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:
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.Recommended to work with azure-ai-ml==1.12
Improvements
https://azuremlschemas.azureedge.net/latest/commandComponent.schema.json.Fixed Bugs
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>/.amlignoreRecommended 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
generate(
source="azureml://registries/{registry_name}/components/{component_name}/versions/0.0.8",
)
Recommended to work with azure-ai-ml==1.9.0
New Features
is_control=True.Annotated type as component input or component return annotation.Annotated type in @group.
from mldesigner import command_component, Output, Meta
from typing_extensions import Annotated
@command_component()
def my_component1(input_int: int) -> int:
return input_int
@command_component()
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
Recommended to work with azure-ai-ml==1.7.0
Improvements
Recommended to work with azure-ai-ml==1.5.0
Improvements
mldesigner execute:
enum values in generate_package.
\w characters, e.g. empty string, -, + and \t.A and a.curated environment as default mldesigner component environment:
azureml://registries/azureml/environments/mldesigner-minimal/labels/latestazureml://registries/azureml/environments/mldesigner/labels/latestmltable type in @dynamic outputsFixed Bugs
Recommended to work with azure-ai-ml==1.4.0
Improvements
<4.0,>=3.7Recommended to work with azure-ai-ml==1.3.0
New Features
@group as component return annotation.Improvements
mldesigner execute result.Fixed Bugs
Recommended to work with azure-ai-ml==1.2.0
Improvements
mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04Fixed Bugs
**kwargs to component funcFixed Bugs
typing_extensionsNew Features
mldesigner compileFixed Bugs
Improvements
mode client default value ro_mount/rw_mount from mldesigner Input/Output class..ipynb.Improvements:
Fixed Bugs:
New Features
from mldesigner import command_component
@command_component
def my_component():
pass
# equals to
@command_component()
def my_component():
pass
Fixed Bugs:
New Features:
mldesigner execute --source ./components.py --name my_component inputs a=1 b=2from mldesigner import execute
from components import my_component
node = my_component(a=1, b=2)
res = execute(node)
mldesigner execute --helpImprovements:
New Features:
from mldesigner import command_component, Input
@command_component()
def my_component(optional_param: Input(type="integer", optional=True)):
pass
Improvements:
Improvements:
New Features:
from mldesigner import command_component, Input, Output
@command_component()
def hello_world(input: Input, output: Output, param='str_param'):
print("Hello World!")
from azure.ai.ml import MLClient
client = MLClient.from_config(credential=credential)
client.components.create_or_update(hello_world)
from azure.ai.ml import dsl
@dsl.pipeline()
def my_pipeline():
node = hello_world()
return {"pipeline_output": node.outputs.output}
pipeline = my_pipeline()
client.jobs.create_or_update(pipeline)
FAQs
Azure Machine Learning Designer SDK
We found that mldesigner demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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