Help Center/ ModelArts/ API Reference/ Historical APIs/ Training Management (Old Version)/ Training Jobs/ Querying the Details About a Training Job Version

Updated on 2024-06-13 GMT+08:00

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Querying the Details About a Training Job Version

Function

This API is used to obtain the details about a specified training job based on the job ID.

URI

GET /v1/{project_id}/training-jobs/{job_id}/versions/{version_id}

Table 1 describes the required parameters.

**Table 1** 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.
job_id	Yes	Long	ID of a training job
version_id	Yes	Long	Version ID of a training job

Request Body

None

Response Body

Table 2 describes the response parameters.

**Table 2** Parameters
Parameter	Type	Description
is_success	Boolean	Whether the request is successful
job_id	Long	ID of a training job
job_name	String	Name of a training job
job_desc	String	Description of a training job
version_id	Long	Version ID of a training job
version_name	String	Version name of a training job
pre_version_id	Long	Name of the previous version of a training job
engine_type	Integer	Engine type of a training job. The mapping between engine_type and engine_name is as follows: engine_type: 1, engine_name: TensorFlow engine_type: 2, engine_name: MXNet engine_type: 3, engine_name: Ray engine_type: 4, engine_name: Caffe engine_type: 5, engine_name: Spark_MLlib engine_type: 9, engine_name: XGBoost-Sklearn engine_type: 10, engine_name: PyTorch engine_type: 12, engine_name: Horovod
engine_name	String	Name of the engine selected for a training job. Currently, the following engines are supported: Ascend-Powered-Engine Caffe Horovod MXNet PyTorch Ray Spark_MLlib TensorFlow XGBoost-Sklearn MindSpore-GPU
engine_id	Long	ID of the engine selected for a training job
engine_version	String	Version of the engine selected for a training job
status	Integer	Status of a training job. For details about the job statuses, see Job Statuses.
app_url	String	Code directory of a training job
boot_file_url	String	Boot file of a training job
create_time	Long	Time when a training job is created
parameter	Array<Object>	Running parameters of a training job. This parameter is a container environment variable when a training job uses a custom image. For details, see Table 3.
duration	Long	Training job running duration, in milliseconds
spec_id	Long	ID of the resource specifications selected for a training job
core	String	Number of cores of the resource specifications
cpu	String	CPU memory of the resource specifications
gpu_num	Integer	Number of GPUs of the resource specifications
gpu_type	String	GPU type of the resource specifications
worker_server_num	Integer	Number of workers in a training job
data_url	String	Dataset of a training job
train_url	String	OBS path of the training job output file
log_url	String	OBS URL of the logs of a training job. By default, this parameter is left blank. Example value: /usr/train/
dataset_version_id	String	Dataset version ID of a training job
dataset_id	String	Dataset ID of a training job
data_source	Array<Object>	Dataset of a training job. For details, see Table 4.
model_id	Long	Model ID of a training job
model_metric_list	String	Model metrics of a training job. For details, see Table 5.
system_metric_list	Object	System monitoring metrics of a training job. For details, see Table 6.
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
resource_id	String	Charged resource ID of a training job
dataset_name	String	Dataset of a training job
spec_code	String	Resource specifications selected for a training job
start_time	Long	Training start time
volumes	Array<Object>	Storage volume that can be used by a training job. For details, see Table 11.
dataset_version_name	String	Dataset of a training job
pool_name	String	Name of a resource pool
pool_id	String	ID of a resource pool
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 3** **parameter** parameters
Parameter	Type	Description
label	String	Parameter name
value	String	Parameter value

**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 obs: Data from OBS is used. dataset: Data from a specified dataset is used.
data_url	String	OBS bucket path

**Table 5** **model_metric_list** parameters
Parameter	Type	Description
metric	JSON Array	Validation metrics of a classification of a training job. For details, see Table 7.
total_metric	JSON	Overall validation parameters of a training job. For details, see Table 9.

**Table 6** **system_metric_list** parameters
Parameter	Type	Description
cpuUsage	Array	CPU usage of a training job
memUsage	Array	Memory usage of a training job
gpuUtil	Array	GPU usage of a training job

**Table 7** **metric** parameters
Parameter	Type	Description
metric_values	JSON	Validation metrics of a classification of a training job. For details, see Table 8.
reserved_data	JSON	Reserved parameter
metric_meta	JSON	Classification of a training job, including the classification ID and name

**Table 8** **metric_values** parameters
Parameter	Type	Description
recall	Float	Recall of a classification of a training job
precision	Float	Precision of a classification of a training job
accuracy	Float	Accuracy of a classification of a training job

**Table 9** **total_metric** parameters
Parameter	Type	Description
total_metric_meta	JSON	Reserved parameter
total_reserved_data	JSON	Reserved parameter
total_metric_values	JSON	Overall validation metrics of a training job. For details, see Table 10.

**Table 10** **total_metric_values** parameters
Parameter	Type	Description
f1_score	Float	F1 score of a training job
recall	Float	Total recall of a training job
precision	Float	Total precision of a training job
accuracy	Float	Total accuracy of a training job

**Table 11** **volumes** parameters
Parameter	Mandatory	Type	Description
nfs	No	Object	Storage volume of the shared file system type. Only the training jobs running in the resource pool with a shared file system network connected support such storage volumes. For details, see Table 6.
host_path	No	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 12** **nfs** parameters
Parameter	Mandatory	Type	Description
id	Yes	String	ID of an SFS Turbo file system
src_path	Yes	String	Path to an SFS Turbo file system
dest_path	Yes	String	Local path to a training job
read_only	No	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 13** **host_path** parameters
Parameter	Mandatory	Type	Description
src_path	Yes	String	Local path to a host
dest_path	Yes	String	Local path to a training job
read_only	No	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.

Sample Request

The following shows how to obtain the details about the job whose job_id is 10 and version_id is 10.

GET    https://endpoint/v1/{project_id}/training-jobs/10/versions/10

Sample Response

Successful response

{
    "is_success": true,
    "job_id": 10,
    "job_name": "TestModelArtsJob",
    "job_desc": "TestModelArtsJob desc",
    "version_id": 10,
    "version_name": "jobVersion",
    "pre_version_id": 5,
    "engine_type": 1,
    "engine_name": "TensorFlow",
    "engine_id": 1,
    "engine_version": "TF-1.4.0-python2.7",
    "status": 10,
    "app_url": "/usr/app/",
    "boot_file_url": "/usr/app/boot.py",
    "create_time": 1524189990635,
    "parameter": [
        {
            "label": "learning_rate",
            "value": 0.01
        }
    ],
    "duration": 532003,
    "spec_id": 1,
    "core": 2,
    "cpu": 8,
    "gpu_num": 2,
    "gpu_type": "Pnt1",
    "worker_server_num": 1,
    "data_url": "/usr/data/",
    "train_url": "/usr/train/",
    "log_url": "/usr/log/",
    "dataset_version_id": "2ff0d6ba-c480-45ae-be41-09a8369bfc90",
    "dataset_id": "38277e62-9e59-48f4-8d89-c8cf41622c24",
    "data_source": [
        {
            "type": "obs",
            "data_url": "/qianjiajun-test/minst/data/"
        }
    ],
    "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",
    "model_id": 1,
    "model_metric_list": "{\"metric\":[{\"metric_values\":{\"recall\":0.005833,\"precision\":0.000178,\"accuracy\":0.000937},\"reserved_data\":{},\"metric_meta\":{\"class_name\":0,\"class_id\":0}}],\"total_metric\":{\"total_metric_meta\":{},\"total_reserved_data\":{},\"total_metric_values\":{\"recall\":0.005833,\"id\":0,\"precision\":0.000178,\"accuracy\":0.000937}}}",
    "system_metric_list": {
        "cpuUsage": [
            "0",
            "3.10",
            "5.76",
            "0",
            "0",
            "0",
            "0"
        ],
        "memUsage": [
            "0",
            "0.77",
            "2.09",
            "0",
            "0",
            "0",
            "0"
        ],
        "gpuUtil": [
            "0",
            "0.25",
            "0.88",
            "0",
            "0",
            "0",
            "0"
        ]
},
    "dataset_name": "dataset-test",
    "dataset_version_name": "dataset-version-test",
    "spec_code": "xxxxxxxx",
    "start_time": 1563172362000,
    "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
            }
        }
    ],
    "pool_id": "pool9928813f",
    "pool_name": "pnt1",
    "nas_mount_path": "/home/work/nas",
    "nas_share_addr": "192.168.8.150:/",
    "nas_type": "nfs"
}

Failed response

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

Status Code

For details about the status code, see Status Code.

Parent topic: Training Jobs

Previous topic: Querying a Training Job List

Next topic: Deleting a Version of a Training Job

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