Examples

There are three scenarios:

• Deploying a real-time service
• Modifying a real-time service
• Getting the inference address from the service deployment phase

import modelarts.workflow as wf
# Use ServiceStep to define a service deployment phase and specify a model for service deployment.

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

service_step = wf.steps.ServiceStep(
    name="service_step", # Name of the service deployment 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="Deploying a service",  # Title
    inputs=wf.steps.ServiceInput(name="si_service_ph", data=wf.data.ServiceInputPlaceholder(name="si_placeholder1", 
                                                                                  # Restrictions on the model name: Only the model name specified here can be used in the running state; use the same model name as model_name of the model registration phase.
                                                                                  model_name=model_name)),# ServiceStep inputs
    outputs=wf.steps.ServiceOutput(name="service_output") # ServiceStep outputs
)

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

import modelarts.workflow as wf
# Use ServiceStep to define a service deployment phase and specify a model for service update.

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

# Define a service object.
service = wf.data.ServiceUpdatePlaceholder(name="placeholder_name")

service_step  = wf.steps.ServiceStep(
    name="service_step", # Name of the service deployment 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="Service update", # Title
    inputs=[wf.steps.ServiceInput(name="si2", data=wf.data.ServiceInputPlaceholder(name="si_placeholder2", 
                                                                                  # Restrictions on the model name: Only the model name specified here can be used in the running state.
                                                                                  model_name=model_name)),
           wf.steps.ServiceInput(name="si_service_data", data=service) # ServiceStep inputs are configured when the workflow is running. You can also use wf.data.ServiceData(service_id="fake_service") for the data field.
    ], # ServiceStep inputs
    outputs=wf.steps.ServiceOutput(name="service_output") # ServiceStep outputs
)

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

• For services deployed in the public resource pool, you can use access_address to obtain the inference address registered on the public network from the output.
• For services deployed in a dedicated resource pool, you can get the internal inference address from the output using cluster_inner_access_address, in addition to the public inference address. The internal address can only be accessed by other inference services.

  import modelarts.workflow as wf
  
  # Define model name parameters.
  sub_model_name = wf.Placeholder(name="si_placeholder1", placeholder_type=wf.PlaceholderType.STR)
  
  sub_service_step = wf.steps.ServiceStep(
      name="sub_service_step", # Name of the service deployment 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="Subservice",  # Title
      inputs=wf.steps.ServiceInput(
          name="si_service_ph",
          data=wf.data.ServiceInputPlaceholder(name="si_placeholder1", model_name=sub_model_name)
      ),# ServiceStep inputs
      outputs=wf.steps.ServiceOutput(name="service_output") # ServiceStep outputs
  )
  
  
  main_model_name = wf.Placeholder(name="si_placeholder2", placeholder_type=wf.PlaceholderType.STR)
  
  # Obtain the inference address output by the subservice and transfer the address to the main service through envs.
  main_service_config = wf.steps.ServiceConfig(
      infer_type="real-time", 
      envs={"infer_address": sub_service_step.outputs["service_output"].get_output_variable("access_address")} # Obtain the inference address output by the subservice and transfer the address to the main service through envs.
  )
  
  main_service_step = wf.steps.ServiceStep(
      name="main_service_step", # Name of the service deployment 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="Main service",  # Title
      inputs=wf.steps.ServiceInput(
          name="si_service_ph",
          data=wf.data.ServiceInputPlaceholder(name="si_placeholder2", model_name=main_model_name)
      ),# ServiceStep inputs
      outputs=wf.steps.ServiceOutput(name="service_output", service_config=main_service_config), # ServiceStep outputs
      depend_steps=sub_service_step
  )
  
  workflow = wf.Workflow(
      name="service-step-demo",
      desc="this is a demo workflow",
      steps=[sub_service_step, main_service_step]
  )