Value . Not supported for text properties.

LessThanOrEqualTo

The value of Name is less than or equal to Value . Not supported for text properties.

In

The value of Name is one of the comma delimited strings in Value . Only supported for text properties.

Contains

The value of Name contains the string Value . Only supported for text properties.

A SearchExpression can include the Contains operator multiple times when the value of Name is one of the following:

Experiment.DisplayName

Experiment.ExperimentName

Experiment.Tags

Trial.DisplayName

Trial.TrialName

Trial.Tags

TrialComponent.DisplayName

TrialComponent.TrialComponentName

TrialComponent.Tags

TrialComponent.InputArtifacts

TrialComponent.OutputArtifacts

A SearchExpression can include only one Contains operator for all other values of Name . In these cases, if you include multiple Contains operators in the SearchExpression , the result is the following error message: “'CONTAINS' operator usage limit of 1 exceeded. “

Value -> (string)

A value used with Name and Operator to determine which resources satisfy the filter’s condition. For numerical properties, Value must be an integer or floating-point decimal. For timestamp properties, Value must be an ISO 8601 date-time string of the following format: YYYY-mm-dd'T'HH:MM:SS .

NestedFilters -> (list)

A list of nested filter objects.

(structure)

A list of nested Filter objects. A resource must satisfy the conditions of all filters to be included in the results returned from the Search API.

For example, to filter on a training job’s InputDataConfig property with a specific channel name and S3Uri prefix, define the following filters:

'{Name:"InputDataConfig.ChannelName", "Operator":"Equals", "Value":"train"}',

'{Name:"InputDataConfig.DataSource.S3DataSource.S3Uri", "Operator":"Contains", "Value":"mybucket/catdata"}'

NestedPropertyName -> (string)

The name of the property to use in the nested filters. The value must match a listed property name, such as InputDataConfig .

Filters -> (list)

A list of filters. Each filter acts on a property. Filters must contain at least one Filters value. For example, a NestedFilters call might include a filter on the PropertyName parameter of the InputDataConfig property: InputDataConfig.DataSource.S3DataSource.S3Uri .

(structure)

A conditional statement for a search expression that includes a resource property, a Boolean operator, and a value. Resources that match the statement are returned in the results from the Search API.

If you specify a Value , but not an Operator , Amazon SageMaker uses the equals operator.

In search, there are several property types:

Metrics

To define a metric filter, enter a value using the form "Metrics.<name>" , where <name> is a metric name. For example, the following filter searches for training jobs with an "accuracy" metric greater than "0.9" :

{

"Name": "Metrics.accuracy",

"Operator": "GreaterThan",

"Value": "0.9"

}

HyperParameters

To define a hyperparameter filter, enter a value with the form "HyperParameters.<name>" . Decimal hyperparameter values are treated as a decimal in a comparison if the specified Value is also a decimal value. If the specified Value is an integer, the decimal hyperparameter values are treated as integers. For example, the following filter is satisfied by training jobs with a "learning_rate" hyperparameter that is less than "0.5" :

{

"Name": "HyperParameters.learning_rate",

"Operator": "LessThan",

"Value": "0.5"

}

Tags

To define a tag filter, enter a value with the form Tags.<key> .

Name -> (string)

A resource property name. For example, TrainingJobName . For valid property names, see SearchRecord . You must specify a valid property for the resource.

Operator -> (string)

A Boolean binary operator that is used to evaluate the filter. The operator field contains one of the following values:

Equals

The value of Name equals Value .

NotEquals

The value of Name doesn’t equal Value .

Exists

The Name property exists.

NotExists

The Name property does not exist.

GreaterThan

The value of Name is greater than Value . Not supported for text properties.

GreaterThanOrEqualTo

The value of Name is greater than or equal to Value . Not supported for text properties.

LessThan

The value of Name is less than Value . Not supported for text properties.

LessThanOrEqualTo

The value of Name is less than or equal to Value . Not supported for text properties.

In

The value of Name is one of the comma delimited strings in Value . Only supported for text properties.

