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Creating a Model Registration Phase

Updated on 2024-10-29 GMT+08:00

Description

This phase integrates capabilities of ModelArts AI application management. This enables trained models to be registered in AI Application Management for service deployment and update. The application scenarios are as follows:

  • Registering models trained from ModelArts training jobs
  • Registering models from custom images

Parameter Overview

You can use ModelStep to create a model registration phase. The following is an example of defining a ModelStep.

Table 1 ModelStep

Parameter

Description

Mandatory

Data Type

name

Name of a model registration phase. The name contains a maximum of 64 characters, including only letters, digits, underscores (_), and hyphens (-). It must start with a letter and must be unique in a workflow.

Yes

str

inputs

Inputs of the model registration phase.

No

ModelInput or ModelInput list

outputs

Outputs of the model registration phase.

Yes

ModelOutput or ModelOutput list

title

Title for frontend display.

No

str

description

Description of the model registration phase.

No

str

policy

Phase execution policy.

No

StepPolicy

depend_steps

Dependent phases.

No

Step or step list

Table 2 ModelInput

Parameter

Description

Mandatory

Data Type

name

Input name of the model registration phase. The name can contain a maximum of 64 characters, including only letters, digits, underscores (_), and hyphens (-), and must start with a letter. The input name of a step must be unique.

Yes

str

data

Input data object of the model registration phase.

Yes

OBS, SWR, or subscribed model object. Currently, only OBSPath, SWRImage, OBSConsumption, OBSPlaceholder, SWRImagePlaceholder, DataConsumptionSelector, and GalleryModel are supported.

Table 3 ModelOutput

Parameter

Description

Mandatory

Data Type

name

Output name of the model registration phase. The name can contain a maximum of 64 characters, including only letters, digits, underscores (_), and hyphens (-), and must start with a letter. The output name of a step must be unique.

Yes

str

model_config

Configurations for model registration.

Yes

ModelConfig

Table 4 ModelConfig

Parameter

Description

Mandatory

Data Type

model_type

Model type. Supported types: TensorFlow, MXNet, Caffe, Spark_MLlib, Scikit_Learn, XGBoost, Image, PyTorch, Template, and Custom. The default value is TensorFlow.

Yes

str

model_name

Model name. Enter 1 to 64 characters. Only letters, digits, hyphens (-), and underscores (_) are allowed.

No

str, Placeholder

model_version

Model version in the format of Digit.Digit.Digit. The value range of the digits is [1, 99]. If this parameter is left blank, the version number automatically increases.

CAUTION:

No part of the version number can start with 0. For example, 01.01.01 is not allowed.

No

str, Placeholder

runtime

Model runtime environment. The options of runtime are the same as those of model_type.

No

str, Placeholder

description

Model description that consists of 1 to 100 characters. The following special characters cannot be contained: &!'"<>=

No

str

execution_code

OBS path for storing the execution code. By default, this parameter is left blank. The name of the execution code file is fixed to customize_service.py.

The inference code file must be stored in the model directory. This parameter is left blank. The system can automatically identify the inference code in the model directory.

No

str

dependencies

Package required for the inference code and model. By default, this parameter is left blank. It is read from the configuration file.

No

str

model_metrics

Model precision, which is read from the configuration file.

No

str

apis

All apis input and output parameters of a model (optional), which are parsed from the configuration file.

No

str

initial_config

Model configuration information.

No

dict

template

Template configuration items. This parameter is mandatory when model_type is set to Template.

No

Template

dynamic_load_mode

Dynamic loading mode. Currently, only Single is supported.

No

str, Placeholder

prebuild

Whether the model is prebuilt. The default value is False.

No

bool, Placeholder

install_type

Model installation type. The value can be real_time, edge, batch. If this parameter is left blank, all types are supported by default.

No

list[str]

Table 5 Template

Parameter

Description

Mandatory

Data Type

template_id

ID of the used template. The template has a built-in input and output mode.

Yes

str, Placeholder

infer_format

Input and output mode. When this parameter is used, the input and output mode built in the template does not take effect.

