dsl 套件

  • 參考

函數

pipeline

建置管線，其中包含此函式中定義的所有元件節點。

pipeline(func: Callable[[P], T] | None = None, *, name: str | None = None, version: str | None = None, display_name: str | None = None, description: str | None = None, experiment_name: str | None = None, tags: Dict[str, str] | None = None, **kwargs) -> Callable[[Callable[[P], T]], Callable[[P], PipelineJob]] | Callable[[P], PipelineJob]

參數

func
FunctionType
預設值: None

要裝飾的使用者管線函式。

name
str

管線元件的名稱預設為函式名稱。

version
str

管線元件的版本預設為 「1」。

display_name
str

管線元件的顯示名稱預設為函式名稱。

description
str

建置管線的描述。

experiment_name
str

如果提供 [無]，則會在底下建立作業的實驗名稱，實驗將會設定為目前的目錄。

tags
dict[str, str]

管線元件的標記。

kwargs
dict

其他組態參數的字典。

傳回

或

  • 裝飾專案，如果 func 是 None
  • 裝飾 的 func

傳回類型

Union[ Callable[[Callable], Callable[…, ~PipelineJob]], Callable[<xref:P>, ~PipelineJob]]

範例

示範如何使用這個裝飾專案建立管線。


   from azure.ai.ml import load_component
   from azure.ai.ml.dsl import pipeline

   component_func = load_component(
       source="./sdk/ml/azure-ai-ml/tests/test_configs/components/helloworld_component.yml"
   )

   # Define a pipeline with decorator
   @pipeline(name="sample_pipeline", description="pipeline description")
   def sample_pipeline_func(pipeline_input1, pipeline_input2):
       # component1 and component2 will be added into the current pipeline
       component1 = component_func(component_in_number=pipeline_input1, component_in_path=uri_file_input)
       component2 = component_func(component_in_number=pipeline_input2, component_in_path=uri_file_input)
       # A decorated pipeline function needs to return outputs.
       # In this case, the pipeline has two outputs: component1's output1 and component2's output1,
       # and let's rename them to 'pipeline_output1' and 'pipeline_output2'
       return {
           "pipeline_output1": component1.outputs.component_out_path,
           "pipeline_output2": component2.outputs.component_out_path,
       }

   # E.g.: This call returns a pipeline job with nodes=[component1, component2],
   pipeline_job = sample_pipeline_func(
       pipeline_input1=1.0,
       pipeline_input2=2.0,
   )
   ml_client.jobs.create_or_update(pipeline_job, experiment_name="pipeline_samples", compute="cpu-cluster")