pipelines stuck on remote

[lolpa1n]

Hello,

I deployed clearml server on my machine and wanted to make pipelines:

my code:

from clearml import PipelineController

def read_data(csv_path):
    import pandas as pd
    import os

    df = pd.read_csv(csv_path)

    return df
    
def log_data(df):
    from clearml import Logger
    
    logger = Logger.current_logger()
    logger.report_table(title=f'Dataframe FULL', series='pandas DataFrame', iteration=1, table_plot=df)
    print("Logged data successfully")
    return True
    
if __name__ == '__main__':
    pipe = PipelineController(
        project='ASMBT', 
        name='test_pipe',
        version='1.0',
        add_pipeline_tags=True,
        # working_dir='.'
    )
    pipe.add_parameter(
        name='csv_path',
        description='path to csv file', 
        default='../data/data.csv'
    )
    pipe.add_function_step(
        name='read_data',
        function=read_data,
        function_kwargs=dict(csv_path='${pipeline.csv_path}'),
        function_return=['data_frame'],
        cache_executed_step=False,
        execution_queue='test_gpu'
    )
    pipe.add_function_step(
        name='log_data',
        function=log_data,
        function_kwargs=dict(df='${read_data.data_frame}'),
        cache_executed_step=False,
        execution_queue='test_gpu'
    )
    # pipe.start_locally(run_pipeline_steps_locally=True)
    pipe.start(queue='test_gpu')
    print('pipeline completed')

if I execute: pipe.start_locally(run_pipeline_steps_locally=True)
then everything works,
but if I change to pipe.start(queue='test_gpu'), after
clearml-agent daemon --detached --queue test_gpu --gpus 0:

then nothing happens and the green status - QUEUED
logs:

Launching the next 1 steps
Launching step [read_data]
2024-09-12 17:16:01
Launching step: read_data
Parameters:
{'kwargs/csv_path': '${pipeline.csv_path}'}
Configurations:
{}
Overrides:
{} 

Tell me pls, how to do this correctly, if I want, for example, to select a specific GPU, etc. for launch

[ainoam]

@lolpa1n As you'll see in this example, the queue specified in PipelineController.start is the one through which the controller itself will be executed.
The queue through which the pipeline steps will be executed is controlled through PipelineController.set_default_execution_queue

[lolpa1n]

@lolpa1n As you'll see in this example, the queue specified in PipelineController.start is the one through which the controller itself will be executed. The queue through which the pipeline steps will be executed is controlled through PipelineController.set_default_execution_queue

i am add pipe.set_default_execution_queue(default_execution_queue='test_gpu')
but nothing happens -

maybe is this because I'm running on the same machine?