Querying a Built-in Algorithm

1: image classification
2: object class and location
3: image semantic segmentation
4: natural language processing

Sample Code

In ModelArts notebook, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.

    
         from modelarts.session import Session
from modelarts.estimator import Estimator
session = Session()
algo_info = Estimator.get_built_in_algorithms(modelarts_session=session)
print(algo_info)

Parameters

**Table 1** **get_built_in_algorithms** request parameters
Parameter	Mandatory	Type	Description
modelarts_session	Yes	Object	Session object. For details about the initialization method, see Session Authentication.

**Table 2** **get_built_in_algorithms** response parameters
Parameter	Type	Description
error_msg	String	Error message when the API call fails. This parameter is not included when the API call succeeds.
error_code	String	Error code when the API fails to be called. For details, see Error Codes. This parameter is not included when the API call succeeds.
model_total_count	Integer	Number of models
models	JSON Array	Parameter list of a model
is_success	Boolean	Whether the API call succeeds

**Table 3** **models** parameters
Parameter	Type	Description
model_id	Integer	Model ID
model_name	String	Model name
model_usage	Integer	Model usage. The options are as follows: 1: image classification 2: object class and location 3: image semantic segmentation 4: natural language processing
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	JSON Array	Running parameters of a model. It is a collection of label-value pairs. This parameter is a container environment variable when a 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