Obtaining an Auto Labeling Task List by Page

Function

Obtain auto labeling tasks by page, including auto labeling and auto grouping tasks. You can specify type to obtain the list of a specific type of tasks.

Auto labeling means learning and training are performed based on the selected labels and images and an existing model is selected to quickly label the remaining images. Auto labeling includes active learning and pre-labeling.
- Active learning: The system uses semi-supervised learning and hard example filtering to perform auto labeling, reducing manual labeling workload and helping you find hard examples.
- Pre-labeling: You select a model on the Model Management page for auto labeling.
In auto grouping, unlabeled images are clustered using a clustering algorithm and then processed based on the clustering result. Images can be labeled by group or cleaned.

Debugging

You can debug this API in API Explorer which supports automatic authentication. API Explorer can automatically generate SDK code examples and provide the SDK code example debugging.

URI

GET /v2/{project_id}/datasets/{dataset_id}/tasks

**Table 1** URI parameters
Parameter	Mandatory	Type	Description
dataset_id	Yes	String	Dataset ID
project_id	Yes	String	Project ID. For details, see Obtaining a Project ID and Name.

**Table 2** Query parameters
Parameter	Mandatory	Type	Description
limit	No	Integer	Maximum number of records returned on each page. The value ranges from 1 to 100. The default value is 10.
offset	No	Integer	Start page for pagination display. The default value is 0.
task_name	No	String	Task name filtering.
type	No	String	Task type. If this parameter is not delivered, the auto labeling (active learning or pre-labeling) task list is returned by default. Options: auto-label: active learning pre-label: pre-labeling auto-grouping: auto grouping

Request Parameters

None

Response Parameters

Status code: 200

**Table 3** Response body parameters
Parameter	Type	Description
exist_running_deploy_task	Boolean	Whether the dataset contains running (including initialization) one-click model deployment. Options: true: The dataset contains running one-click model deployment. false: The dataset does not contain running one-click model deployment.
tasks	Array of RunningTask objects	Specifies the task list.
total_count	Integer	Total number of tasks.

**Table 4** RunningTask
Parameter	Type	Description
annotated_sample_count	Integer	Number of labeled samples
code	String	Error code
config	SmartTaskConfig object	Task configuration
create_time	String	Task creation time
dataset_id	String	Dataset ID
elapsed_time	Long	Task execution time
error_code	String	Error code
error_detail	String	Error details
error_msg	String	Error message
message	String	Error message
model_id	String	Model ID
model_name	String	Model name
model_version	String	Model version
progress	Float	Current task progress
result	Result object	Task result
status	Integer	Task status
task_id	String	Task ID
task_name	String	Task name
total_sample_count	Integer	Total number of samples
type	Integer	Task type
unconfirmed_sample_count	Integer	Number of samples to be confirmed

