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training_plans

Creates, updates, deletes, gets or lists a training_plans resource.

Overview​

Nametraining_plans
TypeResource
Idaws.sagemaker.training_plans

Fields​

The following fields are returned by SELECT queries:

NameDatatypeDescription
available_instance_countintegerThe number of instances currently available for use in this training plan.
available_spare_instance_countintegerThe number of available spare instances in the training plan.
currency_codestringThe currency code for the upfront fee (e.g., USD).
duration_hoursinteger (int64)The number of whole hours in the total duration for this training plan.
duration_minutesinteger (int64)The additional minutes beyond whole hours in the total duration for this training plan.
end_timestring (date-time)The end time of the training plan.
in_use_instance_countintegerThe number of instances currently in use from this training plan.
reserved_capacity_summariesarrayThe list of Reserved Capacity providing the underlying compute resources of the plan.
start_timestring (date-time)The start time of the training plan.
statusstringThe current status of the training plan (e.g., Pending, Active, Expired). To see the complete list of status values available for a training plan, refer to the Status attribute within the TrainingPlanSummary object. (Pending, Active, Scheduled, Expired, Failed)
status_messagestringA message providing additional information about the current status of the training plan.
target_resourcesarrayThe target resources (e.g., SageMaker Training Jobs, SageMaker HyperPod, SageMaker Endpoints, Studio apps) that can use this training plan. Training plans are specific to their target resource. A training plan designed for SageMaker training jobs can only be used to schedule and run training jobs. A training plan for HyperPod clusters can be used exclusively to provide compute resources to a cluster's instance group. A training plan for SageMaker endpoints can be used exclusively to provide compute resources to SageMaker endpoints for model deployment. A training plan for Studio apps can be used to launch JupyterLab and Code Editor apps on reserved training plan capacity.
total_instance_countintegerThe total number of instances reserved in this training plan.
total_ultra_server_countintegerThe total number of UltraServers reserved to this training plan.
training_plan_arnstringThe Amazon Resource Name (ARN); of the training plan. (pattern: <code>arn:aws[a-z-]:sagemaker:[a-z0-9-]:[0-9]{12}:training-plan/.*</code>)
training_plan_namestringThe name of the training plan. (pattern: <code>[a-zA-Z0-9](-*[a-zA-Z0-9]){0,63}</code>)
unhealthy_instance_countintegerThe number of instances in the training plan that are currently in an unhealthy state.
upfront_feestringThe upfront fee for the training plan.

Methods​

The following methods are available for this resource:

NameAccessible byRequired ParamsOptional ParamsDescription
describe_training_planselectregionRetrieves detailed information about a specific training plan.
list_training_plansselectregionRetrieves a list of training plans for the current account.
create_training_planinsertregion, TrainingPlanName, TrainingPlanOfferingIdCreates a new training plan in SageMaker to reserve compute capacity. Amazon SageMaker Training Plan is a capability within SageMaker that allows customers to reserve and manage GPU capacity for large-scale AI model training. It provides a way to secure predictable access to computational resources within specific timelines and budgets, without the need to manage underlying infrastructure. How it works Plans can be created for specific resources such as SageMaker Training Jobs or SageMaker HyperPod clusters, automatically provisioning resources, setting up infrastructure, executing workloads, and handling infrastructure failures. Plan creation workflow Users search for available plan offerings based on their requirements (e.g., instance type, count, start time, duration) using the SearchTrainingPlanOfferings API operation. They create a plan that best matches their needs using the ID of the plan offering they want to use. After successful upfront payment, the plan's status becomes Scheduled. The plan can be used to: Queue training jobs. Allocate to an instance group of a SageMaker HyperPod cluster. When the plan start date arrives, it becomes Active. Based on available reserved capacity: Training jobs are launched. Instance groups are provisioned. Plan composition A plan can consist of one or more Reserved Capacities, each defined by a specific instance type, quantity, Availability Zone, duration, and start and end times. For more information about Reserved Capacity, see ReservedCapacitySummary .

Parameters​

Parameters can be passed in the WHERE clause of a query. Check the Methods section to see which parameters are required or optional for each operation.

NameDatatypeDescription
regionstringAWS region (default: us-east-1)

SELECT examples​

Retrieves detailed information about a specific training plan.

SELECT
available_instance_count,
available_spare_instance_count,
currency_code,
duration_hours,
duration_minutes,
end_time,
in_use_instance_count,
reserved_capacity_summaries,
start_time,
status,
status_message,
target_resources,
total_instance_count,
total_ultra_server_count,
training_plan_arn,
training_plan_name,
unhealthy_instance_count,
upfront_fee
FROM aws.sagemaker.training_plans
WHERE region = '{{ region }}' -- required
;

INSERT examples​

Creates a new training plan in SageMaker to reserve compute capacity. Amazon SageMaker Training Plan is a capability within SageMaker that allows customers to reserve and manage GPU capacity for large-scale AI model training. It provides a way to secure predictable access to computational resources within specific timelines and budgets, without the need to manage underlying infrastructure. How it works Plans can be created for specific resources such as SageMaker Training Jobs or SageMaker HyperPod clusters, automatically provisioning resources, setting up infrastructure, executing workloads, and handling infrastructure failures. Plan creation workflow Users search for available plan offerings based on their requirements (e.g., instance type, count, start time, duration) using the SearchTrainingPlanOfferings API operation. They create a plan that best matches their needs using the ID of the plan offering they want to use. After successful upfront payment, the plan's status becomes Scheduled. The plan can be used to: Queue training jobs. Allocate to an instance group of a SageMaker HyperPod cluster. When the plan start date arrives, it becomes Active. Based on available reserved capacity: Training jobs are launched. Instance groups are provisioned. Plan composition A plan can consist of one or more Reserved Capacities, each defined by a specific instance type, quantity, Availability Zone, duration, and start and end times. For more information about Reserved Capacity, see ReservedCapacitySummary .

INSERT INTO aws.sagemaker.training_plans (
TrainingPlanName,
TrainingPlanOfferingId,
SpareInstanceCountPerUltraServer,
Tags,
region
)
SELECT 
'{{ TrainingPlanName }}' /* required */,
'{{ TrainingPlanOfferingId }}' /* required */,
{{ SpareInstanceCountPerUltraServer }},
'{{ Tags }}',
'{{ region }}'
RETURNING
training_plan_arn
;
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