# Querying the Intelligent Task List by Page

#### Function

This API is used to query the intelligent task list by page, including auto labeling, one-click model deployment, and auto grouping tasks. You can specify the type parameter to query the list of a specific type of tasks.

- Auto labeling: 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.

- One-click model deployment: You can use the one-click model deployment function to create training jobs for the labeled dataset, import a model, and deploy the model as a real-time service all in one step.

- Auto grouping: Unlabeled images are clustered using the clustering algorithm and then processed based on the clustering result. Images can be labeled by group or cleaned.

#### URI

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

Table 1 Path parameters
Parameter | Mandatory | Type | Description
--- | --- | --- | ---
dataset_id | Yes | String | Dataset ID.
project_id | Yes | String | Project ID. For details about how to obtain the project ID, see Obtaining a Project ID.

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 of the paging list. 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. The options are as follows:

- auto-label: active learning

- pre-label: pre-labeling

- auto-grouping: auto grouping

- auto-deploy: one-click model deployment

#### Request Parameters

None

#### Response Parameters

Status code: 200

Table 3 Response body parameters
Parameter | Type | Description
--- | --- | ---
tasks | Array of RunningTask objects | 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 | 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 | Task progress percentage.
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. This type of algorithm achieves faster labeling.

- accurate: In addition to labeled samples, unlabeled samples are used for semi-supervised training. This type of algorithm achieves more accurate labeling.

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. Currently, only value all is available.

collect_sample

Boolean

Whether to enable sample collection. The options are as follows:

- true: Enable sample collection. (Default value)

- false: Do not enable sample collection.

confidence_scope | String | Confidence range of key samples. The minimum and maximum values are separated by hyphens (-). Example: 0.10-0.90.
description | String | Task description.
engine_name | String | Engine name.

export_format

Integer

Format of the exported directory. The options are as follows:

- 1: tree structure. For example: cat/1.jpg,dog/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 the 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 task, 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 parameter.
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 | ID of a resource pool.
property | String | Attribute name.
req_uri | String | Inference path of a batch job.

result_type

Integer

Processing mode of auto grouping results. The options are as follows:

- 0: Save to OBS.

- 1: Save to samples.

samples | Array of SampleLabels objects | List of 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 | URL of the OBS path where the file of a training job is outputted. By default, this parameter is left blank.

version_format

String

Format of a dataset version. The options are as follows:

- Default: default format

- CarbonData: CarbonData (supported only by table datasets)

- CSV: 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. The options are as follows:

- true: Clear hard example attributes. (Default value)

- false: Do not clear hard example attributes.

export_dataset_version_format | String | Format of the dataset version to which data is exported.
export_dataset_version_name | String | Name of the dataset version to which data is exported.

export_dest

String

Export destination. The options are as follows:

- DIR: Export data to OBS. (Default value)

- NEW_DATASET: Export data 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 the training set and validation set based on the specified ratio. The options are as follows:

- true: Allocate the training set and validation set.

- false: Do not allocate the training set and validation set. (Default value)

sample_state

String

Sample status. The options are as follows:

- ALL: labeled

- NONE: unlabeled

- UNCHECK: pending acceptance

- ACCEPTED: accepted

- REJECTED: rejected

- UNREVIEWED: pending review

- REVIEWED: reviewed

- WORKFORCE_SAMPLED: sampled

- WORKFORCE_SAMPLED_UNCHECK: sampling unchecked

- 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 conditions. The relationship between multiple search conditions is OR.
train_sample_ratio | String | Split ratio of training set and verification set during specified version release. The default value is 1.00, indicating that all released versions are training sets.

Table 7 SearchCondition
Parameter | Type | Description
--- | --- | ---
coefficient | String | Filter by coefficient of difficulty.
frame_in_video | Integer | A frame in the video.

hard

String

Whether a sample is a hard sample. The options are as follows:

- 0: non-hard sample

- 1: hard sample

import_origin | String | Filter by data source.
kvp | String | CT dosage, filtered by dosage.
label_list | SearchLabels object | Label search criteria.
labeler | String | Labeler.
metadata | SearchProp object | Search by sample attribute.
parent_sample_id | String | Parent sample ID.
sample_dir | String | Directory where data 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. The options are as follows:

- month: Search for samples added from 30 days ago to the current day.

- day: Search for samples added from yesterday (one day ago) to the current day.