**Table 5** SmartTaskConfig
Parameter	Type	Description
algorithm_type	String	Algorithm type for auto labeling. Options: fast: Only labeled samples are used for training. accurate: Unlabeled samples are also used for semi-supervised training.
ambiguity	Boolean	Whether to perform clustering based on the image blurring degree.
annotation_output	String	Output path of the active learning labeling result
collect_rule	String	Sample collection rule. The default value is all, indicating full collection. Only all is available.
collect_sample	Boolean	Whether to enable sample collection. Options: true: Sample collection is enabled. (Default) false: Sample collection is disabled.
confidence_scope	String	Confidence range of key samples. The minimum and maximum values are separated by hyphens (-), for example, 0.10-0.90.
description	String	Job description
engine_name	String	Engine name
export_format	Integer	Format of the exported directory. Options: 1: tree structure, for example, rabbits/1.jpg,bees/2.jpg. 2: tile structure, for example, 1.jpg, 1.txt; 2.jpg, 2.txt
export_params	ExportParams object	Parameters of a dataset export task
flavor	Flavor object	Training resource flavor
image_brightness	Boolean	Whether to perform clustering based on the image brightness
image_colorfulness	Boolean	Whether to perform clustering based on the image color
inf_cluster_id	String	ID of a dedicated cluster. This parameter is left blank by default, indicating that a dedicated cluster is not used. When using a dedicated cluster to deploy services, ensure that the cluster status is normal. After this parameter is set, the network configuration of the cluster is used, and the vpc_id parameter does not take effect.
inf_config_list	Array of InfConfig objects	Configuration list required for running an inference job, which is optional and left blank by default
inf_output	String	Output path of inference in active learning
infer_result_output_dir	String	OBS directory for storing sample prediction results. This parameter is optional. The {service_id}-infer-result subdirectory in the output_dir directory is used by default.
key_sample_output	String	Output path of hard examples in active learning
log_url	String	OBS URL of the logs of a training job. By default, this parameter is left blank.
manifest_path	String	Path of the manifest file, which is used as the input for training and inference
model_id	String	Model ID
model_name	String	Model name
model_parameter	String	Model parameters
model_version	String	Model version
n_clusters	Integer	Number of clusters
name	String	Task name
output_dir	String	Sample output path. The format is as follows: Dataset output path/Dataset name-Dataset ID/annotation/auto-deploy/. Example: /test/work_1608083108676/dataset123-g6IO9qSu6hoxwCAirfm/annotation/auto-deploy/.
parameters	Array of TrainingParameter objects	Running parameters of a training job
pool_id	String	Resource pool ID
property	String	Attribute name
req_uri	String	Inference path of a batch job
result_type	Integer	Processing mode of auto grouping results. Options: 0: The results are saved to OBS. 1: The results are saved to samples.
samples	Array of SampleLabels objects	Labeling information for samples to be auto labeled
stop_time	Integer	Timeout interval, in minutes. The default value is 15 minutes. This parameter is used only in the scenario of auto labeling for videos.
time	String	Timestamp in active learning
train_data_path	String	Path for storing existing training datasets
train_url	String	OBS URL of the output file of a training job. By default, this parameter is left blank.
version_format	String	Format of a dataset version. Options: Default CarbonData (supported only by table datasets) CSV
worker_server_num	Integer	Number of workers in a training job

**Table 6** ExportParams
Parameter	Type	Description
clear_hard_property	Boolean	Whether to clear hard example attributes. Options: true: Hard example attributes are cleared. (Default) false: Hard example attributes are not cleared.
export_dataset_version_format	String	Format of the dataset version to be exported
export_dataset_version_name	String	Name of the dataset version to be exported
export_dest	String	Format of the exported dataset. Options: DIR: Export to OBS. (default) NEW_DATASET: Export to a new dataset.
export_new_dataset_name	String	Name of the new dataset to which data is exported
export_new_dataset_work_path	String	Working directory of the new dataset to which data is exported
ratio_sample_usage	Boolean	Whether to randomly allocate data to the training and validation datasets based on the specified ratio. Options: true: The data is randomly allocated to the training and validation datasets. false: The data is not randomly allocated to the training and validation datasets. (Default)
sample_state	String	Sample status. Options: __ALL__: labeled __NONE__: unlabeled __UNCHECK__: to be accepted __ACCEPTED__: accepted __REJECTED__: rejected __UNREVIEWED__: to be reviewed __REVIEWED__: reviewed __WORKFORCE_SAMPLED__: sampled __WORKFORCE_SAMPLED_UNCHECK__: sampling pending check __WORKFORCE_SAMPLED_CHECKED__: sampling checked __WORKFORCE_SAMPLED_ACCEPTED__: sampling accepted __WORKFORCE_SAMPLED_REJECTED__: sampling rejected __AUTO_ANNOTATION__: to be confirmed
samples	Array of strings	ID list of exported samples
search_conditions	Array of SearchCondition objects	Exported search criteria. Multiple search criteria are in the OR relationship.
train_sample_ratio	String	Split ratio of training and validation datasets for specified version release. The default value is 1.00, indicating that all data is allocated to the training dataset.