Contains

The value of Name contains the string Value . Only supported for text properties.

A SearchExpression can include the Contains operator multiple times when the value of Name is one of the following:

Experiment.DisplayName

Experiment.ExperimentName

Experiment.Tags

Trial.DisplayName

Trial.TrialName

Trial.Tags

TrialComponent.DisplayName

TrialComponent.TrialComponentName

TrialComponent.Tags

TrialComponent.InputArtifacts

TrialComponent.OutputArtifacts

A SearchExpression can include only one Contains operator for all other values of Name . In these cases, if you include multiple Contains operators in the SearchExpression , the result is the following error message: “'CONTAINS' operator usage limit of 1 exceeded. “

Value -> (string)

A value used with Name and Operator to determine which resources satisfy the filter’s condition. For numerical properties, Value must be an integer or floating-point decimal. For timestamp properties, Value must be an ISO 8601 date-time string of the following format: YYYY-mm-dd'T'HH:MM:SS .

SubExpressions -> (list)

A list of search expression objects.

( … recursive … )

Operator -> (string)

A Boolean operator used to evaluate the search expression. If you want every conditional statement in all lists to be satisfied for the entire search expression to be true, specify And . If only a single conditional statement needs to be true for the entire search expression to be true, specify Or . The default value is And .

Operator -> (string)

A Boolean operator used to evaluate the search expression. If you want every conditional statement in all lists to be satisfied for the entire search expression to be true, specify And . If only a single conditional statement needs to be true for the entire search expression to be true, specify Or . The default value is And .

JSON Syntax:

{
  "Filters": [
    {
      "Name": "string",
      "Operator": "Equals"|"NotEquals"|"GreaterThan"|"GreaterThanOrEqualTo"|"LessThan"|"LessThanOrEqualTo"|"Contains"|"Exists"|"NotExists"|"In",
      "Value": "string"
    }
    ...
  ],
  "NestedFilters": [
    {
      "NestedPropertyName": "string",
      "Filters": [
        {
          "Name": "string",
          "Operator": "Equals"|"NotEquals"|"GreaterThan"|"GreaterThanOrEqualTo"|"LessThan"|"LessThanOrEqualTo"|"Contains"|"Exists"|"NotExists"|"In",
          "Value": "string"
        }
        ...
      ]
    }
    ...
  ],
  "SubExpressions": [
    {
      "Filters": [
        {
          "Name": "string",
          "Operator": "Equals"|"NotEquals"|"GreaterThan"|"GreaterThanOrEqualTo"|"LessThan"|"LessThanOrEqualTo"|"Contains"|"Exists"|"NotExists"|"In",
          "Value": "string"
        }
        ...
      ],
      "NestedFilters": [
        {
          "NestedPropertyName": "string",
          "Filters": [
            {
              "Name": "string",
              "Operator": "Equals"|"NotEquals"|"GreaterThan"|"GreaterThanOrEqualTo"|"LessThan"|"LessThanOrEqualTo"|"Contains"|"Exists"|"NotExists"|"In",
              "Value": "string"
            }
            ...
          ]
        }
        ...
      ],
      "SubExpressions": [
        { ... recursive ... }
        ...
      ],
      "Operator": "And"|"Or"
    }
    ...
  ],
  "Operator": "And"|"Or"
}

--sort-by (string)

The name of the resource property used to sort the SearchResults . The default is LastModifiedTime .

--sort-order (string)

How SearchResults are ordered. Valid values are Ascending or Descending . The default is Descending .

Possible values:

Ascending

Descending

--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.

--starting-token (string)

A token to specify where to start paginating. This is the NextToken from a previously truncated response.

For usage examples, see Pagination in the AWS Command Line Interface User Guide .

--page-size (integer)

The size of each page to get in the AWS service call. This does not affect the number of items returned in the command’s output. Setting a smaller page size results in more calls to the AWS service, retrieving fewer items in each call. This can help prevent the AWS service calls from timing out.

