To monitor the status or retrieve details of a training plan, you can use the DescribeTrainingPlan API. The API response includes a
Status field, which reflects the current state of the training plan:
-
If the plan purchase fails, the status is set to
Failed. -
Upon successful payment, the status transitions from
PendingtoScheduled, based on the plan's start date. -
When the plan reaches its start date, the status changes to
Active. -
For plans with multiple discontinuous reserved capacities, the status reverts to
Scheduledbetween active periods, until the start date of the next reserved capacity. -
After the plan's end date, the status becomes
Expired.
Once the status is Scheduled, you can utilize the capacity reserved in the
plan for your SageMaker training jobs or HyperPod cluster workloads.
Note
-
Training jobs associated with the plan remain in
Pendingstatus until the plan becomesActive. -
For HyperPod clusters using a training plan for compute capacity, the instance group status appears as
InServiceonce created.
The following example uses an AWS CLI command to retrieve the details of a training plan by its name.
aws sagemaker describe-training-plan \
--training-plan-name "name" This JSON document is a sample response from the SageMaker training plans API. This response provides details about a training plan that has been successfully created.
{
"AvailableInstanceCount": 2,
"CurrencyCode": "USD",
"DurationHours": 48,
"DurationMinutes": 0,
"EndTime": "2024-09-28T04:30:00-07:00",
"InUseInstanceCount": 2,
"ReservedCapacitySummaries": [
{
"AvailabilityZone": "string",
"DurationHours": 48,
"DurationMinutes": 0,
"EndTime": "2024-09-28T04:30:00-07:00",
"InstanceType": "ml.p5.48xlarge",
"ReservedCapacityArn": "arn:aws:sagemaker:us-east-1:123456789123:reserved-capacity/large-models-fine-tuning-rc1",
"StartTime": "2024-09-26T04:30:00-07:00",
"Status": "Scheduled",
"TotalInstanceCount": 4
}
],
"StartTime": "2024-09-26T04:30:00-07:00",
"Status": "Scheduled",
"StatusMessage": "Payment confirmed, training plan scheduled."
"TargetResources": [ "training-job" ],
"TotalInstanceCount": 4,
"TrainingPlanArn": "arn:aws:sagemaker:us-east-1:123456789123:training-plan/large-models-fine-tuning",
"TrainingPlanName": "large-models-fine-tuning",
"UpfrontFee": "xxxx.xx"
}The following sections define the mandatory input request parameter for the
DescribeTrainingPlan API operation.
Required parameters
-
TrainingPlanName: The name of the training plan you want to describe.
