Querying the Intelligent Task List by Page¶

Function¶

This interface is used to query the intelligent task list by page, including intelligent labeling and automatic grouping tasks. You can specify the type parameter to query the list of a specific type of tasks.

Intelligent Labeling allows you to select an existing model in the system for intelligent labeling based on the labeling and image learning training in the current labeling phase 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. - 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.

Debugging¶

You can debug this API through automatic authentication in or use the SDK sample code generated by API Explorer.

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 a project ID, 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 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 transferred, the list of intelligent labeling (active learning or pre-labeling) tasks is returned by default. The options are as follows: - 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 a running one-click model deployment. false: The dataset does not contain a running one-click model deployment.
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. Options: 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. Options: 1: tree structure. Example: rabbits/1.jpg,bees/2.jpg. 2: tile structure. 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	Runtime 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. Options: 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. Options: 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. Options: 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. Options: 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. Options: 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. Options: __ALL__: labeled __NONE__: unlabeled __UNCHECK__: to be checked __ACCEPTED__: accepted __REJECTED__: rejected __UNREVIEWED__: to be reviewed __REVIEWED__: reviewed __WORKFORCE_SAMPLED__: reviewed data sampled __WORKFORCE_SAMPLED_UNCHECK__: samples to be checked __WORKFORCE_SAMPLED_CHECKED__: samples checked __WORKFORCE_SAMPLED_ACCEPTED__: samples accepted __WORKFORCE_SAMPLED_REJECTED__: samples rejected __AUTO_ANNOTATION__: to be checked
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. Options: 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. Options: 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. 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>	(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 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. 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: 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. Options: 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. Options: 0: image 1: text 2: speech 4: table 6: video 9: custom format
sample_usage	String	Sample usage. Options: 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. Options: 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. 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. Example: If the text is "Barack Hussein Obama II (born 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 the image is used as the coordinate origin [0, 0]. Each coordinate point is represented by [x, y], where x indicates the horizontal coordinate and y indicates the vertical coordinate (both x and y are >=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	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. Options: 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. Options: 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:import_origin	Integer	Sample source, which is a built-in attribute.
@modelarts:hard	Double	Whether the sample is labeled as a hard sample, which is a default attribute. Options: 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. 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 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<Integer>. 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 Intelligent Labeling or Automatic Grouping Tasks in Pagination Mode

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

Example Responses¶

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_d910_2_993",
      "hard_select_tasks" : [ {
        "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
      } ]
    },
    "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
}

Status Codes¶

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

Error Codes¶

See Error Codes.

last updated: 2024-11-28 19:02 UTC - commit: 7e602c6c50500da4fb13dea67589040fd3f7deb8