For usage examples, see Pagination in the AWS Command Line Interface User Guide .

--max-items (integer)

The total number of items to return in the command’s output. If the total number of items available is more than the value specified, a NextToken is provided in the command’s output. To resume pagination, provide the NextToken value in the starting-token argument of a subsequent command. Do not use the NextToken response element directly outside of the AWS CLI.

For usage examples, see Pagination in the AWS Command Line Interface User Guide .

--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.

--cli-auto-prompt (boolean) Automatically prompt for CLI input parameters.

See ‘aws help’ for descriptions of global parameters.

Output¶

Results -> (list)

A list of SearchRecord objects.

(structure)

A single resource returned as part of the Search API response.

TrainingJob -> (structure)

The properties of a training job.

TrainingJobName -> (string)

The name of the 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 labeling job.

AutoMLJobArn -> (string)

The Amazon Resource Name (ARN) of the 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.

Training job statuses are:

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 about 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.

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.

Warning

Valid values for SecondaryStatus are subject to change.

We no longer support the following secondary statuses:

LaunchingMLInstances

PreparingTrainingStack

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

TrainingInputMode -> (string)

The input mode that the algorithm supports. For the input modes that Amazon SageMaker algorithms support, see Algorithms . If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the container.

In File mode, make sure you provision ML storage volume with sufficient capacity to accommodate the data download from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container use ML storage volume to also store intermediate information, if any.

For distributed algorithms using File mode, training data is distributed uniformly, and your training duration is predictable if the input data objects size is approximately same. Amazon 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 where one host in a training cluster is overloaded, thus becoming bottleneck in training.

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 AWS 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 AWS Key Management Service (AWS 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 master 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 AWS KMS in the AWS 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 AWS 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. 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 the training or compilation job can run. If job does not complete during this time, Amazon SageMaker ends the job. If value is not specified, default value is 1 day. The maximum value is 28 days.

MaxWaitTimeInSeconds -> (integer)

The maximum length of time, in seconds, how long you are willing to wait for a managed spot training job to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the training job runs. It must be equal to or greater than MaxRuntimeInSeconds .

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 list of final metric values that are set when the training job completes. Used only if the training job was configured to use metrics.

(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 the TrainingJob was created with network isolation, the value is set to true . If network isolation is enabled, nodes can’t communicate beyond the VPC they run in.

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 algorithm in distributed training.

EnableManagedSpotTraining -> (boolean)

When true, enables managed spot training using Amazon EC2 Spot instances to run training jobs instead of on-demand instances. For more information, see Managed Spot Training .

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.

DebugHookConfig -> (structure)

Configuration information for the debug hook parameters, collection configuration, and storage paths.

LocalPath -> (string)

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

S3OutputPath -> (string)

Path to Amazon S3 storage location for tensors.

HookParameters -> (map)

Configuration information for the debug hook parameters.

key -> (string)

value -> (string)

CollectionConfigurations -> (list)

Configuration information for tensor collections.

(structure)

Configuration information for 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)

Configuration for the experiment.

ExperimentName -> (string)

The name of the experiment.

TrialName -> (string)

The name of the trial.

TrialComponentDisplayName -> (string)

Display name for the trial component.

DebugRuleConfigurations -> (list)

Information about the debug rule configuration.

(structure)

Configuration information for debugging 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 for 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 TensorBoard output.

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)

Information about the evaluation status of the rules for the 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.

Tags -> (list)

An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide .

(structure)

Describes a tag.

Key -> (string)

The tag key.

Value -> (string)

The tag value.

Experiment -> (structure)

The properties of an experiment.

ExperimentName -> (string)

The name of the experiment.

ExperimentArn -> (string)

The Amazon Resource Name (ARN) of the experiment.

DisplayName -> (string)

The name of the experiment as displayed. If DisplayName isn’t specified, ExperimentName is displayed.

Source -> (structure)

The source of the experiment.

SourceArn -> (string)

The Amazon Resource Name (ARN) of the source.

SourceType -> (string)

The source type.

