Importing a Model

Importing a model includes:

Initialize the existing model and create a model object based on the model ID.
Create a model. For details about the attributes of the created model, see Obtaining Details About a Model.

Sample Model File

The following uses PyTorch as an example to describe how to edit a model file. For details about the PyTorch model package structure, see Introduction to Model Package Specifications.

OBS bucket or directory name
├── resnet
│   ├── model Mandatory: Fixed subdirectory name. The subdirectory is used to store model-related files.
│   │  ├──<<Custom Python package>> (Optional) Custom Python package, which can be directly referenced in model inference code
│   │  ├──mnist_mlp.pt (Mandatory) PyTorch model file, which contains variable and weight information and is saved as state_dict
│   │  ├──config.json Mandatory: Model configuration file. The file name is fixed to config.json. Only one model configuration file is allowed.
│   │  ├──customize_service.py Mandatory: Model inference code. The file name is fixed to customize_service.py. Only one model inference file is allowed. The files on which customize_service.py depends can be directly stored in the model directory.

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.model import Model
from modelarts.config.model_config import ServiceConfig, Params, Dependencies, Packages

session = Session()

Method 1: Initialize an existing model.

      
           model_instance = Model(session, model_id="your_model_id")

Method 2: Create a model.

Use a preset image and specify an OBS path to create a model.

        
             model_location = "/your_obs_bucket/model_path"            # Change to the OBS path to the model file
execution_code = "/your_obs_bucket/model_path/customize_service.py"
runtime = "python3.7"

model_instance = Model(
                        session,
                        model_name="input_model_name",    #  (Optional) Model name
                        model_version="1.0.0",            # (Optional) Model version
                        source_location=model_location,   # OBS path to the model file, for example, /your_obs_bucket/model_path
                        model_type="PyTorch",             # Model type
                        execution_code=execution_code,    # (Optional) OBS path to the execution script, for example, /your_obs_bucket/model_path/customize_service.py
                        runtime = runtime                 # (Optional) Supported runtime environment
                       )

dependencies will overwrite the data in config.json in the preceding example. You do not need to use dependencies. The following section describes the dependencies formats.

Format of the dependencies parameter group

SDKs define the dependencies parameter group. dependencies is in list format, and those of the tuple objects in the list are Dependencies.

The code is as follows:

          
               dependencies = []
dependency1 = Dependencies(
                            installer="pip",                     # Installation mode. pip is supported.
      packages=packages                   # Collection of dependency packages. For details, see packages.
)
dependencies.append(dependency1)

Format of the package parameter group

SDKs define the packages parameter group. packages is in list format, and those of the tuple objects in the list are Packages.

The code is as follows:

          
               packages = []
package1 = Packages(
                      package_name="package_name",               # Package name
                      package_version="version",                 # Package version
     restraint="EXACT"
)  
packages.append(package1)

The following is an example of creating a dependencies parameter group:

dependencies = []
packages = [{
    "package_name": "numpy",
    "package_version": "1.15.0",
    "restraint": "EXACT"
    }, {
        "package_name": "h5py",
        "package_version": "2.8.0",
        "restraint": "EXACT"
    }]
dependency = Dependencies(installer="pip", packages=packages)
dependencies.append(dependency)

Use a custom image to create a model.

This method applies if the script of the inference service has been built in the custom image and the service is automatically started when the image is started.

from modelarts.session import Session
from modelarts.model import Model

session = Session()
image_path = "custom_image_path"
model_instance = Model(
                        session, 
                        model_name="your_model_name",               # Model name
                        model_version="0.1.0",                      # Model version
                        source_location=image_path,                 # Model file path
                        model_type="Image"                         # Model type
                      )

Parameters

**Table 1** Parameters for initializing a model
Parameter	Mandatory	Type	Description
session	Yes	Object	Session object. For details about the initialization method, see Session Authentication.
model_id	Yes	String	Model ID

