CreateTrainingJobRequest
Types
Properties
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.
Contains information about the output location for managed spot training checkpoint data.
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.
Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.
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.
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.
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.
The environment variables to set in the Docker container.
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
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.
Contains information about the infrastructure health check configuration for the training job.
An array of Channel objects. Each channel is a named input source. InputDataConfig describes the input data and its location.
Specifies the path to the S3 location where you want to store model artifacts. SageMaker creates subfolders for the artifacts.
Configuration information for Amazon SageMaker Debugger system monitoring, framework profiling, and storage paths.
Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.
Configuration for remote debugging. To learn more about the remote debugging functionality of SageMaker, see Access a training container through Amazon Web Services Systems Manager (SSM) for remote debugging.
The resources, including the ML compute instances and ML storage volumes, to use for model training.
The number of times to retry the job when the job fails due to an InternalServerError.
Contains information about attribute-based access control (ABAC) for the training job.
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.
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
Configuration of storage locations for the Amazon SageMaker Debugger TensorBoard output data.
The name of the training job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.
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.