**Table 7** SearchCondition
Parameter	Type	Description
coefficient	String	Filter by difficulty coefficient
frame_in_video	Integer	A frame in the video
hard	String	Whether a sample is a hard example. Options: 0: The label is not a hard example. 1: The label is a hard example.
import_origin	String	Filter by data source
kvp	String	CT dosage, filtered by dosage.
label_list	SearchLabels object	Label search criteria
labeler	String	Annotator
metadata	SearchProp object	Search by sample attribute
parent_sample_id	String	Parent sample ID
sample_dir	String	Directory where samples are stored (the directory must end with a slash (/)). Only samples in the specified directory are searched for. Recursive search of directories is not supported.
sample_name	String	Search by sample name, including the file name extension
sample_time	String	When a sample is added to the dataset, an index is created based on the last modification time (accurate to day) of the sample on OBS. You can search for the sample based on the time. Options: month: Search for samples added in the last 30 days day: Search for samples added from yesterday (one day before) to today. yyyyMMdd-yyyyMMdd: Search for samples added in a specified period. The format is Start date-End date. Maximum number of days for the search: 30. For example, 20190901-2019091501 indicates that samples generated from September 1 to September 15, 2019 are searched for.
score	String	Search by confidence
slice_thickness	String	DICOM layer thickness. Samples are filtered by layer thickness.
study_date	String	DICOM scanning time
time_in_video	String	A time point in the video

**Table 8** SearchLabels
Parameter	Type	Description
labels	Array of SearchLabel objects	Label search criteria
op	String	If you want to search for multiple labels, op must be specified. If you search for only one label, op can be left blank. Options: OR: OR operation AND: AND operation

**Table 9** SearchLabel
Parameter	Type	Description
name	String	Label name
op	String	Operation type between multiple attributes. Options: OR: OR operation AND: AND operation
property	Map<String,Array<String>>	Label attribute, which is in the Object format and stores any key-value pairs. key indicates the attribute name, and value indicates the value list. If value is null, the search is not performed by value. Otherwise, the search value can be any value in the list.
type	Integer	Label type. Options: 0: image classification 1: object detection 3: image segmentation 100: text classification 101: named entity recognition 102: text triplet relationship 103: text triplet entity 200: sound classification 201: speech content 202: speech paragraph labeling 600: video labeling

**Table 10** SearchProp
Parameter	Type	Description
op	String	Relationship between attribute values. Options: AND: AND relationship OR: OR relationship
props	Map<String,Array<String>>	Search criteria of an attribute. Multiple search criteria can be set.

**Table 11** Flavor
Parameter	Type	Description
code	String	Attribute code of a resource specification, which is used for task creating

**Table 12** InfConfig
Parameter	Type	Description
envs	Map<String,String>	Environment variable key-value pair required for running a model. This parameter is optional. By default, it is left blank. To ensure data security, do not enter sensitive information in environment variables.
instance_count	Integer	Number of instances (compute nodes) for deploying a model
model_id	String	Model ID
specification	String	Resource specifications of real-time services. For details, see Deploying a Service.
weight	Integer	Traffic weight allocated to a model. This parameter is mandatory only when infer_type is set to real-time. The sum of the weights must be 100.