- yyyyMMdd-yyyyMMdd: Search for samples added in a specified period (at most 30 days), in the format of Start date-End date. For example, 20190901-2019091501 indicates that samples generated from September 1 to September 15, 2019 are searched.

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 | List of 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. The options are as follows:

- OR: OR operation

- AND: AND operation

Table 9 SearchLabel
Parameter | Type | Description
--- | --- | ---
name | String | Label name.

op

String

Operation type between multiple attributes. The options are as follows:

- 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. The options are as follows:

- 0: image classification

- 1: object detection

- 100: text classification

- 101: named entity recognition

- 102: text triplet relationship

- 103: text triplet entity

- 200: speech classification

- 201: speech content

- 202: speech paragraph labeling

- 600: video classification

Table 10 SearchProp
Parameter | Type | Description
--- | --- | ---

op

String

Relationship between attribute values. The options are as follows:

- 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> | (Optional) Environment variable key-value pair required for running a model. By default, this parameter is left blank. To ensure data security, do not enter sensitive information, such as plaintext passwords, in environment variables.
instance_count | Integer | Instance number of model deployment, that is, the number of compute nodes.
model_id | String | Model ID.
specification | String | Resource specifications of real-time services. For details, see Deploying Services.
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 range.
dataset_name | String | Dataset name.

dataset_type

String

Dataset type. The options are as follows:

- 0: image classification

- 1: object detection

- 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: custom format

description | String | Description.
dlf_model_job_name | String | Name of a DLF model inference job.
dlf_service_job_name | String | Name of a DLF real-time service job.
dlf_train_job_name | String | Name of a DLF training job.
events | Array of Event objects | Event.
hard_example_path | String | Path for storing hard examples.
hard_select_tasks | Array of HardSelectTask objects | Selected task list of hard examples.
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 | ID of a real-time service.
service_name | String | Name of a real-time service.
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 | ID of a training job.
train_job_name | String | Name of a training job.
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 | Time when an event is created.
description | String | Description.
elapsed_time | Long | Time when an event is executed.
error_code | String | Error code.
error_message | String | Error message.
events | Array of Event objects | Subevent list.
level | Integer | Event severity.
name | String | Event name.
ordinal | Integer | Sequence number.
parent_name | String | Parent event name.

status

String

Status. The options are as follows:

- waiting: waiting

- running: running

- failed: failed

- success: successful

Table 16 HardSelectTask
Parameter | Type | Description
--- | --- | ---
create_at | Long | Creation time.
dataset_id | String | Dataset ID.
dataset_name | String | Dataset name.
hard_select_task_id | String | ID of a hard example filtering task.
task_status | String | Task status.
time | Long | Execution time.
update_at | Long | Update time.

Table 17 SampleLabels
Parameter | Type | Description
--- | --- | ---
labels | Array of SampleLabel objects | Sample label list. If this parameter is left blank, all sample labels are deleted.
metadata | SampleMetadata object | Key-value pair of the sample metadata attribute.
sample_id | String | Sample ID.

sample_type

Integer

Sample type. The options are as follows:

- 0: image

- 1: text

- 2: speech

- 4: table

- 6: video

- 9: custom format

sample_usage

String

Sample usage. The options are as follows:

- TRAIN: training

- EVAL: evaluation

- TEST: test

- INFERENCE: inference

source | String | Source address of sample data.
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 distinguish whether a video is labeled manually or automatically. The options are as follows:

- human: manual labeling

- auto: automatic 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.

type

Integer

Label type. The options are as follows:

- 0: image classification

- 1: object detection

- 100: text classification

- 101: named entity recognition

- 102: text triplet relationship

- 103: text triplet entity

- 200: speech classification

- 201: speech content

- 202: speech paragraph labeling

- 600: video classification

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 are as follows.

- If the text content is "Barack Hussein Obama II (born August 4, 1961) is an American attorney and politician.", the start_index and end_index values of "Barack Hussein Obama II" are 0 and 23, respectively.

- If the text content is "By the end of 2018, the company has more than 100 employees.", the start_index and end_index values of "By the end of 2018" are 0 and 18, 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 first point is located at the upper left corner of the rectangle and the second point is located at the lower right corner of the rectangle. That is, the X coordinate of the first point must be smaller than that of the second point, and the Y coordinate of the second point must be smaller than that of the first 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 point 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 | ID of the head entity in the triplet relationship label, which is a default attribute dedicated to the triplet relationship label.

