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Azure Machine Learning Designer SDK

Project description

Azure Machine Learning Designer Python SDK

The mldesigner package provide the SDK interface which work along with Azure ML Designer (drag-n-drop ML) UI experience.

Especially, the package ease the authoring experience of resources like Components & Pipelines:

  • Components: self-contained piece of code that does one step in a machine learning pipeline: data preprocessing, model training, model scoring, a hyperparameter tuning run, etc. Such that it can be parameterized and then used in different contexts.
  • Pipelines: independently executable workflow of machine learning tasks composed by Components.

Change Log

v0.1.0b18 (2024.01.31)

Recommended to work with azure-ai-ml==1.13

New Features

  • mldesigner compile supports to compile parallel component.
  • mldesigner generate supports to generate package with provided authentication.

v0.1.0b17 (2023.11.14)

Recommended to work with azure-ai-ml==1.12


  • [Compile] Update compiled command component schema path to

Fixed Bugs

  • [Compile] Fixed the error when source component inputs is empty.

v0.1.0b16 (2023.10.18)

Recommended to work with azure-ai-ml==1.11

New Features

  • mldesigner compile supports to compile with .amlignore:
    • mldesigner compile --source xxx.yaml --amlignore-file <path-to>/.amlignore

v0.1.0b15 (2023.09.13)

Recommended to work with azure-ai-ml==1.10

New Features

  • mldesigner compile support v1.5 spark components.
  • is_control=True is not required for primitive type output.
  • generate package support specify version for registry component and generate by sdk support remote url.
    from mldesigner import generate

v0.1.0b14 (2023.07.31)

Recommended to work with azure-ai-ml==1.9.0

New Features

  • Support primitive type as component return annotation and not need to write is_control=True.
  • Support Annotated type as component input or component return annotation.
  • Support Annotated type in @group.
    from mldesigner import command_component, Output, Meta
    from typing_extensions import Annotated
    def my_component1(input_int: int) -> int:
        return input_int
    def my_component2(input_int: Annotated[int, Meta(description="test annotation int", min=1, max=10, default=1)],) \
                                                         -> Annotated[int, Meta(description="test annotation int")]:  # using Meta class to configure more meta data
        return input_int

v0.1.0b13 (2023.05.16)

Recommended to work with azure-ai-ml==1.7.0


  • Support to generate components with specified version or label:
    • azureml://subscriptions//resourcegroups/<resource_group>/workspaces//components/<component_name>/labels/
    • azureml://subscriptions//resourcegroups/<resource_group>/workspaces//components/<component_name>/versions/
  • Refine the log content of generate component package.

v0.1.0b12 (2023.03.28)

Recommended to work with azure-ai-ml==1.5.0


  • Support multiple types of identity for dynamic pipeline creation, the priority is user identity > managed identity > others.
  • Refine mldesigner execute:
    • When component name is not specified, if there is only one component in the source file, execute this component, raise error when there are multiple components.
    • If required parameters are not provided, list all of them and raise error.
  • Improve support for enum values in generate_package.
    • Support enum values with no \w characters, e.g. empty string, -, + and \t.
    • Support enum values with duplicate sanitized names, e.g. A and a.
  • Use curated environment as default mldesigner component environment:
    • For command component: azureml://registries/azureml/environments/mldesigner-minimal/labels/latest
    • For dynamic pipeline: azureml://registries/azureml/environments/mldesigner/labels/latest
  • Support mltable type in @dynamic outputs

Fixed Bugs

  • Fix wrong command error when mldesigner component code is set to be a file instead of a folder.

v0.1.0b11 (2023.02.09)

Recommended to work with azure-ai-ml==1.4.0


  • Add python version requirements <4.0,>=3.7

v0.1.0b10 (2023.01.20)

Recommended to work with azure-ai-ml==1.3.0

New Features

  • Support primitive type as component return annotation.
  • Support @group as component return annotation.
  • Add validation for component execution results according to return annotation.


  • Return component execution result as mldesigner execute result.

Fixed Bugs

  • Fixed missing keyword arguments when executing component in an environment that has both mldesigner and azure-ai-ml
  • Fixed compile error when compiling pipeline that has a node uses remote registered component either from workspace or registry.
  • Fixed parse error when input string is a json serialized string.

v0.1.0b9 (2022.12.06)

Recommended to work with azure-ai-ml==1.2.0


  • Enable creating component with auto-incremented version if no version specified.
  • Update mldesigner default environment image to be
  • Mldesigner compile improvements:
    • Exits with code 1 when there is failed compilation.
    • Output yaml file with original file name if the input is a yaml.
    • Generated pipeline node keys is ordered.

Fixed Bugs

  • Fixed mldesigner executor does not pass **kwargs to component func

v0.1.0b8 (2022.10.26)

Fixed Bugs

  • Fixed dependency issue when importing typing_extensions

v0.1.0b7 (2022.10.25)

New Features

  • Support mldesigner compile: mldesigner compile
  • Support list type component input for generate_package.

Fixed Bugs

  • Fixed execute error when input string has space or "=" inside.
  • Fixed execute error for bool parameter parsing.


  • Remove mode client default value ro_mount/rw_mount from mldesigner Input/Output class.
  • Refine error message when defining a component in .ipynb.
  • Refine error message when failed to create component entity.

v0.1.0b6 (2022.09.19)


  • Adopted optional input new format: '$[[]]' instead of old '[]'.
  • Support Enum for mldesigner input.
  • Raise error if input has no annotation specified.

Fixed Bugs:

  • Fixed import error when used along with azure-ai-ml<0.1.0b7.

v0.1.0b5 (2022.09.08)

New Features

  • Enable using @command_component without brackets when no additional parameters.
    from mldesigner import command_component
    def my_component():
    # equals to
    def my_component():

Fixed Bugs:

  • Fixed component command execution error when no inputs or outputs specified.
  • Fixed incompatible issue with azure-ai-ml >=0.1.0b7: error when trying to import azure-ai-ml constants

v0.1.0b4 (2022.08.22)

New Features:

  • Support mldesigner generate:
  • Support mldesigner execute: execute component in local host environment.
    • CLI example: mldesigner execute --source ./ --name my_component inputs a=1 b=2
    • SDK example:
      from mldesigner import execute
      from components import my_component
      node = my_component(a=1, b=2)
      res = execute(node)
    • More information: mldesigner execute --help


  • Fix bump version config.

v0.1.0b3 (2022.07.14)

New Features:

  • Support optional input for mldesigner Input class.
    from mldesigner import command_component, Input
    def my_component(optional_param: Input(type="integer", optional=True)):
  • Support io descriptions inferring from docstring for pipeline & component.


  • Handle compatibility issue for future changes. Less imports of private functions
  • Remove default property for mldesigner Input class.
  • Enable mldesigner to use argparser to parse incoming args.
  • Compatibility handling: old azure ai ml package use different way to load component.
  • Component input like "int_param=3", no longer to be processed as optional input when registering to remote.

v0.1.0b2 (2022.05.23)


  • Refine code terminologies, replace old dsl with new mldesigner.

v0.1.0b1 (2022.05.20)

New Features:

  • Support using decorator @command_component to define a component.
    • Create a component:
      from mldesigner import command_component, Input, Output
      def hello_world(input: Input, output: Output, param='str_param'):
          print("Hello World!")
    • Register the component to server:
      from import MLClient
      client = MLClient.from_config(credential=credential)
    • Use sdk component in pipeline:
      from import dsl
      def my_pipeline():
          node = hello_world()
          return {"pipeline_output": node.outputs.output}
      pipeline = my_pipeline()

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