Querying All Trials Using Hyperparameter Search

Function

This API is used to obtain the detailed results of all trials in a specified hyperparameter search job.

This API is used when you need to view the performance metrics and status of all trials in a hyperparameter search job. Before using this API, ensure that the hyperparameter search job exists and you have the required permission. After the query, the API returns the detailed results of all trials. If the hyperparameter search job does not exist or you do not have the required permission, the API will return an error message.

Debugging

You can debug this API through automatic authentication in API Explorer or use the SDK sample code generated by API Explorer.

Authorization Information

Each account has all the permissions required to call all APIs, but IAM users must be assigned the required permissions.

If you are using role/policy-based authorization, see Permissions Policies and Supported Actions for details on the required permissions.

If you are using identity policy-based authorization, the following identity policy-based permissions are required.

Action	Access Level	Resource Type (*: required)	Condition Key	Alias	Dependencies
modelarts:trainJob:get	Read	trainJob *	g:ResourceTag/<tag-key>	-	-
modelarts:trainJob:get	Read	-	modelarts:poolType modelarts:poolId	-	-

URI

GET /v2/{project_id}/training-jobs/{training_job_id}/autosearch-trials

**Table 1** Path Parameters
Parameter	Mandatory	Type	Description
project_id	Yes	String	Definition: Project ID. For details, see Obtaining a Project ID and Name. Constraints: N/A Range: The value can contain 1 to 64 characters. Only letters, digits, and hyphens (-) are allowed. Default Value: N/A
training_job_id	Yes	String	Definition: Training job ID. For details, see Obtaining Training Jobs. Constraints: N/A Range: N/A Default Value: N/A

**Table 2** Query Parameters
Parameter	Mandatory	Type	Description
limit	No	Integer	Definition: Number of returned records. Constraints: N/A Range: N/A Default Value: N/A
offset	No	Integer	Definition: Offset of data records Constraints: N/A Range: N/A Default Value: N/A

Request Parameters

None

Response Parameters

Status code: 200

**Table 3** Response body parameters
Parameter	Type	Description
total	Integer	Definition: Number of trials searched using hyperparameters. Range: N/A
count	Integer	Definition: Number of trials searched using hyperparameters displayed on the current page. Range: N/A
limit	Integer	Definition: Maximum number of trials searched using hyperparameters displayed on the current page. Range: N/A
offset	Integer	Definition: Page number of trials searched using hyperparameters. Range: N/A
group_by	String	Definition: Category. Range: N/A
items	items object	Definition: Hyperparameter search list.

**Table 4** items
Parameter	Type	Description
header	Array of strings	Definition: Field information of all trial results searched using hyperparameters.
data	Array<Array<String>>	Definition: Data list of all trial results searched using hyperparameters.

Example Requests

The following shows how to query all trial information about the job whose training_job_id is 5b60a667-1438-4eb5-9705-85b860e623dc.

GET https://{endpoint}/v2/{project_id}/training-jobs/5b60a667-1438-4eb5-9705-85b860e623dc/autosearch-trials

Example Responses

Status code: 200

{
  "total" : 8,
  "count" : 8,
  "limit" : 50,
  "offset" : 0,
  "group_by" : "",
  "items" : {
    "header" : [ "", "done", "pid", "config", "trial_id", "training_iteration", "time_total_s", "worker_index", "reward_attr", "status", "acc", "loss", "best_reward" ],
    "data" : [ [ "0", "True", "314", "batch_size: 32\nlearning_rate: 0.05512301741232006\ntrial_index: 0\nparam/batch_size: 32\nparam/learning_rate: 0.05512301741232006", "ae544174", "2", "19.477163314819336", "", "0.0625", "TERMINATED", "0.0625", "tensor(0.0754, device='cuda:0', requires_grad=True)", "0.0625" ], [ "1", "True", "315", "batch_size: 32\nlearning_rate: 0.0785570955603036\ntrial_index: 1\nparam/batch_size: 32\nparam/learning_rate: 0.0785570955603036", "ae548666", "2", "3.601897954940796", "", "0.0", "TERMINATED", "0.0", "tensor(0.0760, device='cuda:0', requires_grad=True)", "0.0" ], [ "2", "True", "312", "batch_size: 16\nlearning_rate: 0.04015387428829642\ntrial_index: 2\nparam/batch_size: 16\nparam/learning_rate: 0.04015387428829642", "ae54c0ea", "2", "3.5978384017944336", "", "0.1875", "TERMINATED", "0.1875", "tensor(0.1469, device='cuda:0', requires_grad=True)", "0.1875" ], [ "3", "True", "313", "batch_size: 32\nlearning_rate: 0.0340820322164706\ntrial_index: 3\nparam/batch_size: 32\nparam/learning_rate: 0.0340820322164706", "ae5503c0", "2", "3.641200304031372", "", "0.25", "TERMINATED", "0.25", "tensor(0.0716, device='cuda:0', requires_grad=True)", "0.25" ], [ "4", "True", "470", "batch_size: 32\nlearning_rate: 0.03656488928171769\ntrial_index: 4\nparam/batch_size: 32\nparam/learning_rate: 0.03656488928171769", "bef46590", "2", "3.6120550632476807", "", "0.09375", "TERMINATED", "0.09375", "tensor(0.0740, device='cuda:0', requires_grad=True)", "0.09375" ], [ "5", "True", "499", "batch_size: 32\nlearning_rate: 0.008413169003970163\ntrial_index: 5\nparam/batch_size: 32\nparam/learning_rate: 0.008413169003970163", "bef578f4", "2", "3.6379287242889404", "", "0.1875", "TERMINATED", "0.1875", "tensor(0.0723, device='cuda:0', requires_grad=True)", "0.1875" ], [ "6", "True", "528", "batch_size: 64\nlearning_rate: 0.06297447200613912\ntrial_index: 6\nparam/batch_size: 64\nparam/learning_rate: 0.06297447200613912", "bef5c584", "2", "3.711118221282959", "", "0.046875", "TERMINATED", "0.046875", "tensor(0.0381, device='cuda:0', requires_grad=True)", "0.046875" ], [ "7", "True", "557", "batch_size: 32\nlearning_rate: 0.04426479392014276\ntrial_index: 7\nparam/batch_size: 32\nparam/learning_rate: 0.04426479392014276", "bef60684", "2", "3.6971280574798584", "", "0.0625", "TERMINATED", "0.0625", "tensor(0.0778, device='cuda:0', requires_grad=True)", "0.0625" ] ]
  }
}