No

str, Placeholder

template_inputs

Template input configuration, specifying the source path for configuring a model

Yes

list of TemplateInputs object

Table 6 TemplateInputs

Parameter

Description

Mandatory

Data Type

input_id

Input item ID, which is obtained from the template details.

Yes

str, Placeholder

input

Template input path, which can be an OBS file path or OBS directory path. When you use a template with multiple input items to create a model, if the target paths input_properties specified in the template are the same, the OBS directory or OBS file name entered here must be unique to prevent files from being overwritten.

Yes

str, Placeholder, Storage

Examples

There are six scenarios:

  • Registering models output by JobStep
  • Registering a model using OBS data
  • Registering a model using a template
  • Registering a model using a custom image
  • Registering a model using a custom image and OBS
  • Registering a model using a subscribed model and OBS

Registering a Model from a Training Job (Model Source: JobStep Output)

import modelarts.workflow as wf

# Create an OutputStorage object to centrally manage training output directories.
storage = wf.data.OutputStorage(name="storage_name", title="title_info", description="description_info") # Only name is mandatory.

# Define an input dataset.
dataset = wf.data.DatasetPlaceholder(name="input_dataset")

# Use JobStep to define a training phase. Use a dataset as the input, and use OBS to store the output.
job_step = wf.steps.JobStep(
    name="training_job", # Name of a training phase. The name contains a maximum of 64 characters, including only letters, digits, underscores (_), and hyphens (-). It must start with a letter and must be unique in a workflow.
    title="Image Classification Training", # Title, which defaults to the value of name
    algorithm=wf.AIGalleryAlgorithm(
        subscription_id="subscription_id", # Algorithm subscription ID. You can also enter the version number.
        item_version_id="item_version_id", # Algorithm version ID. You can also enter the version number instead.
        parameters=[
            wf.AlgorithmParameters(
                name="parameter_name", 
                value=wf.Placeholder(name="parameter_name", placeholder_type=wf.PlaceholderType.STR, default="fake_value",description="description_info")
            ) # Algorithm hyperparameters are represented using placeholders, which can be integer, bool, float, or string.
        ]
    ), # Algorithm used for training. An algorithm subscribed to in AI Gallery is used in this example. If the value of an algorithm hyperparameter does not need to be changed, you do not need to configure the hyperparameter in parameters. Hyperparameter values will be automatically filled.
    
    inputs=wf.steps.JobInput(name="data_url", data=dataset), # JobStep inputs are configured when the workflow is running. You can also use wf.data.Dataset(dataset_name="fake_dataset_name", version_name="fake_version_name") for the data field.
    outputs=wf.steps.JobOutput(name="train_url", obs_config=wf.data.OBSOutputConfig(obs_path=storage.join("directory_path"))), # JobStep outputs
    spec=wf.steps.JobSpec(
        resource=wf.steps.JobResource(
            flavor=wf.Placeholder(name="train_flavor", placeholder_type=wf.PlaceholderType.JSON, description="Training flavor")
           
        )
    )# Training flavors
)

# Define a model registration phase using ModelStep. The output of JobStep is used as the input of ModelStep.

# Define model name parameters.
model_name = wf.Placeholder(name="placeholder_name", placeholder_type=wf.PlaceholderType.STR)

model_registration = wf.steps.ModelStep(
    name="model_registration", # Name of the model registration phase. The name contains a maximum of 64 characters, including only letters, digits, underscores (_), and hyphens (-). It must start with a letter and must be unique in a workflow.
    title="Model Registration",  # Title
    inputs=wf.steps.ModelInput(name='model_input', data=job_step.outputs["train_url"].as_input()),  # The output of JobStep is used as the input of ModelStep.
    outputs=wf.steps.ModelOutput(name='model_output',model_config=wf.steps.ModelConfig(model_name=model_name, model_type="TensorFlow")), # ModelStep outputs
    depend_steps=job_step # Preceding job phase
)
# job_step is an instance object of wf.steps.JobStep and train_url is the value of the name field of wf.steps.JobOutput.

workflow = wf.Workflow(
    name="model-step-demo",
    desc="this is a demo workflow",
    steps=[job_step, model_registration],
    storages=[storage]
)

Registering a Model from a Training Job (Model Source: A Trained Model Stored in OBS)

import modelarts.workflow as wf
# Define a model registration phase using ModelStep. The input is from OBS.

