doc-exports/docs/modelarts/api-ref/modelarts_03_0062.html

Querying the Details About a Training Job Configuration

Function

This API is used to obtain the details about a specified training job configuration.

URI

GET /v1/{project_id}/training-job-configs/{config_name}

Table 1 describes the required parameters.

Table 1 URI parameters

Parameter	Mandatory	Type	Description
project_id	Yes	String	Project ID. For details about how to obtain a project ID, see Obtaining a Project ID and Name.
config_name	Yes	String	Name of a training job configuration

Table 2 Query parameters

Parameter	Mandatory	Type	Description
config_type	No	String	Configuration type to be queried. Options: custom: Query the custom configurations. sample: Query the sample configurations. The default value is custom.

Request Body

None

Response Body

Table 3 describes the response parameters.

Table 3 Parameters

Parameter	Type	Description
is_success	Boolean	Whether the request is successful
error_message	String	Error message of a failed API call. This parameter is not included when the API call succeeds.
error_code	String	Error code of a failed API call. For details, see Error Codes. This parameter is not included when the API call succeeds.
config_name	String	Name of a training job configuration
config_desc	String	Description of a training job configuration
worker_server_num	Integer	Number of workers in a training job
app_url	String	Code directory of a training job
boot_file_url	String	Boot file of a training job
model_id	Long	Model ID of a training job
parameter	JSON Array	Running parameters of a training job. It is a collection of label-value pairs. This parameter is a container environment variable when a training job uses a custom image. For details, see Table 8.
spec_id	Long	ID of the resource specifications selected for a training job
data_url	String	Dataset of a training job
dataset_id	String	Dataset ID of a training job
dataset_version_id	String	Dataset version ID of a training job
data_source	JSON Array	Dataset of a training job For details, see Table 4.
engine_type	Integer	Engine type of a training job
engine_name	String	Name of the engine selected for a training job
engine_id	Long	ID of the engine selected for a training job
engine_version	String	Version of the engine selected for a training job
train_url	String	OBS URL of the output file of a training job. By default, this parameter is left blank. Example value: /usr/train/
log_url	String	OBS URL of the logs of a training job. By default, this parameter is left blank. Example value: /usr/train/
user_image_url	String	SWR URL of a custom image used by a training job
user_command	String	Boot command used to start the container of a custom image of a training job
spec_code	String	Resource specifications selected for a training job
gpu_type	String	GPU type of the resource specifications
create_time	Long	Time when a training job parameter configuration is created
cpu	String	CPU memory of the resource specifications
gpu_num	Integer	Number of GPUs of the resource specifications
core	String	Number of cores of the resource specifications
dataset_name	String	Dataset of a training job
dataset_version_name	String	Dataset of a training job
pool_id	String	ID of a resource pool
pool_name	String	Name of a resource pool
volumes	JSON Array	Storage volume that can be used by a training job. For details, see Table 5.
nas_mount_path	String	Local mount path of SFS Turbo (NAS). Example value: /home/work/nas
nas_share_addr	String	Shared path of SFS Turbo (NAS). Example value: 192.168.8.150:/
nas_type	String	Only NFS is supported. Example value: nfs

Table 4 data_source parameters

Parameter	Type	Description
dataset_id	String	Dataset ID of a training job
dataset_version	String	Dataset version ID of a training job
type	String	Dataset type. Options: obs: Data from OBS is used. dataset: Data from a specified dataset is used.
data_url	String	OBS bucket path

Table 5 volumes parameters

Parameter	Type	Description
nfs	Object	Storage volume of the shared file system type. Only the training jobs running in a resource pool with the shared file system network connected support such storage volumes. For details, see Table 6.
host_path	Object	Storage volume of the host file system type. Only training jobs running in a dedicated resource pool support such storage volumes. For details, see Table 7.

Table 6 nfs parameters

Parameter	Type	Description
id	String	ID of an SFS Turbo file system
src_path	String	Address of an SFS Turbo file system
dest_path	String	Local path to a training job
read_only	Boolean	Whether dest_path is read-only. The default value is false. true: read-only permission false: read/write permission. This is the default value.

Table 7 host_path parameters

Parameter	Type	Description
src_path	String	Local path to a host
dest_path	String	Local path to a training job
read_only	Boolean	Whether dest_path is read-only. The default value is false. true: read-only permission false: read/write permission. This is the default value.

Table 8 parameter parameters

Parameter	Type	Description
label	String	Parameter name
value	String	Parameter value

Sample Request

The following shows how to obtain the details about the job configuration named config123.

GET    https://endpoint/v1/{project_id}/training-job-configs/config123

Sample Response

• Successful response

  {
      "spec_code": "xxx",
      "user_image_url": "100.125.5.235:20202/jobmng/custom-cpu-base:1.0",
      "user_command": "bash -x /home/work/run_train.sh python /home/work/user-job-dir/app/mnist/mnist_softmax.py --data_url /home/work/user-job-dir/app/mnist_data",
      
      "dataset_version_id": "2ff0d6ba-c480-45ae-be41-09a8369bfc90",
      "engine_name": "xxx",
      "is_success": true,
      "nas_mount_path": "/home/work/nas",
      "worker_server_num": 1,
      "nas_share_addr": "192.168.8.150:/",
      "train_url": "/test/minst/train_out/out1/",
      "nas_type": "nfs",
      "spec_id": 4,
      "parameter": [
          {
              "label": "learning_rate",
              "value": 0.01
          }
      ],
      "log_url": "/usr/log/",
      "config_name": "config123",
      "app_url": "/usr/app/",
      "create_time": 1559045426000,
      "dataset_id": "38277e62-9e59-48f4-8d89-c8cf41622c24",
      "volumes": [
          {
              "nfs": {
                  "id": "43b37236-9afa-4855-8174-32254b9562e7",
                  "src_path": "192.168.8.150:/",
                  "dest_path": "/home/work/nas",
                  "read_only": false
              }
          },
          {
              "host_path": {
                  "src_path": "/root/work",
                  "dest_path": "/home/mind",
                  "read_only": false
              }
          }
      ],
      "cpu": "64",
      "model_id": 4,
      "boot_file_url": "/usr/app/boot.py",
      "dataset_name": "dataset-test",
      "pool_id": "pool9928813f",
      "config_desc": "This is a config desc test",
      "gpu_num": 1,
      "data_source": [
          {
              "type": "obs",
              "data_url": "/test/minst/data/"
          }
      ],
      "pool_name": "p100",
      "dataset_version_name": "dataset-version-test",
      "core": "8",
      "engine_type": 1,
      "engine_id": 3,
      "engine_version": "TF-1.8.0-python2.7",
      "data_url": "/test/minst/data/"
  }

• Failed response

  {
      "is_success": false,
      "error_message": "Error string",
      "error_code": "ModelArts.0105"
  }

Status Code

For details about the status code, see Table 1.

Parent topic: Training Job Parameter Configuration