**Table 2** Parameters for creating a model
Parameter	Mandatory	Type	Description
session	Yes	Object	Session object. For details about the initialization method, see Session Authentication.
model_name	No	String	Name of a model that consists of 1 to 64 characters and must start with a letter. Only letters, digits, underscores (_), and hyphens (-) are allowed. If this parameter is not specified, the system automatically generates a model name.
model_version	Yes	String	Model version in the format of Digit.Digit.Digit. The value range of the digits is [1, 99]. The version number cannot start with 0, for example, 01.01.01.
publish	No	Bool	Whether to publish a model. The options are as follows: True: Publish the model. (Default value) False: Do not publish the model. Create a local model, which can be used to debug related code.
source_location_type	No	String	Model location type. The options are as follows: OBS_SOURCE: OBS path. (Default value) LOCAL_SOURCE: local path.
source_location	Yes	String	Path (parent directory) of the model file If source_location_type is set to OBS_SOURCE, the model file path is an OBS path in the format of /obs_bucketname/.../model_file_parent_dir/. If source_location_type is set to LOCAL_SOURCE, the model file path is a local path in the format of /local_path/.../model_file_parent_dir/.
environment	No	Environment instance	Environment required for normal model running, such as the Python or TensorFlow version For details about the example environment, see Sample Code.
source_job_id	No	String	ID of the source training job. If the model is generated from a training job, specify this parameter for source tracing. If the model is imported from a third-party meta model, leave this parameter blank. By default, this parameter is left blank.
source_job_version	No	String	Version of the source training job. If the model is generated from a training job, specify this parameter for source tracing. If the model is imported from a third-party meta model, leave this parameter blank. By default, this parameter is left blank.
source_type	No	String	Model source type. The value can only be auto, which indicates an ExeML model (model download is not allowed). If the model is deployed via a training job, leave this parameter blank. By default, this parameter is left blank.
model_type	Yes	String	Model type. The value can be TensorFlow, MXNet, Spark_MLlib, Scikit_Learn, XGBoost, MindSpore, Image, or PyTorch.
model_algorithm	No	String	Model algorithm. If the algorithm has been configured in the model configuration file, this parameter can be left blank. For example, predict_analysis, object_detection, or image_classification.
description	No	String	Model description, which contains a maximum of 100 characters and cannot contain the following special characters: !<>=&'"
execution_code	No	String	OBS path to the script to be executed. If customize_service.py is not output by the model, configure this parameter to specify the path. The inference script must be stored in the model directory in the path where the model is located. For details, see the source_location parameter. The script name is fixed to customize_service.py.
runtime	No	String	Supported runtime environment. This parameter is mandatory if model_type is used. For details, see Supported AI engines and their runtime.
input_params	No	params array	List of input parameters for model inference. By default, this parameter is left blank. If the apis information has been configured in the model configuration file, you do not need to set this parameter. The backend automatically reads the input parameters from the apis field in the configuration file.
output_params	No	params array	List of output parameters for model inference. By default, this parameter is left blank. If the apis information has been configured in the model configuration file, you do not need to set this parameter. The backend automatically reads the output parameters from the apis field in the configuration file.
dependencies	No	dependency array	Dependency package required for running the code and model. By default, this parameter is left blank. If the dependencies information has been configured in the model configuration file, you do not need to set this parameter. The backend automatically reads the dependencies to be installed from the dependencies field in the configuration file.
apis	No	String	List of inference APIs provided by a model. By default, this parameter is left blank. If the apis information has been configured in the model configuration file, you do not need to set this parameter. The backend automatically reads the configured inference API information from the apis field in the configuration file.

**Table 3** **params** parameters
Parameter	Mandatory	Type	Description
url	Yes	String	Request path of a model inference API
param_name	Yes	String	Parameter name, which contains a maximum of 64 characters
param_type	Yes	String	Basic parameter types of JSON schema, including string, object, array, boolean, number, and integer
min	No	Double	This parameter is optional when param_type is set to int or float. By default, this parameter is left blank.
max	No	Double	This parameter is optional when param_type is set to int or float. By default, this parameter is left blank.
param_desc	No	String	Parameter description, which contains a maximum of 100 characters. By default, this parameter is left blank.

**Table 4** **dependency** parameters
Parameter	Mandatory	Type	Description
installer	Yes	String	Installation mode. Only pip is supported.
packages	Yes	package array	Collection of dependency packages

**Table 5** **package** parameters
Parameter	Mandatory	Type	Description
package_name	Yes	String	Name of a dependency package
package_version	No	String	Version of a dependency package
restraint	No	String	Version filtering condition. This parameter is mandatory only when package_version exists. Possible values are as follows: EXACT: the specified version ATLEAST: not earlier than the specified version ATMOST: not later than the specified version

**Table 6** **create_model** response parameters
Parameter	Mandatory	Type	Description
model_instance	Yes	Model object	Model object, which can be any of the APIs described in this chapter

Example of creating a model in a handwritten digit recognition project using MXNet:

       
            from modelarts.session import Session
from modelarts.model import Model

session = Session()
model_instance = Model(session,
                       model_name="digit_recognition",
                       model_version="1.0.0",
                       source_location=model_location,
                       model_type="MXNet",
                       model_algorithm="image_classification"
                       )