Hi @<1603198163143888896:profile|LonelyKangaroo55> , I don't believe this is supported.
@PipelineDecorator.pipeline(
name="Sub Pipeline",
project="Pipelines",
version="1.0",
multi_instance_support="parallel",
)
def sub_pipeline(parameter):
print(f"Running sub-pipeline with parameter={parameter}")
return parameter * 2
@PipelineDecorator.pipeline(
name="Main Pipeline",
project="Pipelines",
version="1.0",
)
def main_pipeline():
refs = []
for p in [1, 2, 3]:
ref = sub_pipeline(parameter=p)
refs.append(ref)
PipelineDecorator.wait_for_multi_pipelines()
# Aggregate results from all sub-pipelines