Description -> (string)

The description of the experiment.

CreationTime -> (timestamp)

When the experiment was created.

CreatedBy -> (structure)

Information about the user who created or modified an experiment, trial, or trial component.

UserProfileArn -> (string)

The Amazon Resource Name (ARN) of the user’s profile.

UserProfileName -> (string)

The name of the user’s profile.

DomainId -> (string)

The domain associated with the user.

LastModifiedTime -> (timestamp)

When the experiment was last modified.

LastModifiedBy -> (structure)

Information about the user who created or modified an experiment, trial, or trial component.

UserProfileArn -> (string)

The Amazon Resource Name (ARN) of the user’s profile.

UserProfileName -> (string)

The name of the user’s profile.

DomainId -> (string)

The domain associated with the user.

Tags -> (list)

The list of tags that are associated with the experiment. You can use Search API to search on the tags.

(structure)

Describes a tag.

Key -> (string)

The tag key.

Value -> (string)

The tag value.

Trial -> (structure)

The properties of a trial.

TrialName -> (string)

The name of the trial.

TrialArn -> (string)

The Amazon Resource Name (ARN) of the trial.

DisplayName -> (string)

The name of the trial as displayed. If DisplayName isn’t specified, TrialName is displayed.

ExperimentName -> (string)

The name of the experiment the trial is part of.

Source -> (structure)

The source of the trial.

SourceArn -> (string)

The Amazon Resource Name (ARN) of the source.

SourceType -> (string)

The source job type.

CreationTime -> (timestamp)

When the trial was created.

CreatedBy -> (structure)

Information about the user who created or modified an experiment, trial, or trial component.

UserProfileArn -> (string)

The Amazon Resource Name (ARN) of the user’s profile.

UserProfileName -> (string)

The name of the user’s profile.

DomainId -> (string)

The domain associated with the user.

LastModifiedTime -> (timestamp)

Who last modified the trial.

LastModifiedBy -> (structure)

Information about the user who created or modified an experiment, trial, or trial component.

UserProfileArn -> (string)

The Amazon Resource Name (ARN) of the user’s profile.

UserProfileName -> (string)

The name of the user’s profile.

DomainId -> (string)

The domain associated with the user.

Tags -> (list)

The list of tags that are associated with the trial. You can use Search API to search on the tags.

(structure)

Describes a tag.

Key -> (string)

The tag key.

Value -> (string)

The tag value.

TrialComponentSummaries -> (list)

A list of the components associated with the trial. For each component, a summary of the component’s properties is included.

(structure)

A short summary of a trial component.

TrialComponentName -> (string)

The name of the trial component.

TrialComponentArn -> (string)

The Amazon Resource Name (ARN) of the trial component.

TrialComponentSource -> (structure)

The Amazon Resource Name (ARN) and job type of the source of a trial component.

SourceArn -> (string)

The source ARN.

SourceType -> (string)

The source job type.

CreationTime -> (timestamp)

When the component was created.

CreatedBy -> (structure)

Information about the user who created or modified an experiment, trial, or trial component.

UserProfileArn -> (string)

The Amazon Resource Name (ARN) of the user’s profile.

UserProfileName -> (string)

The name of the user’s profile.

DomainId -> (string)

The domain associated with the user.

TrialComponent -> (structure)

The properties of a trial component.

TrialComponentName -> (string)

The name of the trial component.

DisplayName -> (string)

The name of the component as displayed. If DisplayName isn’t specified, TrialComponentName is displayed.

TrialComponentArn -> (string)

The Amazon Resource Name (ARN) of the trial component.

Source -> (structure)

The Amazon Resource Name (ARN) and job type of the source of the component.

SourceArn -> (string)

The source ARN.

SourceType -> (string)

The source job type.

Status -> (structure)

The status of the trial component.

PrimaryStatus -> (string)

The status of the trial component.

Message -> (string)

If the component failed, a message describing why.

StartTime -> (timestamp)

When the component started.

EndTime -> (timestamp)

When the component ended.

