Importing 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 "Model Inference > Inference Specifications > Model Package Specifications > Introduction to Model Package Specifications" in ModelArts User Guide.

For details about PyTorch custom scripts, see "Model Inference > Inference Specifications > PyTorch" in ModelArts User Guide.

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.

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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.

  1	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.

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    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:

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      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:

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      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

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

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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" )