**Table 13** TrainingParameter
Parameter	Type	Description
label	String	Parameter name
value	String	Parameter value

**Table 14** Result
Parameter	Type	Description
annotated_sample_count	Integer	Number of labeled samples
confidence_scope	String	Confidence. The value ranges from 0 to 1.
dataset_name	String	Dataset name, which can contain 1 to 100 characters. Only letters, digits, underscores (_), and hyphens (-) are allowed.
dataset_type	String	Dataset type. Options: 0: image classification 1: object detection 3: image segmentation 100: text classification 101: named entity recognition 102: text triplet 200: sound classification 201: speech content 202: speech paragraph labeling 400: table dataset 600: video labeling 900: free format
description	String	Description
dlf_model_job_name	String	DLF model inference job name
dlf_service_job_name	String	DLF real-time service job name
dlf_train_job_name	String	DLF training job name
events	Array of Event objects	Event
hard_example_path	String	Path for storing hard examples
hard_select_tasks	Array of HardSelectTask objects	List of selected hard example jobs
manifest_path	String	Path for storing the manifest files
model_id	String	Model ID
model_name	String	Model name
model_version	String	Model version
samples	Array of SampleLabels objects	Inference result of the real-time video service.
service_id	String	Real-time service ID
service_name	String	Real-time service name
service_resource	String	ID of the real-time service bound to a user.
total_sample_count	Integer	Total number of samples
train_data_path	String	Path for storing training data
train_job_id	String	Training job ID
train_job_name	String	Training job name
unconfirmed_sample_count	Integer	Number of samples to be confirmed
version_id	String	Dataset version ID
version_name	String	Dataset version name
workspace_id	String	Workspace ID. If no workspace is created, the default value is 0. If a workspace is created and used, use the actual value.

**Table 15** Event
Parameter	Type	Description
create_time	Long	Event creation time
description	String	Description
elapsed_time	Long	Event execution time
error_code	String	Error code
error_message	String	Error message
events	Array of Event objects	List of sub-events
level	Integer	Event severity
name	String	Event name
ordinal	Integer	Sequence number
parent_name	String	Parent event name
status	String	Status. Options: waiting: The event is waiting. running: The event is running. failed: The event is failed. success: The subtask is successfully executed.

**Table 16** HardSelectTask
Parameter	Type	Description
create_at	Long	Task creation time
dataset_id	String	Dataset ID
dataset_name	String	Dataset name
hard_select_task_id	String	ID of selected hard example task
task_status	String	Task status
time	Long	Task execution time
update_at	Long	Task update time

**Table 17** SampleLabels
Parameter	Type	Description
labels	Array of SampleLabel objects	List of sample labels. If this parameter is left blank, all sample labels are deleted.
metadata	SampleMetadata object	Attribute key-value pair of the sample metadata
sample_id	String	Sample ID
sample_type	Integer	Sample type. Options: 0: image 1: text 2: audio 4: table 6: video 9: free format
sample_usage	String	Sample usage. Options: TRAIN: training EVAL: validation TEST: test INFERENCE: inference
source	String	Source address of sample data, which can be obtained by calling the sample list API.
worker_id	String	ID of a labeling team member

**Table 18** SampleLabel
Parameter	Type	Description
annotated_by	String	Video labeling method, which is used to determine whether a video is labeled manually or automatically. Options: human: manual labeling auto: auto labeling
id	String	Label ID
name	String	Label name
property	SampleLabelProperty object	Attribute key-value pair of the sample label, such as the object shape and shape feature
score	Float	Confidence. The value ranges from 0 to 1.
type	Integer	Label type. Options: 0: image classification 1: object detection 3: image segmentation 100: text classification 101: named entity recognition 102: text triplet relationship 103: text triplet entity 200: sound classification 201: speech content 202: speech paragraph labeling 600: video labeling