CreationTime -> (timestamp)

When the component was created.

CreatedBy -> (structure)

Information about the user who created or modified an experiment, trial, or trial component.

UserProfileArn -> (string)

The Amazon Resource Name (ARN) of the user’s profile.

UserProfileName -> (string)

The name of the user’s profile.

DomainId -> (string)

The domain associated with the user.

LastModifiedTime -> (timestamp)

When the component was last modified.

LastModifiedBy -> (structure)

Information about the user who created or modified an experiment, trial, or trial component.

UserProfileArn -> (string)

The Amazon Resource Name (ARN) of the user’s profile.

UserProfileName -> (string)

The name of the user’s profile.

DomainId -> (string)

The domain associated with the user.

Parameters -> (map)

The hyperparameters of the component.

key -> (string)

value -> (structure)

The value of a hyperparameter. Only one of NumberValue or StringValue can be specified.

This object is specified in the CreateTrialComponent request.

StringValue -> (string)

The string value of a categorical hyperparameter. If you specify a value for this parameter, you can’t specify the NumberValue parameter.

NumberValue -> (double)

The numeric value of a numeric hyperparameter. If you specify a value for this parameter, you can’t specify the StringValue parameter.

InputArtifacts -> (map)

The input artifacts of the component.

key -> (string)

value -> (structure)

Represents an input or output artifact of a trial component. You specify TrialComponentArtifact as part of the InputArtifacts and OutputArtifacts parameters in the CreateTrialComponent request.

Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types. Examples of output artifacts are metrics, snapshots, logs, and images.

MediaType -> (string)

The media type of the artifact, which indicates the type of data in the artifact file. The media type consists of a type and a subtype concatenated with a slash (/) character, for example, text/csv, image/jpeg, and s3/uri. The type specifies the category of the media. The subtype specifies the kind of data.

Value -> (string)

The location of the artifact.

OutputArtifacts -> (map)

The output artifacts of the component.

key -> (string)

value -> (structure)

Represents an input or output artifact of a trial component. You specify TrialComponentArtifact as part of the InputArtifacts and OutputArtifacts parameters in the CreateTrialComponent request.

Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types. Examples of output artifacts are metrics, snapshots, logs, and images.

MediaType -> (string)

The media type of the artifact, which indicates the type of data in the artifact file. The media type consists of a type and a subtype concatenated with a slash (/) character, for example, text/csv, image/jpeg, and s3/uri. The type specifies the category of the media. The subtype specifies the kind of data.

Value -> (string)

The location of the artifact.

Metrics -> (list)

The metrics for the component.

(structure)

A summary of the metrics of a trial component.

MetricName -> (string)

The name of the metric.

SourceArn -> (string)

The Amazon Resource Name (ARN) of the source.

TimeStamp -> (timestamp)

When the metric was last updated.

Max -> (double)

The maximum value of the metric.

Min -> (double)

The minimum value of the metric.

Last -> (double)

The most recent value of the metric.

Count -> (integer)

The number of samples used to generate the metric.

Avg -> (double)

The average value of the metric.

StdDev -> (double)

The standard deviation of the metric.

SourceDetail -> (structure)

Details of the source of the component.

SourceArn -> (string)

The Amazon Resource Name (ARN) of the source.

TrainingJob -> (structure)

Information about a training job that’s the source of a trial component.

TrainingJobName -> (string)

The name of the 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 labeling job.

AutoMLJobArn -> (string)

The Amazon Resource Name (ARN) of the 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.

Training job statuses are:

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 about 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.

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.

Warning

Valid values for SecondaryStatus are subject to change.

We no longer support the following secondary statuses:

LaunchingMLInstances

PreparingTrainingStack

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

TrainingInputMode -> (string)

The input mode that the algorithm supports. For the input modes that Amazon SageMaker algorithms support, see Algorithms . If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the container.

In File mode, make sure you provision ML storage volume with sufficient capacity to accommodate the data download from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container use ML storage volume to also store intermediate information, if any.

