dsl 套件
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
- 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。
- 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
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")