# Define the OBS data.
obs = wf.data.OBSPlaceholder(name = "obs_placeholder_name", object_type = "directory" ) # object_type must be file or directory.

# Define model name parameters.
model_name = wf.Placeholder(name="placeholder_name", placeholder_type=wf.PlaceholderType.STR)

model_registration = wf.steps.ModelStep(
    name="model_registration", # Name of the model registration phase. The name contains a maximum of 64 characters, including only letters, digits, underscores (_), and hyphens (-). It must start with a letter and must be unique in a workflow.
    title="Model Registration",  # Title
    inputs=wf.steps.ModelInput(name='model_input', data=obs),  # ModelStep inputs are configured when the workflow is running. You can also use wf.data.OBSPath(obs_path="fake_obs_path") for the data field.
    outputs=wf.steps.ModelOutput(name='model_output',model_config=wf.steps.ModelConfig(model_name=model_name, model_type="TensorFlow"))# ModelStep outputs
)

workflow = wf.Workflow(
    name="model-step-demo",
    desc="this is a demo workflow",
    steps=[model_registration]
)

Registering a Model Using a Template

import modelarts.workflow as wf
# Define a model registration phase using ModelStep. Register a model using a preset template.

# Define a preset template object. Fields in the template object can be represented by placeholders.
template = wf.steps.Template(
    template_id="fake_template_id",
    infer_format="fake_infer_format",
    template_inputs=[
        wf.steps.TemplateInputs(
            input_id="fake_input_id",
            input="fake_input_file"
        )
    ]
)

# Define model name parameters.
model_name = wf.Placeholder(name="placeholder_name", placeholder_type=wf.PlaceholderType.STR)

model_registration = wf.steps.ModelStep(
    name="model_registration", # Name of the model registration phase. The name contains a maximum of 64 characters, including only letters, digits, underscores (_), and hyphens (-). It must start with a letter and must be unique in a workflow.
    title="Model Registration",  # Title
    outputs=wf.steps.ModelOutput(
          name='model_output',
          model_config=wf.steps.ModelConfig(
              model_name=model_name, 
              model_type="Template",
              template=template
          )
    ) # ModelStep outputs
)

workflow = wf.Workflow(
    name="model-step-demo",
    desc="this is a demo workflow",
    steps=[model_registration]
)

Registering a Model from a Custom Image

import modelarts.workflow as wf
# Define a model registration phase using ModelStep. The input is from the URL of a custom image.

# Define the image data.
swr = wf.data.SWRImagePlaceholder(name="placeholder_name")

# Define model name parameters.
model_name = wf.Placeholder(name="placeholder_name", placeholder_type=wf.PlaceholderType.STR)

model_registration = wf.steps.ModelStep(
    name="model_registration", # Name of the model registration phase. The name contains a maximum of 64 characters, including only letters, digits, underscores (_), and hyphens (-). It must start with a letter and must be unique in a workflow.
    title="Model Registration",  # Title
    inputs=wf.steps.ModelInput(name="input",data=swr), # ModelStep inputs are configured when the workflow is running. You can also use wf.data.SWRImage(swr_path="fake_path") for the data field.
    outputs=wf.steps.ModelOutput(name='model_output',model_config=wf.steps.ModelConfig(model_name=model_name, model_type="TensorFlow"))# ModelStep outputs
)

workflow = wf.Workflow(
    name="model-step-demo",
    desc="this is a demo workflow",
    steps=[model_registration]
)

Registering a Model Using a Custom Image and OBS

import modelarts.workflow as wf
# Define a model registration phase using ModelStep. The input is from the URL of a custom image.

