Querying a Built-in Algorithm¶
Function¶
This API is used to query the details about a built-in model.
URI¶
GET /v1/{project_id}/built-in-algorithms
Table 1 describes the required parameters.
Parameter | Mandatory | Type | Description |
---|---|---|---|
project_id | Yes | String | Project ID. For details about how to obtain the project ID, see Obtaining a Project ID. |
Request Body¶
Table 2 describes the request 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. The options are as follows:
|
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.
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. |
Parameter | Type | Description |
---|---|---|
model_id | Integer | Model ID |
model_name | String | Model name |
model_usage | Integer | Model usage. The options are as follows:
|
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 |
Parameter | Type | Description |
---|---|---|
label | String | Parameter name |
value | String | Parameter value |
required | Boolean | Whether a parameter is mandatory |
Samples¶
The following shows how to query the algorithm whose name contains configname.
Sample request
GET https://endpoint//v1/{project_id}/built-in-algorithms?per_page=10&page=1&sortBy=engine&order=asc&search_content=model
Successful sample 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 sample response
{ "is_success": false, "error_message": "Error string", "error_code": "ModelArts.0105" }
Status Code¶
For details about the status code, see Status Code.
Error Codes¶
See Error Codes.