View training plan details
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
Pending
toScheduled
, 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
Scheduled
between 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
Pending
status until the plan becomesActive
. -
For HyperPod clusters using a training plan for compute capacity, the instance group status appears as
InService
once 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.