Obtaining Details About an AI Application

Function

This API is used to query details about an AI application based on the AI application ID.

URI

GET /v1/{project_id}/models/{model_id}

**Table 1** Path Parameters
Parameter	Mandatory	Type	Description
model_id	Yes	String	Model ID
project_id	Yes	String	Project ID. For details, see Obtaining a Project ID and Name.

Request Parameters

**Table 2** Request header parameters
Parameter	Mandatory	Type	Description
X-Auth-Token	Yes	String	User token. It can be obtained by calling the IAM API that is used to obtain a user token. The value of X-Subject-Token in the response header is the user token.

Response Parameters

Status code: 200

**Table 3** Response body parameters
Parameter	Type	Description
model_version	String	Model version
source_job_version	String	Version of the source training job
source_location	String	OBS path where the model is located or the template address of the SWR image
source_job_id	String	ID of the source training job
source_copy	String	Whether to enable image replication. This parameter is valid only when model_type is set to Image. true: Default value, indicating that image replication is enabled. After this function is enabled, AI applications cannot be rapidly created, and modifying or deleting an image in the SWR source directory will not affect service deployment. false: Image replication is not enabled. After this function is disabled, AI applications can be rapidly created, but modifying or deleting an image in the SWR source directory will affect service deployment.
description	String	Model description
project	String	Project ID of the tenant to which a model belongs
workspace_id	String	Workspace ID. Value 0 indicates the default workspace.
model_algorithm	String	Model algorithm type, which can be predict_analysis, object_detection, or image_classification
model_name	String	Model name
tenant	String	Account ID of the tenant to which a model belongs
model_docs	Array of GuideDoc objects	List of template documents
owner	String	ID of the tenant to which a model belongs
execution_code	String	OBS path for storing the execution code. The name of the execution code file is consistently to be customize_service.py.
schema_doc	String	Download address of the model schema file
image_address	String	Image path generated after model packaging
output_params	Array of ModelParamsInfo objects	Collection of output parameters of a model
health	ModelHealth object	Model health check information
runtime	String	Model runtime environment
model_metrics	String	Model precision
source_type	String	Model source type. This parameter is valid and its value is auto only if the model is deployed using ExeML.
model_type	String	Model type. The value is TensorFlow/Image/PyTorch/Template/MindSpore.
model_id	String	Model ID
dependencies	Array of ModelDependencies objects	Package required for running the code and model
model_size	Long	Model size, in bytes
model_status	String	Model status
apis	String	All API input and output parameter information of a model, which is obtained from the model preview
model_source	String	Model source. Options: auto: ExeML algos: preset algorithm custom: customized
tunable	Boolean	Whether a model can be tuned. Options: true: Supported. false: Not supported.
market_flag	Boolean	Whether a model is subscribed from the marketplace. Options: true: from the market false: no
publishable_flag	Boolean	Whether a model can be published to the marketplace. Options: true: The product can be released to the market. false: The product cannot be released to the marketplace.
model_labels	Array of strings	Model label array
labels_map	Map<String,String>	Model label map. The key is consistently to be labels, and the value is the model label array.
install_type	Array of strings	Supported service type for deployment
config	String	Model configurations
specification	ModelSpecification object	Minimum specifications for model deployment
input_params	Array of ModelParamsInfo objects	Collection of input parameters of a model
create_at	Long	Time when a model is created, in milliseconds calculated from 1970.1.1 0:0:0 UTC.

**Table 4** GuideDoc
Parameter	Type	Description
doc_url	String	HTTP(S) link of the document
doc_name	String	Document name, which must start with a letter.

**Table 5** ModelHealth
Parameter	Type	Description
protocol	String	Request protocol for health check, which can only be HTTP
initial_delay_seconds	String	After an instance is started, a health check starts after the time configured in initial_delay_seconds expires.
timeout_seconds	String	Health check timeout
url	String	Path to the health check API

**Table 6** ModelDependencies
Parameter	Type	Description
installer	String	Installation mode. Only pip is supported.
packages	Array of Packages objects	Collection of dependency packages

**Table 7** Packages
Parameter	Type	Description
package_version	String	Version of a dependency package. If this parameter is left blank, the latest version is installed by default.
package_name	String	Name of a dependency package. Ensure that the package name is correct and available.
restraint	String	Version restriction, which can be EXACT, ATLEAST, or ATMOST. This parameter is mandatory only when package_version is available.

