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": "modelarts.vm.gpu.v100",
    "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": "TensorFlow",
    "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": "pnt1",
    "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

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