For distributed algorithms using File mode, training data is distributed uniformly, and your training duration is predictable if the input data objects size is approximately same. Amazon 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 where one host in a training cluster is overloaded, thus becoming bottleneck in training.

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 AWS 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 AWS Key Management Service (AWS 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 master 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 AWS KMS in the AWS 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 AWS 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. 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 the training or compilation job can run. If job does not complete during this time, Amazon SageMaker ends the job. If value is not specified, default value is 1 day. The maximum value is 28 days.

MaxWaitTimeInSeconds -> (integer)

The maximum length of time, in seconds, how long you are willing to wait for a managed spot training job to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the training job runs. It must be equal to or greater than MaxRuntimeInSeconds .

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 list of final metric values that are set when the training job completes. Used only if the training job was configured to use metrics.

(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 the TrainingJob was created with network isolation, the value is set to true . If network isolation is enabled, nodes can’t communicate beyond the VPC they run in.

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 algorithm in distributed training.

EnableManagedSpotTraining -> (boolean)

When true, enables managed spot training using Amazon EC2 Spot instances to run training jobs instead of on-demand instances. For more information, see Managed Spot Training .

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.

DebugHookConfig -> (structure)

Configuration information for the debug hook parameters, collection configuration, and storage paths.

LocalPath -> (string)

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

S3OutputPath -> (string)

Path to Amazon S3 storage location for tensors.

HookParameters -> (map)

Configuration information for the debug hook parameters.

key -> (string)

value -> (string)

CollectionConfigurations -> (list)

Configuration information for tensor collections.

(structure)

Configuration information for 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)

Configuration for the experiment.

ExperimentName -> (string)

The name of the experiment.

TrialName -> (string)

The name of the trial.

TrialComponentDisplayName -> (string)

Display name for the trial component.

DebugRuleConfigurations -> (list)

Information about the debug rule configuration.

(structure)

Configuration information for debugging 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 for 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 TensorBoard output.

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)

Information about the evaluation status of the rules for the 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.

Tags -> (list)

An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide .

(structure)

Describes a tag.

Key -> (string)

The tag key.

Value -> (string)

The tag value.

ProcessingJob -> (structure)

Information about a processing job that’s the source of a trial component.

ProcessingInputs -> (list)

For each input, data is downloaded from S3 into the processing container before the processing job begins running if “S3InputMode” is set to File .

(structure)

The inputs for a processing job.

InputName -> (string)

The name of the inputs for the processing job.

S3Input -> (structure)

The S3 inputs for the processing job.

S3Uri -> (string)

The URI for the Amazon S3 storage where you want Amazon SageMaker to download the artifacts needed to run a processing job.

LocalPath -> (string)

The local path to the Amazon S3 bucket where you want Amazon SageMaker to download the inputs to run a processing job. LocalPath is an absolute path to the input data.

S3DataType -> (string)

Whether you use an S3Prefix or a ManifestFile for the data type. If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for the processing job. 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 the processing job.

S3InputMode -> (string)

Whether to use File or Pipe input mode. In File mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.

S3DataDistributionType -> (string)

Whether the data stored in Amazon S3 is FullyReplicated or ShardedByS3Key .

S3CompressionType -> (string)

Whether to use Gzip compression for Amazon S3 storage.

ProcessingOutputConfig -> (structure)

The output configuration for the processing job.

Outputs -> (list)

Output configuration information for a processing job.

(structure)

Describes the results of a processing job.

OutputName -> (string)

The name for the processing job output.

S3Output -> (structure)

Configuration for processing job outputs in Amazon S3.

S3Uri -> (string)

A URI that identifies the Amazon S3 bucket where you want Amazon SageMaker to save the results of a processing job.

LocalPath -> (string)

The local path to the Amazon S3 bucket where you want Amazon SageMaker to save the results of an processing job. LocalPath is an absolute path to the input data.

S3UploadMode -> (string)

Whether to upload the results of the processing job continuously or after the job completes.