**Table 19** SampleLabelProperty
Parameter	Type	Description
@modelarts:content	String	Speech text content, which is a default attribute dedicated to the speech label (including the speech content and speech start and end points)
@modelarts:end_index	Integer	End position of the text, which is a default attribute dedicated to the named entity label. The end position does not include the character corresponding to the value of end_index. Examples: If the text is "Barack Hussein Obama II (born on August 4, 1961) is an attorney and politician.", start_index and end_index of Barack Hussein Obama II are 0 and 23, respectively. If the text is "Hope is the thing with feathers", start_index and end_index of Hope are 0 and 4, respectively.
@modelarts:end_time	String	Speech end time, which is a default attribute dedicated to the speech start/end point label, in the format of hh:mm:ss.SSS. (hh indicates hour; mm indicates minute; ss indicates second; and SSS indicates millisecond.)
@modelarts:feature	Object	Shape feature, which is a default attribute dedicated to the object detection label, with type of List The upper left corner of an image is used as the coordinate origin [0, 0]. Each coordinate point is represented by [x, y]. x indicates the horizontal coordinate, and y indicates the vertical coordinate (both x and y are greater than or equal to 0). The format of each shape is as follows: bndbox: consists of two points, for example, [[0,10],[50,95]]. The upper left vertex of the rectangle is the first point, and the lower right vertex is the second point. That is, the x-coordinate of the first point must be less than the x-coordinate of the second point, and the y-coordinate of the first point must be less than the y-coordinate of the second point. polygon: consists of multiple points that are connected in sequence to form a polygon, for example, [[0,100],[50,95],[10,60],[500,400]]. circle: consists of the center and radius, for example, [[100,100],[50]]. line: consists of two points, for example, [[0,100],[50,95]]. The first point is the start point, and the second point is the end point. dashed: consists of two points, for example, [[0,100],[50,95]]. The first point is the start point, and the second point is the end point. point: consists of one point, for example, [[0,100]]. polyline: consists of multiple points, for example, [[0,100],[50,95],[10,60],[500,400]].
@modelarts:from	String	Start entity ID of the triplet relationship label, which is a default attribute dedicated to the triplet relationship label
@modelarts:hard	String	Whether the sample is labeled as a hard example, which is a default attribute. Options: 0/false: The label is not a hard example. 1/true: The label is a hard example.
@modelarts:hard_coefficient	String	Coefficient of difficulty of each label level, which is a default attribute. The value ranges from 0 to 1.
@modelarts:hard_reasons	String	Reasons why the sample is a hard example, which is a default attribute. Use a hyphen (-) to separate every two hard example reason IDs, for example, 3-20-21-19. Options: 0: No object is identified. 1: The confidence is low. 2: The clustering result based on the training dataset is inconsistent with the prediction result. 3: The prediction result is greatly different from the data of the same type in the training dataset. 4: The prediction results of multiple consecutive similar images are inconsistent. 5: There is a large offset between the image resolution and the feature distribution of the training dataset. 6: There is a large offset between the aspect ratio of the image and the feature distribution of the training dataset. 7: There is a large offset between the brightness of the image and the feature distribution of the training dataset. 8: There is a large offset between the saturation of the image and the feature distribution of the training dataset. 9: There is a large offset between the color richness of the image and the feature distribution of the training dataset. 10: There is a large offset between the definition of the image and the feature distribution of the training dataset. 11: There is a large offset between the number of frames of the image and the feature distribution of the training dataset. 12: There is a large offset between the standard deviation of area of image frames and the feature distribution of the training dataset. 13: There is a large offset between the aspect ratio of image frames and the feature distribution of the training dataset. 14: There is a large offset between the area portion of image frames and the feature distribution of the training dataset. 15: There is a large offset between the edge of image frames and the feature distribution of the training dataset. 16: There is a large offset between the brightness of image frames and the feature distribution of the training dataset. 17: There is a large offset between the definition of image frames and the feature distribution of the training dataset. 18: There is a large offset between the stack of image frames and the feature distribution of the training dataset. 19: The data augmentation result based on GaussianBlur is inconsistent with the prediction result of the original image. 20: The data augmentation result based on fliplr is inconsistent with the prediction result of the original image. 21: The data augmentation result based on Crop is inconsistent with the prediction result of the original image. 22: The data augmentation result based on flipud is inconsistent with the prediction result of the original image. 23: The data augmentation result based on scale is inconsistent with the prediction result of the original image. 24: The data augmentation result based on translate is inconsistent with the prediction result of the original image. 25: The data augmentation result based on shear is inconsistent with the prediction result of the original image. 26: The data augmentation result based on superpixels is inconsistent with the prediction result of the original image. 27: The data augmentation result based on sharpen is inconsistent with the prediction result of the original image. 28: The data augmentation result based on add is inconsistent with the prediction result of the original image. 29: The data augmentation result based on invert is inconsistent with the prediction result of the original image. 30: The data is predicted to be abnormal.
@modelarts:shape	String	Object shape, which is a default attribute dedicated to the object detection label and is left empty by default. Options: bndbox: rectangle polygon: polygon circle: circle line: straight line dashed: dashed line point: point polyline: polyline
@modelarts:source	String	Speech source, which is a default attribute dedicated to the speech start/end point label and can be set to a speaker or narrator
@modelarts:start_index	Integer	Start position of the text, which is a default attribute dedicated to the named entity label. The start value begins from 0, including the character corresponding to the value of start_index.
@modelarts:start_time	String	Speech start time, which is a default attribute dedicated to the speech start/end point label, in the format of hh:mm:ss.SSS. (hh indicates hour; mm indicates minute; ss indicates second; and SSS indicates millisecond.)
@modelarts:to	String	Direction entity ID of the triplet relationship label, which is a default attribute dedicated to the triplet relationship label

