kfp

import kfp from kfp import dsl import os # Define a lightweight Python component @dsl.component def add(a: float, b: float) -> float: '''Calculates sum of two arguments''' return a + b # Define a pipeline using the component @dsl.pipeline( name='Addition pipeline', description='An example pipeline that performs addition calculations.' ) def add_pipeline( a: float = 1.0, b: float = 7.0, ): first_add_task = add(a=a, b=4.0) second_add_task = add(a=first_add_task.output, b=b) # --- Running the pipeline (requires KFP backend) --- # In a real environment, you'd configure the KFP client to connect to your KFP instance. # For local testing without a KFP backend, you can use `kfp.local.init`. # Example of compiling a pipeline (no KFP backend needed for this step) # compiler = kfp.compiler.Compiler() # compiler.compile(pipeline_func=add_pipeline, package_path='add_pipeline.yaml') # Example of running a pipeline against a KFP endpoint # client = kfp.Client(host=os.environ.get('KFP_HOST', 'http://localhost:8080')) # run = client.create_run_from_pipeline_func( # add_pipeline, # arguments={'a': 7.0, 'b': 8.0} # ) # print(f"Pipeline run initiated: {run.url}") print("Pipeline 'add_pipeline' defined successfully. To run, compile and submit to a KFP backend.")