Help Center/ ModelArts/ API Reference/ Training Management/ Querying the Details About a Training Job

Updated on 2025-05-30 GMT+08:00

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Querying the Details About a Training Job

Function

This API is used to query the details about a training job.

URI

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

**Table 1** Path Parameters
Parameter	Mandatory	Type	Description
project_id	Yes	String	Project ID. For details, see Obtaining a Project ID and Name.
training_job_id	Yes	String	ID of a training job.

Request Parameters

None

Response Parameters

Status code: 200

**Table 2** Response body parameters
Parameter	Type	Description
kind	String	Training job type, which is job by default. Options: job: training job
metadata	JobMetadata object	Metadata of a training job.
status	Status object	Status of a training job. You do not need to set this parameter when creating a job.
algorithm	JobAlgorithmResponse object	Algorithm used by a training job. Options: id: Only the algorithm ID is used. subscription_id+item_version_id: The subscription ID and version ID of the algorithm are used. code_dir+boot_file: The code directory and boot file of the training job are used.
tasks	Array of TaskResponse objects	List of tasks in heterogeneous training jobs.
spec	spec object	Specifications of a training job.
endpoints	JobEndpointsResp object	Configuration required for remotely accessing a training job.

**Table 3** JobMetadata
Parameter	Type	Description
id	String	Training job ID, which is generated and returned by ModelArts after the training job is created.
name	String	Name of a training job. The value must contain 1 to 64 characters consisting of only digits, letters, underscores (_), and hyphens (-).
workspace_id	String	Workspace where a job is located. The default value is 0.
description	String	Training job description. The value must contain 0 to 256 characters. The default value is NULL.
create_time	Long	Time when a training job was created, in milliseconds. The value is generated and returned by ModelArts after a training job is created.
user_name	String	Username for creating a training job. The username is generated and returned by ModelArts after a training job is created.
annotations	Map<String,String>	Advanced configuration of a training job. Options: job_template: Template RL (heterogeneous job) fault-tolerance/job-retry-num: 3 (number of retries upon a fault)

**Table 4** Status
Parameter	Type	Description
phase	String	Level-1 status of a training job. The options are as follows: Creating Pending Running Failed Completed, Terminating Terminated Abnormal
secondary_phase	String	The level-2 status of a training job is an internal detailed status, which may be added, modified, or deleted. Dependency is not recommended. The options are as follows: Creating Queuing Running Failed Completed, Terminating Terminated CreateFailed TerminatedFailed Unknown Lost
duration	Long	Running duration of a training job, in milliseconds
node_count_metrics	Array<Array<Integer>>	Node count changes during the training job running period.
tasks	Array of strings	Tasks of a training job.
start_time	Long	Start time of a training job. The value is in timestamp format.
task_statuses	Array of task_statuses objects	Status of a training job task.
running_records	Array of running_records objects	Running and fault recovery records of a training job

**Table 5** task_statuses
Parameter	Type	Description
task	String	Name of a training job task.
exit_code	Integer	Exit code of a training job task.
message	String	Error message of a training job task.

**Table 6** running_records
Parameter	Type	Description
start_at	Integer	Unix timestamp of the start time in the current running record, in seconds
end_at	Integer	Unix timestamp of the end time in the current running record, in seconds
start_type	String	Startup mode of the current running record. The options are as follows: init_or_rescheduled: This startup is the first running after scheduling, including the first startup and the running after scheduling recovery. restarted: This startup is not the first running after scheduling but the running after a process restart.
end_reason	String	Reason why the current running record ends
end_related_task	String	ID of the task worker that causes the end of the current running record, for example, worker-0
end_recover	String	Fault tolerance policy used after the current running record ends. The options are as follows: npu_proc_restart: NPU in-place hot recovery gpu_proc_restart: GPU in-place hot recovery proc_restart: Process in-place recovery pod_reschedule: Pod-level rescheduling job_reschedule: Job-level rescheduling job_reschedule_with_taint: Isolated job-level rescheduling
end_recover_before_downgrade	String	Tolerance policy used after the current running record ends and before the fault tolerance policy is degraded. The options are the same as those of end_recover.

**Table 7** JobAlgorithmResponse
Parameter	Type	Description
id	String	Algorithm used by a training job. Options: id: Only the algorithm ID is used. subscription_id+item_version_id: The subscription ID and version ID of the algorithm are used. code_dir+boot_file: The code directory and boot file of the training job are used.
name	String	Algorithm name.
subscription_id	String	Subscription ID of a subscribed algorithm, which must be used with item_version_id
item_version_id	String	Version ID of the subscribed algorithm, which must be used with subscription_id
code_dir	String	Code directory of a training job, for example, /usr/app/. This parameter must be used together with boot_file. If id or subscription_id+item_version_id is set, leave it blank.
boot_file	String	Boot file of a training job, which must be stored in the code directory, for example, /usr/app/boot.py. This parameter must be used with code_dir. Leave this parameter blank if id, or subscription_id and item_version_id are specified.
autosearch_config_path	String	YAML configuration path of auto search jobs. An OBS URL is required.
autosearch_framework_path	String	Framework code directory of auto search jobs. An OBS URL is required.
command	String	Boot command used to start the container of a custom image of a training job. For example, python train.py.
parameters	Array of Parameter objects	Running parameters of a training job.
policies	policies object	Policies supported by jobs.
inputs	Array of Input objects	Input of a training job.
outputs	Array of Output objects	Output of a training job.
engine	engine object	Engine of a training job. Leave this parameter blank if the job is created using id of the algorithm in algorithm management, or subscription_id+item_version_id of the subscribed algorithm.
local_code_dir	String	Local directory to the training container to which the algorithm code directory is downloaded. Ensure that the following rules are complied with: The directory must be in the /home directory. In v1 compatibility mode, the current field does not take effect. When code_dir is prefixed with file://, the current field does not take effect.
working_dir	String	Work directory where an algorithm is executed. Note that this parameter does not take effect in v1 compatibility mode.
environments	Array of Map<String,String> objects	Environment variables of a training job. The format is key: value. Leave this parameter blank.

**Table 8** Parameter
Parameter	Type	Description
name	String	Parameter name.
value	String	Parameter value.
description	String	Parameter description.
constraint	constraint object	Parameter constraint.
i18n_description	i18n_description object	Internationalization description.

**Table 9** constraint
Parameter	Type	Description
type	String	Parameter type.
editable	Boolean	Whether the parameter is editable.
required	Boolean	Whether the parameter is mandatory.
sensitive	Boolean	Whether the parameter is sensitive This function is not implemented currently.
valid_type	String	Valid type.
valid_range	Array of strings	Valid range.

**Table 10** i18n_description
Parameter	Type	Description
language	String	Language. Options: zh-cn: Chinese en-us: English
description	String	Description.

**Table 11** policies
Parameter	Type	Description
auto_search	auto_search object	Hyperparameter search configuration.

**Table 12** auto_search
Parameter	Type	Description
skip_search_params	String	Hyperparameter parameters that need to be skipped.
reward_attrs	Array of reward_attrs objects	List of search metrics.
search_params	Array of search_params objects	Search parameters.
algo_configs	Array of algo_configs objects	Search algorithm configurations.

**Table 13** reward_attrs
Parameter	Type	Description
name	String	Metric name.
mode	String	Search direction. max: A larger metric value indicates better performance. min: A smaller metric value indicates better performance.
regex	String	Regular expression of a metric.

**Table 14** search_params
Parameter	Type	Description
name	String	Hyperparameter name.
param_type	String	Parameter type If continuous is specified, the hyperparameter is of the continuous type. When an algorithm is used in a training job, continuous hyperparameters are displayed as text boxes on the console. - discrete: The hyperparameter is of the discrete type. When an algorithm is used for training jobs, discrete hyperparameters are displayed as a drop-down list box on the console.
lower_bound	String	Lower bound of the hyperparameter.
upper_bound	String	Upper bound of the hyperparameter.
discrete_points_num	String	Number of discrete points of a continuous hyperparameter.
discrete_values	Array of strings	List of discrete hyperparameter values.

**Table 15** algo_configs
Parameter	Type	Description
name	String	Name of the search algorithm.
params	Array of AutoSearchAlgoConfigParameter objects	Search algorithm parameters.

**Table 16** AutoSearchAlgoConfigParameter
Parameter	Type	Description
key	String	Parameter key.
value	String	Parameter value.
type	String	Parameter type.

**Table 17** Input
Parameter	Type	Description
name	String	Name of the data input channel.
description	String	Description of the data input channel.
local_dir	String	Local directory of the container to which the data input channel is mapped.
remote	InputDataInfo object	Data input. Options: dataset: Dataset as the data input obs: OBS path as the data input
remote_constraint	Array of remote_constraint objects	Data input constraint

**Table 18** InputDataInfo
Parameter	Type	Description
dataset	dataset object	Dataset as the data input.
obs	obs object	OBS in which data input and output stored.

**Table 19** dataset
Parameter	Type	Description
id	String	Dataset ID of a training job.
version_id	String	Dataset version ID of a training job.
obs_url	String	OBS URL of the dataset required by a training job. ModelArts automatically parses and generates the URL based on the dataset and dataset version IDs. For example, /usr/data/.

**Table 20** obs
Parameter	Type	Description
obs_url	String	OBS URL of the dataset required by a training job. For example, /usr/data/.

**Table 21** remote_constraint
Parameter	Type	Description
data_type	String	Data input type, including the data storage location and dataset.
attributes	String	Attributes if a dataset is used as the data input. Options: data_format: Data format data_segmentation: Data segmentation dataset_type: Labeling type

**Table 22** Output
Parameter	Type	Description
name	String	Name of the data output channel.
description	String	Description of the data output channel.
local_dir	String	Local directory of the container to which the data output channel is mapped.
remote	remote object	Description of the actual data output.

**Table 23** remote
Parameter	Type	Description
obs	obs object	OBS to which data is actually exported.

**Table 24** obs
Parameter	Type	Description
obs_url	String	OBS URL to which data is actually exported.

**Table 25** engine
Parameter	Type	Description
engine_id	String	Engine ID selected for a training job. You can set this parameter to engine_id, engine_name + engine_version, or image_url.
engine_name	String	Name of the engine selected for a training job. If engine_id is set, leave this parameter blank.
engine_version	String	Name of the engine version selected for a training job. If engine_id is set, leave this parameter blank.
image_url	String	Custom image URL selected for a training job.

**Table 26** TaskResponse
Parameter	Type	Description
role	String	Task role. This function is not supported currently.
algorithm	algorithm object	Algorithm management and configuration.
task_resource	FlavorResponse object	Flavors of a training job or an algorithm.

**Table 27** algorithm
Parameter	Type	Description
code_dir	String	Absolute path of the directory where the algorithm boot file is stored.
boot_file	String	Absolute path of the algorithm boot file.
inputs	inputs object	Algorithm input channel.
outputs	outputs object	Algorithm output channel.
engine	engine object	Engine on which a heterogeneous job depends.
local_code_dir	String	Local directory to the training container to which the algorithm code directory is downloaded. Ensure that the following rules are complied with: The directory must be in the /home directory. In v1 compatibility mode, the current field does not take effect. When code_dir is prefixed with file://, the current field does not take effect.
working_dir	String	Work directory where an algorithm is executed. Note that this parameter does not take effect in v1 compatibility mode.

**Table 28** inputs
Parameter	Type	Description
name	String	Name of the data input channel.
local_dir	String	Local path of the container to which the data input and output channels are mapped.
remote	remote object	Actual data input. Heterogeneous jobs support only OBS.

**Table 29** remote
Parameter	Type	Description
obs	obs object	OBS in which data input and output stored.

**Table 30** obs
Parameter	Type	Description
obs_url	String	OBS URL of the dataset required by a training job. For example, /usr/data/.

**Table 31** outputs
Parameter	Type	Description
name	String	Name of the data output channel.
local_dir	String	Local directory of the container to which the data output channel is mapped.
remote	remote object	Description of the actual data output.
mode	String	Data transmission mode. The default value is upload_periodically.
period	String	Data transmission period. The default value is 30s.

**Table 32** remote
Parameter	Type	Description
obs	obs object	OBS to which data is actually exported.

**Table 33** obs
Parameter	Type	Description
obs_url	String	OBS URL to which data is actually exported.

**Table 34** engine
Parameter	Type	Description
engine_id	String	Engine ID of a heterogeneous job, for example, caffe-1.0.0-python2.7.
engine_name	String	Engine name of a heterogeneous job, for example, Caffe.
engine_version	String	Engine version of a heterogeneous job.
v1_compatible	Boolean	Whether the v1 compatibility mode is used.
run_user	String	User UID started by default by the engine.
image_url	String	Custom image URL selected by an algorithm.

**Table 35** FlavorResponse
Parameter	Type	Description
flavor_id	String	ID of the resource flavor.
flavor_name	String	Name of the resource flavor.
max_num	Integer	Maximum number of nodes in a resource flavor.
flavor_type	String	Resource flavor type. Options: CPU GPU Ascend
billing	billing object	Billing information of a resource flavor.
flavor_info	flavor_info object	Resource flavor details.
attributes	Map<String,String>	Other specification attributes.

**Table 36** billing
Parameter	Type	Description
code	String	Billing code.
unit_num	Integer	Number of billing units.

**Table 37** flavor_info
Parameter	Type	Description
max_num	Integer	Maximum number of nodes that can be selected. The value 1 indicates that the distributed mode is not supported.
cpu	cpu object	CPU specifications.
gpu	gpu object	GPU specifications.
npu	npu object	Ascend specifications
memory	memory object	Memory information.
disk	disk object	Disk information.

**Table 38** cpu
Parameter	Type	Description
arch	String	CPU architecture.
core_num	Integer	Number of cores.

**Table 39** gpu
Parameter	Type	Description
unit_num	Integer	Number of GPUs.
product_name	String	Product name.
memory	String	Memory.

**Table 40** npu
Parameter	Type	Description
unit_num	String	Number of NPUs.
product_name	String	Product name.
memory	String	Memory.

**Table 41** memory
Parameter	Type	Description
size	Integer	Memory size.
unit	String	Memory size

**Table 42** disk
Parameter	Type	Description
size	Integer	Disk size.
unit	String	Unit of the disk size.

**Table 43** spec
Parameter	Type	Description
resource	Resource object	Resource flavors of a training job. Select either flavor_id or pool_id+[flavor_id].
volumes	Array of volumes objects	Volumes attached to a training job.
log_export_path	log_export_path object	Export path of training job logs.

**Table 44** Resource
Parameter	Type	Description
policy	String	Resource flavor of a training job. Options: regular
flavor_id	String	ID of the resource flavor selected for a training job. flavor_id cannot be specified for dedicated resource pools with CPU specifications. The options for dedicated resource pools with GPU/Ascend specifications are as follows: modelarts.pool.visual.xlarge (1 card) modelarts.pool.visual.2xlarge (2 cards) modelarts.pool.visual.4xlarge (4 cards) modelarts.pool.visual.8xlarge (8 cards)
flavor_name	String	Read-only flavor name returned by ModelArts when flavor_id is used.
node_count	Integer	Number of resource replicas selected for a training job.
pool_id	String	Resource pool ID selected for a training job.
flavor_detail	flavor_detail object	Flavors of a training job or an algorithm.

**Table 45** flavor_detail
Parameter	Type	Description
flavor_type	String	Resource flavor type. Options: CPU GPU Ascend
billing	billing object	Billing information of a resource flavor.
flavor_info	flavor_info object	Resource flavor details.

**Table 46** billing
Parameter	Type	Description
code	String	Billing code.
unit_num	Integer	Number of billing units.

**Table 47** flavor_info
Parameter	Type	Description
max_num	Integer	Maximum number of nodes that can be selected. The value 1 indicates that the distributed mode is not supported.
cpu	cpu object	CPU specifications.
gpu	gpu object	GPU specifications.
npu	npu object	Ascend specifications
memory	memory object	Memory information.
disk	disk object	Disk information.

**Table 48** cpu
Parameter	Type	Description
arch	String	CPU architecture.
core_num	Integer	Number of cores.

**Table 49** gpu
Parameter	Type	Description
unit_num	Integer	Number of GPUs.
product_name	String	Product name.
memory	String	Memory.

**Table 50** npu
Parameter	Type	Description
unit_num	String	Number of NPUs.
product_name	String	Product name.
memory	String	Memory.

**Table 51** memory
Parameter	Type	Description
size	Integer	Memory size.
unit	String	Number of memory units.

**Table 52** disk
Parameter	Type	Description
size	String	Disk size.
unit	String	Unit of the disk size. Generally, the value is GB.

**Table 53** volumes
Parameter	Type	Description
nfs	nfs object	Volumes attached in NFS mode.

**Table 54** nfs
Parameter	Type	Description
nfs_server_path	String	NFS server path.
local_path	String	Path for attaching volumes to the training container.
read_only	Boolean	Whether the volumes attached to the container in NFS mode are read-only.

**Table 55** log_export_path
Parameter	Type	Description
obs_url	String	OBS URL for storing training job logs.
host_path	String	Path of the host where training job logs are stored.

**Table 56** JobEndpointsResp
Parameter	Type	Description
ssh	SSHResp object	SSHConnection information.
jupyter_lab	JupyterLab object	JupyterLabConnection information.

**Table 57** SSHResp
Parameter	Type	Description
key_pair_names	Array of strings	SSH key pair name, which can be created and viewed on the Key Pair page of the ECS console.
task_urls	Array of TaskUrls objects	SSH connection address information.

**Table 58** TaskUrls
Parameter	Type	Description
task	String	Task ID of a training job.
url	String	SSH connection address of a training job.

**Table 59** JupyterLab
Parameter	Type	Description
url	String	JupyterLab address of a training job.
token	String	JupyterLab token of the training job.

Example Requests

The following shows how to query a training job whose UUID is 3faf5c03-aaa1-4cbe-879d-24b05d997347.

GET https://endpoint/v2/{project_id}/training-jobs/3faf5c03-aaa1-4cbe-879d-24b05d997347

Example Responses

Status code: 200

{
  "kind" : "job",
  "metadata" : {
    "id" : "3faf5c03-aaa1-4cbe-879d-24b05d997347",
    "name" : "trainjob--py14_mem06-108",
    "description" : "",
    "create_time" : 1636447346315,
    "workspace_id" : "0",
    "user_name" : ""
  },
  "status" : {
    "phase" : "Abnormal",
    "secondary_phase" : "CreateFailed",
    "duration" : 0,
    "start_time" : 0,
    "node_count_metrics" : [ [ 1636447746000, 0 ], [ 1636447755000, 0 ], [ 1636447756000, 0 ] ],
    "tasks" : [ "worker-0" ],
    "running_records" : [ {
      "start_at" : 1701327093,
      "end_at" : 1701322341,
      "start_type" : "init_or_rescheduled",
      "end_recover" : "job_reschedule",
      "end_reason" : "exit with 127",
      "end_related_task" : "worker-2",
      "end_recover_before_downgrade" : "npu_proc_restart"
    }, {
      "start_at" : 1701323345,
      "end_at" : 1701325432,
      "start_type" : "init_or_rescheduled",
      "end_reason" : "job completed"
    } ]
  },
  "algorithm" : {
    "code_dir" : "obs://test/economic_test/py_minist/",
    "boot_file" : "obs://test/economic_test/py_minist/minist_common.py",
    "inputs" : [ {
      "name" : "data_url",
      "local_dir" : "/home/ma-user/modelarts/inputs/data_url_0",
      "remote" : {
        "obs" : {
          "obs_url" : "/test/data/py_minist/"
        }
      }
    } ],
    "outputs" : [ {
      "name" : "train_url",
      "local_dir" : "/home/ma-user/modelarts/outputs/train_url_0",
      "remote" : {
        "obs" : {
          "obs_url" : "/test/train_output/"
        }
      }
    } ],
    "engine" : {
      "engine_id" : "pytorch-cp36-1.4.0-v2",
      "engine_name" : "PyTorch",
      "engine_version" : "PyTorch-1.4.0-python3.6-v2"
    }
  },
  "spec" : {
    "resource" : {
      "flavor_id" : "modelarts.vm.p100.large.eco",
      "node_count" : 1,
      "flavor_detail" : {
        "flavor_type" : "GPU",
        "billing" : {
          "code" : "modelarts.vm.gpu.p100.eco",
          "unit_num" : 1
        },
        "flavor_info" : {
          "cpu" : {
            "arch" : "x86",
            "core_num" : 8
          },
          "gpu" : {
            "unit_num" : 1,
            "memory" : "8GB"
          },
          "memory" : {
            "size" : 64,
            "unit" : "GB"
          }
        }
      }
    }
  }
}