**Table 8** ModelSpecification
Parameter	Type	Description
min_cpu	String	Minimal CPU specifications
min_gpu	String	Minimal GPU specifications
min_memory	String	Minimum memory
min_ascend	String	Minimal Ascend specifications

**Table 9** ModelParamsInfo
Parameter	Type	Description
protocol	String	Request protocol, for example, http
url	String	API URL
min	Number	Minimum value of a numeric parameter
method	String	Request method, for example, post
max	Number	Maximum value of a numeric parameter
param_desc	String	Parameters
param_name	String	Parameter
param_type	String	Type

Example Requests

GET https://{endpoint}/v1/{project_id}/models/{model_id}

Example Responses

Status code: 200

Model details

{
  "model_id" : "10eb0091-887f-4839-9929-cbc884f1e20e",
  "model_name" : "mnist",
  "model_version" : "1.0.0",
  "runtime" : "python2.7",
  "tenant" : "6d28e85aa78b4e1a9b4bd83501bcd4a1",
  "project" : "d04c10db1f264cfeb1966deff1a3527c",
  "owner" : "6d28e85aa78b4e1a9b4bd83501bcd4a1",
  "source_location" : "https://models.obs.xxxxx.com/mnist",
  "model_type" : "TensorFlow",
  "model_size" : 5633481,
  "model_status" : "published",
  "execution_code" : "https://testmodel.obs.xxxxx.com/customize_service.py",
  "image_address" : "100.125.5.235:20202/models/10eb0091-887f-4839-9929-cbc884f1e20e:1.0.0",
  "input_params" : [ {
    "url" : "/",
    "method" : "post",
    "protocol" : "http",
    "param_name" : "data",
    "param_type" : "object",
    "param_desc" : "{\"type\":\"object\",\"properties\":{\"req_data\":{\"items\":[{\"type\":\"object\",\"properties\":{}}],\"type\":\"array\"}}}"
  } ],
  "output_params" : [ {
    "url" : "/",
    "method" : "post",
    "protocol" : "http",
    "param_name" : "data",
    "param_type" : "object",
    "param_desc" : "{\"type\":\"object\",\"properties\":{\"resp_data\":{\"type\":\"array\",\"items\":[{\"type\":\"object\",\"properties\":{}}]}}}"
  } ],
  "dependencies" : [ {
    "installer" : "pip",
    "packages" : [ {
      "package_name" : "pkg1",
      "package_version" : "1.0.1",
      "restraint" : "ATLEAST"
    } ]
  } ],
  "model_metrics" : "{\"f1\":0.52381,\"recall\":0.666667,\"precision\":0.466667,\"accuracy\":0.625}",
  "apis" : "[{\"protocol\":\"http\",\"method\":\"post\",\"url\":\"/\",\"input_params\":{\"type\":\"object\",\"properties\":{\"data\":{\"type\":\"object\",\"properties\":{\"req_data\":{\"items\":[{\"type\":\"object\",\"properties\":{}}],\"type\":\"array\"}}}}},\"output_params\":{\"type\":\"object\",\"properties\":{\"data\":{\"type\":\"object\",\"properties\":{\"resp_data\":{\"type\":\"array\",\"items\":[{\"type\":\"object\",\"properties\":{}}]}}}}}}]",
  "model_labels" : [ ],
  "labels_map" : {
    "labels" : [ ]
  },
  "workspace_id" : "0",
  "install_type" : [ "realtime", "batch", "edge" ],
  "specification" : { },
  "config" : "{\"model_algorithm\":\"image_classification\",\"model_source\":\"auto\",\"tunable\":false,\"downloadable_flag\":true,\"algorithm\":\"resnet_v2_50,mobilenet_v1\",\"metrics\":{\"f1\":0.912078373015873,\"recall\":0.9125,\"precision\":0.9340277777777778,\"accuracy\":0.263250724969475},\"model_type\":\"TensorFlow\",\"runtime\":\"tf1.13-python3.6-cpu\",\"apis\":[{\"protocol\":\"https\",\"url\":\"/\",\"method\":\"post\",\"request\":{\"data\":{\"type\":\"object\",\"properties\":{\"images\":{\"type\":\"file\"}}},\"Content-type\":\"multipart/form-data\"},\"response\":{\"data\":{\"type\":\"object\",\"required\":[\"predicted_label\",\"scores\"],\"properties\":{\"predicted_label\":{\"type\":\"string\"},\"scores\":{\"type\":\"array\",\"items\":{\"type\":\"array\",\"minItems\":2,\"maxItems\":2,\"items\":[{\"type\":\"string\"},{\"type\":\"number\"}]}}}},\"Content-type\":\"multipart/form-data\"}}],\"dependencies\":[{\"installer\":\"pip\",\"packages\":[{\"package_name\":\"numpy\",\"package_version\":\"1.17.0\",\"restraint\":\"EXACT\"},{\"package_name\":\"h5py\",\"package_version\":\"2.8.0\",\"restraint\":\"EXACT\"},{\"package_name\":\"Pillow\",\"package_version\":\"5.2.0\",\"restraint\":\"EXACT\"},{\"package_name\":\"scipy\",\"package_version\":\"1.2.1\",\"restraint\":\"EXACT\"},{\"package_name\":\"resampy\",\"package_version\":\"0.2.1\",\"restraint\":\"EXACT\"},{\"package_name\":\"scikit-learn\",\"package_version\":\"0.19.1\",\"restraint\":\"EXACT\"}]}]}"
}