**Table 20** SampleMetadata
Parameter	Type	Description
@modelarts:import_origin	Integer	Sample source, which is a default attribute.
@modelarts:hard	Double	Whether the sample is labeled as a hard sample, which is a default attribute. Options: 0: The label is not a hard example. 1: The label is a hard example.
@modelarts:hard_coefficient	Double	Coefficient of difficulty of each sample level, which is a default attribute. The value ranges from 0 to 1.
@modelarts:hard_reasons	Array of integers	ID of a hard example reason, which is a default attribute. Options: 0: No object is identified. 1: The confidence is low. 2: The clustering result based on the training dataset is inconsistent with the prediction result. 3: The prediction result is greatly different from the data of the same type in the training dataset. 4: The prediction results of multiple consecutive similar images are inconsistent. 5: There is a large offset between the image resolution and the feature distribution of the training dataset. 6: There is a large offset between the aspect ratio of the image and the feature distribution of the training dataset. 7: There is a large offset between the brightness of the image and the feature distribution of the training dataset. 8: There is a large offset between the saturation of the image and the feature distribution of the training dataset. 9: There is a large offset between the color richness of the image and the feature distribution of the training dataset. 10: There is a large offset between the definition of the image and the feature distribution of the training dataset. 11: There is a large offset between the number of frames of the image and the feature distribution of the training dataset. 12: There is a large offset between the standard deviation of area of image frames and the feature distribution of the training dataset. 13: There is a large offset between the aspect ratio of image frames and the feature distribution of the training dataset. 14: There is a large offset between the area portion of image frames and the feature distribution of the training dataset. 15: There is a large offset between the edge of image frames and the feature distribution of the training dataset. 16: There is a large offset between the brightness of image frames and the feature distribution of the training dataset. 17: There is a large offset between the definition of image frames and the feature distribution of the training dataset. 18: There is a large offset between the stack of image frames and the feature distribution of the training dataset. 19: The data augmentation result based on GaussianBlur is inconsistent with the prediction result of the original image. 20: The data augmentation result based on fliplr is inconsistent with the prediction result of the original image. 21: The data augmentation result based on Crop is inconsistent with the prediction result of the original image. 22: The data augmentation result based on flipud is inconsistent with the prediction result of the original image. 23: The data augmentation result based on scale is inconsistent with the prediction result of the original image. 24: The data augmentation result based on translate is inconsistent with the prediction result of the original image. 25: The data augmentation result based on shear is inconsistent with the prediction result of the original image. 26: The data augmentation result based on superpixels is inconsistent with the prediction result of the original image. 27: The data augmentation result based on sharpen is inconsistent with the prediction result of the original image. 28: The data augmentation result based on add is inconsistent with the prediction result of the original image. 29: The data augmentation result based on invert is inconsistent with the prediction result of the original image. 30: The data is predicted to be abnormal.
@modelarts:size	Array of objects	image size, including width, height, and depth. The type is List[/topic/body/section/table/tgroup/tbody/row/entry/p/br. {""}) (br]. In the list, the first number indicates the width (pixels), the second number indicates the height (pixels), and the third number indicates the depth (the depth can be left blank and the default value is 3). For example, [100,200,3] and [100,200] are both valid. Note: This parameter is mandatory only when the sample label list contains the object detection label.