@modelarts:hard

String

Sample labeled as a hard sample or not, which is a default attribute. Options:

- 0/false: not a hard example

- 1/true: hard example

@modelarts:hard_coefficient | String | Coefficient of difficulty of each label level, which is a default attribute. The value range is [0,1].

@modelarts:hard_reasons

String

Reasons that the sample is a hard sample, which is a default attribute. Use a hyphen (-) to separate every two hard sample reason IDs, for example, 3-20-21-19. The options are as follows:

- 0: No target objects are 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 enhancement result based on GaussianBlur is inconsistent with the prediction result of the original image.

- 20: The data enhancement result based on fliplr is inconsistent with the prediction result of the original image.

- 21: The data enhancement result based on Crop is inconsistent with the prediction result of the original image.

- 22: The data enhancement result based on flipud is inconsistent with the prediction result of the original image.

- 23: The data enhancement result based on scale is inconsistent with the prediction result of the original image.

- 24: The data enhancement result based on translate is inconsistent with the prediction result of the original image.

- 25: The data enhancement result based on shear is inconsistent with the prediction result of the original image.

- 26: The data enhancement result based on superpixels is inconsistent with the prediction result of the original image.

- 27: The data enhancement result based on sharpen is inconsistent with the prediction result of the original image.

- 28: The data enhancement result based on add is inconsistent with the prediction result of the original image.

- 29: The data enhancement 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. The options are as follows:

- bndbox: rectangle

- polygon: polygon

- circle: circle

- line: straight line

- dashed: dotted 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 | ID of the tail entity in the triplet relationship label, which is a default attribute dedicated to the triplet relationship label.

Table 20 SampleMetadata
Parameter | Type | Description
--- | --- | ---

@modelarts:hard

Double

Whether the sample is labeled as a hard sample, which is a default attribute. The options are as follows:

- 0: non-hard sample

- 1: hard sample

@modelarts:hard_coefficient | Double | Coefficient of difficulty of each sample level, which is a default attribute. The value range is [0,1].

@modelarts:hard_reasons

Array of integers

ID of a hard sample reason, which is a default attribute. The options are as follows:

- 0: No target objects are 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 enhancement result based on GaussianBlur is inconsistent with the prediction result of the original image.

- 20: The data enhancement result based on fliplr is inconsistent with the prediction result of the original image.

- 21: The data enhancement result based on Crop is inconsistent with the prediction result of the original image.

- 22: The data enhancement result based on flipud is inconsistent with the prediction result of the original image.

- 23: The data enhancement result based on scale is inconsistent with the prediction result of the original image.

- 24: The data enhancement result based on translate is inconsistent with the prediction result of the original image.

- 25: The data enhancement result based on shear is inconsistent with the prediction result of the original image.

- 26: The data enhancement result based on superpixels is inconsistent with the prediction result of the original image.

- 27: The data enhancement result based on sharpen is inconsistent with the prediction result of the original image.

- 28: The data enhancement result based on add is inconsistent with the prediction result of the original image.

- 29: The data enhancement 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 (width, height, and depth of the image), which is a default attribute, with type of List. 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.

#### Example Requests

Querying the List of Auto Labeling, One-Click Model Deployment, or Auto Grouping Tasks by Page

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

#### Example Responses

Status code: 200

OK

    {
      "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_d910_2_993",
          "hard_select_tasks" : [ {
            "id" : "887c4ac6-26c8-4ca3-b07a-cd75e16fdc2d",
            "hard_select_task_id" : "86711ab3-8ceb-4b0e-bd52-8545b184a2a7",
            "dataset_id" : "OBegCXHxTJ2JHRAZWr0",
            "dataset_name" : "xubo_cls_d910_2",
            "task_status" : "import_dataset_completed",
            "time" : 262,
            "create_at" : 0,
            "update_at" : 0
          } ],
          "continuity" : false
        },
        "export_type" : 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" : [ {
            "model_id" : null,
            "specification" : "modelarts.vm.cpu.2u",
            "weight" : 0,
            "instance_count" : 1,
            "envs" : null
          } ],
          "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",
          "continuity" : false
        },
        "export_type" : 0,
        "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
    }

#### Status Codes

Status Code | Description
--- | ---
200 | OK
401 | Unauthorized
403 | Forbidden
404 | Not Found

#### Error Codes

See Error Codes.

Parent topic: Intelligent Task