Can you please provide a stand alone snippet that reproduces this behavior? Can you provide a log of the run?
Hi, Can you please elaborate on what you mean and what is happening?
Hello. Please tell me how to make sure that when you start the pipeline, nothing superfluous is installed in the service queue?
@PipelineDecorator.pipeline(
project=CLEARML_CONFIG.pipeline.project_name,
name=CLEARML_CONFIG.pipeline.pipeline_name,
)
def executing_pipeline(dataset_id: str):
print(f"{dataset_id = }")
weights_path = train_step(dataset_id)
print(f"{weights_path = }")
evaluate_step(weights_path)
@PipelineDecorator.component(
execution_queue=CLEARML_CONFIG.queue,
docker=CLEARML_CONFIG.docker_image,
docker_args=CLEARML_CONFIG.docker_arguments,
return_values=["weights_path"],
cache=False,
task_type=TaskTypes.training,
repo="",
repo_branch=BRANCH,
)
def train_step(dataset_id: str):
...
return weights_path
@PipelineDecorator.component(
execution_queue=CLEARML_CONFIG.queue,
cache=False,
task_type=TaskTypes.testing,
docker=CLEARML_CONFIG.docker_image,
docker_args=CLEARML_CONFIG.docker_arguments,
parents=["train_step"],
repo="",
repo_branch=BRANCH,
)
def evaluate_step(weights_path):
...
if __name__ == "__main__":
PipelineDecorator.set_default_execution_queue("services")
executing_pipeline(dataset_id="...")
Can you please provide a stand alone snippet that reproduces this behavior? Can you provide a log of the run?
Hi, Can you please elaborate on what you mean and what is happening?