Request Example

Run the following command to obtain auto labeling or auto grouping tasks by page:

GET https://{endpoint}/v2/{project_id}/datasets/{dataset_id}/tasks?offset=0&limit=10

Response Example

Status code: 200

{
  "tasks" : [ {
    "dataset_id" : "OBegCXHxTJ2JHRAZWr0",
    "task_id" : "14cyxyu6UXaNT3lrPFl",
    "type" : 1,
    "create_time" : "2020-11-03 15:22:39",
    "status" : 3,
    "code" : "ModelArts.4996",
    "message" : "prelabel task execute successfully.",
    "elapsed_time" : 531,
    "result" : {
      "service_id" : "ee2ade80-0967-4ef3-b6da-e8c873017b9a",
      "service_name" : "prelabel_infer_1604388201993_xubo_cls_snt9_2_993",
      "hard_select_tasks" : [ {
        "hard_select_task_id" : "86711ab3-8ceb-4b0e-bd52-8545b184a2a7",
        "dataset_id" : "OBegCXHxTJ2JHRAZWr0",
        "dataset_name" : "xubo_cls_snt9_2",
        "task_status" : "import_dataset_completed",
        "time" : 262,
        "create_at" : 0,
        "update_at" : 0
      } ]
    },
    "progress" : 100.0,
    "total_sample_count" : 246,
    "annotated_sample_count" : 38,
    "unconfirmed_sample_count" : 208,
    "model_id" : "c717a39f-c64f-45df-a9d3-be9ed79cdcb4",
    "model_name" : "auto-deploy-50041602581620628",
    "model_version" : "0.0.1",
    "config" : {
      "ambiguity" : false,
      "name" : "5fXxR01TyUoiobqNEd9",
      "worker_server_num" : 0,
      "inf_config_list" : [ {
        "specification" : "modelarts.vm.cpu.2u",
        "weight" : 0,
        "instance_count" : 1
      } ],
      "collect_sample" : false,
      "confidence_scope" : "0.0-0.5",
      "algorithm_type" : "supervisory",
      "image_brightness" : false,
      "image_colorfulness" : false
    }
  }, {
    "dataset_id" : "OBegCXHxTJ2JHRAZWr0",
    "task_id" : "5QPy73VwnwHi5NqvbcP",
    "type" : 0,
    "create_time" : "2020-10-31 16:11:37",
    "status" : 3,
    "code" : "ModelArts.4996",
    "message" : "task executed successfully.",
    "elapsed_time" : 397,
    "result" : {
      "train_job_name" : "BNFURaEyftGNMITaBiv",
      "train_job_id" : "74679",
      "version_id" : "89745"
    },
    "progress" : 100.0,
    "total_sample_count" : 246,
    "annotated_sample_count" : 38,
    "unconfirmed_sample_count" : 198,
    "model_name" : "Supervisory",
    "model_version" : "0.0.1",
    "config" : {
      "ambiguity" : false,
      "worker_server_num" : 0,
      "collect_sample" : false,
      "algorithm_type" : "fast",
      "image_brightness" : false,
      "image_colorfulness" : false
    }
  } ],
  "total_count" : 2,
  "exist_running_deploy_task" : false
}