create-training-job¶

Description¶

Starts a model training job. After training completes, Amazon SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.

If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a machine learning service other than Amazon SageMaker, provided that you know how to use them for inferences.

In the request body, you provide the following:

AlgorithmSpecification - Identifies the training algorithm to use.
HyperParameters - Specify these algorithm-specific parameters to enable the estimation of model parameters during training. Hyperparameters can be tuned to optimize this learning process. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms .
InputDataConfig - Describes the training dataset and the Amazon S3, EFS, or FSx location where it is stored.
OutputDataConfig - Identifies the Amazon S3 bucket where you want Amazon SageMaker to save the results of model training.
ResourceConfig - Identifies the resources, ML compute instances, and ML storage volumes to deploy for model training. In distributed training, you specify more than one instance.
EnableManagedSpotTraining - Optimize the cost of training machine learning models by up to 80% by using Amazon EC2 Spot instances. For more information, see Managed Spot Training .
RoleARN - The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during model training. You must grant this role the necessary permissions so that Amazon SageMaker can successfully complete model training.
StoppingCondition - To help cap training costs, use MaxRuntimeInSeconds to set a time limit for training. Use MaxWaitTimeInSeconds to specify how long you are willing to wait for a managed spot training job to complete.

For more information about Amazon SageMaker, see How It Works .

Synopsis¶

  create-training-job
--training-job-name <value>
[--hyper-parameters <value>]
--algorithm-specification <value>
--role-arn <value>
[--input-data-config <value>]
--output-data-config <value>
--resource-config <value>
[--vpc-config <value>]
--stopping-condition <value>
[--tags <value>]
[--enable-network-isolation | --no-enable-network-isolation]
[--enable-inter-container-traffic-encryption | --no-enable-inter-container-traffic-encryption]
[--enable-managed-spot-training | --no-enable-managed-spot-training]
[--checkpoint-config <value>]
[--debug-hook-config <value>]
[--debug-rule-configurations <value>]
[--tensor-board-output-config <value>]
[--experiment-config <value>]
[--cli-input-json | --cli-input-yaml]
[--generate-cli-skeleton <value>]
[--cli-auto-prompt <value>]

Options¶

--training-job-name (string)

The name of the training job. The name must be unique within an AWS Region in an AWS account.

--hyper-parameters (map)

Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms .

You can specify a maximum of 100 hyperparameters. Each hyperparameter is a key-value pair. Each key and value is limited to 256 characters, as specified by the Length Constraint .

key -> (string)

value -> (string)

Shorthand Syntax:

KeyName1=string,KeyName2=string

JSON Syntax:

{"string": "string"
  ...}

--algorithm-specification (structure)

The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata, including the input mode. For more information about algorithms provided by Amazon SageMaker, see Algorithms . For information about providing your own algorithms, see Using Your Own Algorithms with Amazon SageMaker .

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

Shorthand Syntax:

TrainingImage=string,AlgorithmName=string,TrainingInputMode=string,MetricDefinitions=[{Name=string,Regex=string},{Name=string,Regex=string}],EnableSageMakerMetricsTimeSeries=boolean

JSON Syntax:

{
  "TrainingImage": "string",
  "AlgorithmName": "string",
  "TrainingInputMode": "Pipe"|"File",
  "MetricDefinitions": [
    {
      "Name": "string",
      "Regex": "string"
    }
    ...
  ],
  "EnableSageMakerMetricsTimeSeries": true|false
}

--role-arn (string)

The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

During model training, Amazon SageMaker needs your permission to read input data from an S3 bucket, download a Docker image that contains training code, write model artifacts to an S3 bucket, write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch. You grant permissions for all of these tasks to an IAM role. For more information, see Amazon SageMaker Roles .

Note

To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.

--input-data-config (list)

An array of Channel objects. Each channel is a named input source. InputDataConfig describes the input data and its location.

Algorithms can accept input data from one or more channels. For example, an algorithm might have two channels of input data, training_data and validation_data . The configuration for each channel provides the S3, EFS, or FSx location where the input data is stored. It also provides information about the stored data: the MIME type, compression method, and whether the data is wrapped in RecordIO format.

