CreateTrainingJob

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 SageMaker, see Algorithms. For information about providing your own algorithms, see Using Your Own Algorithms with Amazon SageMaker.

Type: AlgorithmSpecification object

Required: Yes

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

Type: CheckpointConfig object

Required: No

Configuration information for the Amazon SageMaker Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the DebugHookConfig parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.

Type: DebugHookConfig object

Required: No

Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.

Type: Array of DebugRuleConfiguration objects

Array Members: Minimum number of 0 items. Maximum number of 20 items.

Required: No

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.

Type: Boolean

Required: No

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.

Type: Boolean

Required: No

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

Type: Boolean

Required: No

The environment variables to set in the Docker container.

Type: String to string map

Map Entries: Maximum number of 100 items.

Key Length Constraints: Maximum length of 512.

Key Pattern: [a-zA-Z_][a-zA-Z0-9_]*

Value Length Constraints: Maximum length of 512.

Value Pattern: [\S\s]*

Required: No

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

Type: ExperimentConfig object

Required: No

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 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.

Type: String to string map

Map Entries: Minimum number of 0 items. Maximum number of 100 items.

Key Length Constraints: Maximum length of 256.

Key Pattern: .*

Value Length Constraints: Maximum length of 2500.

Value Pattern: .*

Required: No

Contains information about the infrastructure health check configuration for the training job.

Type: InfraCheckConfig object

Required: No

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, 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 are available as input streams. They do not need to be downloaded.

Your input must be in the same AWS region as your training job.

Type: Array of Channel objects

Array Members: Minimum number of 1 item. Maximum number of 20 items.

Required: No

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

Type: OutputDataConfig object

Required: Yes

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

Type: ProfilerConfig object

Required: No

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

Type: Array of ProfilerRuleConfiguration objects

Array Members: Minimum number of 0 items. Maximum number of 20 items.

Required: No

Configuration for remote debugging. To learn more about the remote debugging functionality of SageMaker, see Access a training container through AWS Systems Manager (SSM) for remote debugging.

Type: RemoteDebugConfig object

Required: No

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 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.

Type: ResourceConfig object

Required: Yes

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

Type: RetryStrategy object

Required: No

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

During model training, 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 SageMaker Roles.

Type: String

Length Constraints: Minimum length of 20. Maximum length of 2048.

Pattern: ^arn:aws[a-z\-]*:iam::\d{12}:role/?[a-zA-Z_0-9+=,.@\-_/]+$

Required: Yes

Contains information about attribute-based access control (ABAC) for the training job.

Type: SessionChainingConfig object

Required: No

Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.

To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.

Type: StoppingCondition object

Required: Yes

An array of key-value pairs. You can use tags to categorize your AWS resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging AWS Resources.

Type: Array of Tag objects

Array Members: Minimum number of 0 items. Maximum number of 50 items.

Required: No

Configuration of storage locations for the Amazon SageMaker Debugger TensorBoard output data.

Type: TensorBoardOutputConfig object

Required: No

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

Type: String

Length Constraints: Minimum length of 1. Maximum length of 63.

Pattern: ^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,62}

Required: Yes

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.

Type: VpcConfig object

Required: No

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

Type: String

Length Constraints: Maximum length of 256.

Pattern: arn:aws[a-z\-]*:sagemaker:[a-z0-9\-]*:[0-9]{12}:training-job/.*

Resource being accessed is in use.

HTTP Status Code: 400

You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

HTTP Status Code: 400

Resource being access is not found.

HTTP Status Code: 400