# Define the image data.
swr = wf.data.SWRImagePlaceholder(name="placeholder_name")

# Define OBS model data.
model_obs = wf.data.OBSPlaceholder(name = "obs_placeholder_name", object_type = "directory" ) # object_type must be file or directory.

# Define model name parameters.
model_name = wf.Placeholder(name="placeholder_name", placeholder_type=wf.PlaceholderType.STR)

model_registration = wf.steps.ModelStep(
    name="model_registration", # Name of the model registration phase. The name contains a maximum of 64 characters, including only letters, digits, underscores (_), and hyphens (-). It must start with a letter and must be unique in a workflow.
    title="Model Registration",  # Title
    inputs=[
        wf.steps.ModelInput(name="input",data=swr), # ModelStep inputs are configured when the workflow is running. You can also use wf.data.SWRImage(swr_path="fake_path") for the data field.
        wf.steps.ModelInput(name="input",data=model_obs) # ModelStep inputs are configured when the workflow is running. You can also use wf.data.OBSPath(obs_path="fake_obs_path") for the data field.
    ], 
    outputs=wf.steps.ModelOutput(
        name='model_output',
        model_config=wf.steps.ModelConfig(
            model_name=model_name, 
            model_type="Custom",
            dynamic_load_mode="Single"
        )
    ) # ModelStep outputs
)

workflow = wf.Workflow(
    name="model-step-demo",
    desc="this is a demo orkflow",
    steps=[model_registration]
)

Registering a Model Using a Subscribed Model and OBS

This mode is similar to the custom image + OBS mode, except that you obtain a custom image from a subscribed model.

Example:

import modelarts.workflow as wf

# Define the subscribed model object.
base_model = wf.data.GalleryModel(subscription_id="fake_subscription_id", version_num="fake_version") # Model subscribed to from AI Gallery, generally published by a developer

# Define OBS model data.
model_obs = wf.data.OBSPlaceholder(name = "obs_placeholder_name", object_type = "directory" ) # object_type must be file or directory.

# Define model name parameters.
model_name = wf.Placeholder(name="placeholder_name", placeholder_type=wf.PlaceholderType.STR)

model_registration = wf.steps.ModelStep(
    name="model_registration", # Name of the model registration phase. The name contains a maximum of 64 characters, including only letters, digits, underscores (_), and hyphens (-). It must start with a letter and must be unique in a workflow.
    title="Model Registration",  # Title
    inputs=[
        wf.steps.ModelInput(name="input",data=base_model) # Use a subscribed model as the ModelStep input.
        wf.steps.ModelInput(name="input",data=model_obs) # ModelStep inputs are configured when the workflow is running. You can also use wf.data.OBSPath(obs_path="fake_obs_path") for the data field.
    ], 
    outputs=wf.steps.ModelOutput(
        name='model_output',
        model_config=wf.steps.ModelConfig(
            model_name=model_name, 
            model_type="Custom",
            dynamic_load_mode="Single"
        )
    ) # ModelStep outputs
)

workflow = wf.Workflow(
    name="model-step-demo",
    desc="this is a demo workflow",
    steps=[model_registration]
)

In the preceding example, the system automatically obtains the custom image from the subscribed model and registers and generates a model based on the entered OBS model path. model_obs can be replaced with the dynamic output of JobStep.

NOTE:

The value of model_type can be TensorFlow, MXNet, Caffe, Spark_MLlib, Scikit_Learn, XGBoost, Image, PyTorch, Template, or Custom.

If model_type is not set for wf.steps.ModelConfig, TensorFlow is used by default.

  • If the model type of your workflow does not need to be changed, refer to the preceding examples.
  • If the model type of your workflow needs to be changed in multiple executions, write the parameter using placeholders.

    model_type = wf.Placeholder(name="placeholder_name", placeholder_type=wf.PlaceholderType.ENUM, default="TensorFlow", enum_list=["TensorFlow", "MXNet", "Caffe", "Spark_MLlib", "Scikit_Learn", "XGBoost", "Image", "PyTorch", "Template", "Custom"], description="Model type")

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