Depending on the input mode that the algorithm supports, Amazon SageMaker either copies input data files from an S3 bucket to a local directory in the Docker container, or makes it available as input streams. For example, if you specify an EFS location, input data files will be made available as input streams. They do not need to be downloaded.

(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.

JSON Syntax:

[
  {
    "ChannelName": "string",
    "DataSource": {
      "S3DataSource": {
        "S3DataType": "ManifestFile"|"S3Prefix"|"AugmentedManifestFile",
        "S3Uri": "string",
        "S3DataDistributionType": "FullyReplicated"|"ShardedByS3Key",
        "AttributeNames": ["string", ...]
      },
      "FileSystemDataSource": {
        "FileSystemId": "string",
        "FileSystemAccessMode": "rw"|"ro",
        "FileSystemType": "EFS"|"FSxLustre",
        "DirectoryPath": "string"
      }
    },
    "ContentType": "string",
    "CompressionType": "None"|"Gzip",
    "RecordWrapperType": "None"|"RecordIO",
    "InputMode": "Pipe"|"File",
    "ShuffleConfig": {
      "Seed": long
    }
  }
  ...
]

--output-data-config (structure)

Specifies the path to the S3 location where you want to store model artifacts. Amazon SageMaker creates subfolders for the 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 .

Shorthand Syntax:

KmsKeyId=string,S3OutputPath=string

JSON Syntax:

{
  "KmsKeyId": "string",
  "S3OutputPath": "string"
}

--resource-config (structure)

The resources, including the ML compute instances and ML storage volumes, to use for model training.

ML storage volumes store model artifacts and incremental states. Training algorithms might also use ML storage volumes for scratch space. If you want Amazon SageMaker to use the ML storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.

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"

Shorthand Syntax:

InstanceType=string,InstanceCount=integer,VolumeSizeInGB=integer,VolumeKmsKeyId=string

JSON Syntax:

{
  "InstanceType": "ml.m4.xlarge"|"ml.m4.2xlarge"|"ml.m4.4xlarge"|"ml.m4.10xlarge"|"ml.m4.16xlarge"|"ml.g4dn.xlarge"|"ml.g4dn.2xlarge"|"ml.g4dn.4xlarge"|"ml.g4dn.8xlarge"|"ml.g4dn.12xlarge"|"ml.g4dn.16xlarge"|"ml.m5.large"|"ml.m5.xlarge"|"ml.m5.2xlarge"|"ml.m5.4xlarge"|"ml.m5.12xlarge"|"ml.m5.24xlarge"|"ml.c4.xlarge"|"ml.c4.2xlarge"|"ml.c4.4xlarge"|"ml.c4.8xlarge"|"ml.p2.xlarge"|"ml.p2.8xlarge"|"ml.p2.16xlarge"|"ml.p3.2xlarge"|"ml.p3.8xlarge"|"ml.p3.16xlarge"|"ml.p3dn.24xlarge"|"ml.c5.xlarge"|"ml.c5.2xlarge"|"ml.c5.4xlarge"|"ml.c5.9xlarge"|"ml.c5.18xlarge"|"ml.c5n.xlarge"|"ml.c5n.2xlarge"|"ml.c5n.4xlarge"|"ml.c5n.9xlarge"|"ml.c5n.18xlarge",
  "InstanceCount": integer,
  "VolumeSizeInGB": integer,
  "VolumeKmsKeyId": "string"
}

--vpc-config (structure)

A VpcConfig object that specifies the VPC that you want your training job to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud .

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)

Shorthand Syntax:

SecurityGroupIds=string,string,Subnets=string,string

JSON Syntax:

{
  "SecurityGroupIds": ["string", ...],
  "Subnets": ["string", ...]
}

--stopping-condition (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 .

Shorthand Syntax:

MaxRuntimeInSeconds=integer,MaxWaitTimeInSeconds=integer

JSON Syntax:

{
  "MaxRuntimeInSeconds": integer,
  "MaxWaitTimeInSeconds": integer
}

--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.

Shorthand Syntax:

Key=string,Value=string ...

JSON Syntax:

[
  {
    "Key": "string",
    "Value": "string"
  }
  ...
]

--enable-network-isolation | --no-enable-network-isolation (boolean)

Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If you enable network isolation for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

--enable-inter-container-traffic-encryption | --no-enable-inter-container-traffic-encryption (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. For more information, see Protect Communications Between ML Compute Instances in a Distributed Training Job .

