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Execution Overview

This section explains how to execute a function in the ModelServe runtime. First, we examine the usage pattern, then delve into the parameter structure.

Usage Pattern

To execute a ModelServe function, follow this pattern:

  1. Use dh.new_function() or project.new_function() to create the function, passing function parameters.
  2. Call function.run() with the desired action, passing task parameters and run parameters.
# Create function with function parameters
function = dh.new_function(
    name="my-model-function",
    kind="mlflowserve",
    path="s3://my-bucket/path-to-model"
)

# Execute with task and run parameters
run = function.run(
    action="serve",  # Task parameter
    replicas=1  # Task parameter
)

ModelServe functions are executed remotely on Kubernetes clusters managed by the platform.

Parameter Structure

Parameters are organized into three categories:

  • Function Parameters: Define the function's spec attributes, such as model path, image, and execution environment. These are set when creating the function.

  • Task Parameters: Specify the action type and execution environment configuration. For ModelServe runtimes, the action is serve.

  • Run Parameters: Control runtime behavior, such as environment variables and scaling parameters.

Task Actions

The ModelServe runtime supports one task action:

  • serve: Deploy a model as a service

Detailed Documentation

For comprehensive details on each parameter category: