Querying a Built-in Algorithm

Function

This API is used to obtain the details about a built-in model.

URI

GET /v1/{project_id}/built-in-algorithms

Table 1 describes the required parameters.

**Table 1** Parameters
Parameter	Mandatory	Type	Description
project_id	Yes	String	Project ID. For details about how to obtain a project ID, see Obtaining a Project ID and Name.

Request Body

Table 2 describes the request parameters.

**Table 2** Query parameters
Parameter	Mandatory	Type	Description
per_page	No	Integer	Number of job parameters displayed on each page. The value range is [1, 100]. Default value: 10
page	No	Integer	Index of the page to be queried. Default value: 1
sortBy	No	String	Sorting mode of the query. The value can be engine, model_name, model_precision, model_usage, model_precision, model_size, create_time, or parameter. Default value: engine
order	No	String	Sorting order. Options: asc: ascending order desc: descending order. The default value is desc.
search_content	No	String	Search content, for example, a parameter name. By default, this parameter is left blank.

Response Body

Table 3 describes the response parameters.

**Table 3** Parameters
Parameter	Type	Description
is_success	Boolean	Whether the request is successful
error_message	String	Error message of a failed API call. This parameter is not included when the API call succeeds.
error_code	String	Error code of a failed API call. For details, see Error Codes. This parameter is not included when the API call succeeds.
model_total_count	Integer	Number of models
models	Array<Object>	Model parameter list. For details, see Table 4.

**Table 4** **models** structure data
Parameter	Type	Description
model_id	Integer	Model ID
model_name	String	Model name
model_usage	Integer	Model usage. Options: 1: image classification 2: object class and location 3: image semantic segmentation 4: natural language processing 5: image embedding
model_precision	String	Model precision
model_size	Long	Model size, in bytes
model_train_dataset	String	Model training dataset
model_dataset_format	String	Dataset format required by a model
model_description_url	String	URL of the model description
parameter	String	Running parameters of a model. This parameter is a container environment variable when a training job uses a custom image. For details, see the sample request.
create_time	Long	Time when a model is created
engine_id	Long	Engine ID of a model
engine_name	String	Engine name of a model
engine_version	String	Engine version of a model

**Table 5** **parameter** parameters
Parameter	Type	Description
label	String	Parameter name
value	String	Parameter value
required	Boolean	Whether a parameter is mandatory

Sample Request

The following shows how to obtain the algorithm whose name contains configname.

GET https://endpoint//v1/{project_id}/built-in-algorithms?per_page=10&page=1&sortBy=engine&order=asc&search_content=model

Sample Response

Successful response

{
    "models": [
        {
            "model_id": 4,
            "model_name": "ResNet_v2_50",
            "model_usage": 1,
            "model_precision": "75.55%(top1), 92.6%(top5)",
            "model_size": 102503801,
            "model_train_dataset": "ImageNet, 1,000 classes for image classification",
            "model_dataset_format": "shape: [H>=32, W>=32, C>=1]; type: int8",
            "model_description_url": "https://github.com/apache/incubator-mxnet/blob/master/example/image-classification/symbols/resnet.py",
            "parameter": "[{\"label\":\"batch_size\",\"value\":\"4\",\"placeholder_cn\":\"Total number of training images updated each time\",\"placeholder_en\":\"\",\"required\":true},{\"label\":\"lr\",\"value\":\"0.0001\",\"placeholder_cn\":\"Learning rate\",\"placeholder_en\":\"\",\"required\":true},{\"label\":\"save_frequency\",\"value\":\"1\",\"placeholder_cn\":\"Interval for saving the model, indicating that the model is saved every N epochs\",\"placeholder_en\":\"\",\"required\":true},{\"label\":\"num_classes\",\"value\":\"\",\"placeholder_cn\":\"Total number of image classes in training\",\"placeholder_en\":\"\",\"required\":true},{\"label\":\"num_epoch\",\"value\":\"10\",\"placeholder_cn\":\"Number of training epochs\",\"placeholder_en\":\"\",\"required\":true}]",
            "create_time": 1522218780025,
            "engine_id": 501,
            "engine_name": "MXNet",
            "engine_version": "MXNet-1.2.1-python2.7"
        },
        {
            "model_id": 5,
            "model_name": "Faster_RCNN_ResNet_v2_101",
            "model_usage": 2,
            "model_precision": "80.05%(mAP)",
            "model_size": 190936449,
            "model_train_dataset": "PASCAL VOC2007, 20 classes for object detection",
            "model_dataset_format": "shape: [H, W, C==3]; type: int8",
            "model_description_url": "https://github.com/apache/incubator-mxnet/tree/master/example/rcnn",
            "parameter": "[{\"label\":\"lr\",\"value\":\"0.0001\",\"placeholder_cn\":\"Learning rate\",\"placeholder_en\":\"\",\"required\":true},{\"label\":\"eval_frequence\",\"value\":\"1\",\"placeholder_cn\":\"Frequency for validating the model. By default, validation is performed every epoch.\",\"placeholder_en\":\"\",\"required\":true},{\"label\":\"mom\",\"value\":\"0.9\",\"placeholder_cn\":\"Momentum of the training network\",\"placeholder_en\":\"\",\"required\":true},{\"label\":\"wd\",\"value\":\"0.0005\",\"placeholder_cn\":\"Weight decay coefficient\",\"placeholder_en\":\"\",\"required\":true},{\"label\":\"num_classes\",\"value\":\"\",\"placeholder_cn\":\"Total number of image classes in training. The value must plus 1 because there is a background class.\",\"placeholder_en\":\"\",\"required\":true}]",
            "create_time": 1525313224596,
            "engine_id": 501,
            "engine_name": "MXNet",
            "engine_version": "MXNet-1.2.1-python2.7"
        }
    ],
    "model_total_count": 41,
    "is_success": true
}

Failed response

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

Status Code

For details about the status code, see Status Code.

Parent topic: Training Jobs

Previous topic: Obtaining the Name of a Training Job Log File

Next topic: Querying Training Job Logs

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