--enable-managed-spot-training | --no-enable-managed-spot-training (boolean)

To train models using managed spot training, choose True . Managed spot training provides a fully managed and scalable infrastructure for training machine learning models. this option is useful when training jobs can be interrupted and when there is flexibility when the training job is run.

The complete and intermediate results of jobs are stored in an Amazon S3 bucket, and can be used as a starting point to train models incrementally. Amazon SageMaker provides metrics and logs in CloudWatch. They can be used to see when managed spot training jobs are running, interrupted, resumed, or completed.

--checkpoint-config (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/ .

Shorthand Syntax:

S3Uri=string,LocalPath=string

JSON Syntax:

{
  "S3Uri": "string",
  "LocalPath": "string"
}

--debug-hook-config (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)

Shorthand Syntax:

LocalPath=string,S3OutputPath=string,HookParameters={KeyName1=string,KeyName2=string},CollectionConfigurations=[{CollectionName=string,CollectionParameters={KeyName1=string,KeyName2=string}},{CollectionName=string,CollectionParameters={KeyName1=string,KeyName2=string}}]

JSON Syntax:

{
  "LocalPath": "string",
  "S3OutputPath": "string",
  "HookParameters": {"string": "string"
    ...},
  "CollectionConfigurations": [
    {
      "CollectionName": "string",
      "CollectionParameters": {"string": "string"
        ...}
    }
    ...
  ]
}

--debug-rule-configurations (list)

Configuration information for debugging rules.

(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)

Shorthand Syntax:

RuleConfigurationName=string,LocalPath=string,S3OutputPath=string,RuleEvaluatorImage=string,InstanceType=string,VolumeSizeInGB=integer,RuleParameters={KeyName1=string,KeyName2=string} ...

JSON Syntax:

[
  {
    "RuleConfigurationName": "string",
    "LocalPath": "string",
    "S3OutputPath": "string",
    "RuleEvaluatorImage": "string",
    "InstanceType": "ml.t3.medium"|"ml.t3.large"|"ml.t3.xlarge"|"ml.t3.2xlarge"|"ml.m4.xlarge"|"ml.m4.2xlarge"|"ml.m4.4xlarge"|"ml.m4.10xlarge"|"ml.m4.16xlarge"|"ml.c4.xlarge"|"ml.c4.2xlarge"|"ml.c4.4xlarge"|"ml.c4.8xlarge"|"ml.p2.xlarge"|"ml.p2.8xlarge"|"ml.p2.16xlarge"|"ml.p3.2xlarge"|"ml.p3.8xlarge"|"ml.p3.16xlarge"|"ml.c5.xlarge"|"ml.c5.2xlarge"|"ml.c5.4xlarge"|"ml.c5.9xlarge"|"ml.c5.18xlarge"|"ml.m5.large"|"ml.m5.xlarge"|"ml.m5.2xlarge"|"ml.m5.4xlarge"|"ml.m5.12xlarge"|"ml.m5.24xlarge"|"ml.r5.large"|"ml.r5.xlarge"|"ml.r5.2xlarge"|"ml.r5.4xlarge"|"ml.r5.8xlarge"|"ml.r5.12xlarge"|"ml.r5.16xlarge"|"ml.r5.24xlarge",
    "VolumeSizeInGB": integer,
    "RuleParameters": {"string": "string"
      ...}
  }
  ...
]

--tensor-board-output-config (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.

Shorthand Syntax:

LocalPath=string,S3OutputPath=string

JSON Syntax:

{
  "LocalPath": "string",
  "S3OutputPath": "string"
}

--experiment-config (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.

Shorthand Syntax:

ExperimentName=string,TrialName=string,TrialComponentDisplayName=string

JSON Syntax:

{
  "ExperimentName": "string",
  "TrialName": "string",
  "TrialComponentDisplayName": "string"
}

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

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

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

See ‘aws help’ for descriptions of global parameters.

Output¶

TrainingJobArn -> (string)

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

Table of Contents

Feedback

User Guide

create-training-job¶

Description¶

Synopsis¶

Options¶

Output¶