KmsKeyId -> (string)

The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the processing job output. KmsKeyId can be an ID of a KMS key, ARN of a KMS key, alias of a KMS key, or alias of a KMS key. The KmsKeyId is applied to all outputs.

ProcessingJobName -> (string)

The name of the processing job.

ProcessingResources -> (structure)

Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance.

ClusterConfig -> (structure)

The configuration for the resources in a cluster used to run the processing job.

InstanceCount -> (integer)

The number of ML compute instances to use in the processing job. For distributed processing jobs, specify a value greater than 1. The default value is 1.

InstanceType -> (string)

The ML compute instance type for the processing job.

VolumeSizeInGB -> (integer)

The size of the ML storage volume in gigabytes that you want to provision. You must specify sufficient ML storage for your scenario.

VolumeKmsKeyId -> (string)

The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the processing job.

StoppingCondition -> (structure)

Specifies a time limit for how long the processing job is allowed to run.

MaxRuntimeInSeconds -> (integer)

Specifies the maximum runtime in seconds.

AppSpecification -> (structure)

Configuration to run a processing job in a specified container image.

ImageUri -> (string)

The container image to be run by the processing job.

ContainerEntrypoint -> (list)

The entrypoint for a container used to run a processing job.

(string)

ContainerArguments -> (list)

The arguments for a container used to run a processing job.

(string)

Environment -> (map)

Sets the environment variables in the Docker container.

key -> (string)

value -> (string)

NetworkConfig -> (structure)

Networking options for a job, such as network traffic encryption between containers, whether to allow inbound and outbound network calls to and from containers, and the VPC subnets and security groups to use for VPC-enabled jobs.

EnableInterContainerTrafficEncryption -> (boolean)

Whether to encrypt all communications between distributed processing jobs. Choose True to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.

EnableNetworkIsolation -> (boolean)

Whether to allow inbound and outbound network calls to and from the containers used for the processing job.

VpcConfig -> (structure)

Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and 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)

RoleArn -> (string)

The ARN of the role used to create the processing job.

ExperimentConfig -> (structure)

Configuration for the experiment.

ExperimentName -> (string)

The name of the experiment.

TrialName -> (string)

The name of the trial.

TrialComponentDisplayName -> (string)

Display name for the trial component.

ProcessingJobArn -> (string)

The ARN of the processing job.

ProcessingJobStatus -> (string)

The status of the processing job.

ExitMessage -> (string)

A string, up to one KB in size, that contains metadata from the processing container when the processing job exits.

FailureReason -> (string)

A string, up to one KB in size, that contains the reason a processing job failed, if it failed.

ProcessingEndTime -> (timestamp)

The time that the processing job ended.

ProcessingStartTime -> (timestamp)

The time that the processing job started.

LastModifiedTime -> (timestamp)

The time the processing job was last modified.

CreationTime -> (timestamp)

The time the processing job was created.

MonitoringScheduleArn -> (string)

The ARN of a monitoring schedule for an endpoint associated with this processing job.

AutoMLJobArn -> (string)

The Amazon Resource Name (ARN) of the AutoML job associated with this processing job.

TrainingJobArn -> (string)

The ARN of the training job associated with this processing job.

Tags -> (list)

An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide .

(structure)

Describes a tag.

Key -> (string)

The tag key.

Value -> (string)

The tag value.

Tags -> (list)

The list of tags that are associated with the component. You can use Search API to search on the tags.

(structure)

Describes a tag.

Key -> (string)

The tag key.

Value -> (string)

The tag value.

Parents -> (list)

An array of the parents of the component. A parent is a trial the component is associated with and the experiment the trial is part of. A component might not have any parents.

(structure)

The trial that a trial component is associated with and the experiment the trial is part of. A component might not be associated with a trial. A component can be associated with multiple trials.

TrialName -> (string)

The name of the trial.

ExperimentName -> (string)

The name of the experiment.

NextToken -> (string)

If the result of the previous Search request was truncated, the response includes a NextToken. To retrieve the next set of results, use the token in the next request.

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