CostlyOstrich36 maybe you have any idea why this code might not work for me?

SmugDolphin23 , maybe you have an idea?

I'm running 1.7.0 (latest docker available).
Your example did work for me, but I'm gonna try the flush() method now

Let me know, if this still doesn't work, I'll try to reproduce your issue 🙂

Hey GrotesqueDog77
A few things, first you can call _logger.flush() which should solve the issue you're seeing (We are working to add auto-flushing when tasks end 🙂 )
Second, I ran this code and it works for me without a sleep, does it also work for you?
` from clearml import PipelineController

def process_data(inputs):
import pandas as pd
from clearml import PipelineController
data = {'Name': ['Tom', 'nick', 'krish', 'jack'],
'Age': [20, 21, 19, 18]}
_logger = PipelineController.get_logger()
df = pd.DataFrame(data)
_logger.report_table('Awesome', 'Details', table_plot=df)

pipeline = PipelineController(name='erez', project='erez',version='0.1')
pipeline.add_function_step(name='process_data', function=process_data,
cache_executed_step=True)

pipeline.start_locally(run_pipeline_steps_locally=True) `What SDK vesrion are you using? I'm using V1.7.1. I also didn't pass the data as input so it might affect, but I'd be happy if you can give it a try

GrotesqueDog77 checking 🙂

AnxiousSeal95 here

Hi GrotesqueDog77 ,

Can you please add a small code snippet of this behavior? Is there a reason you're not reporting this from within the task itself or to the controller?

When I add sleep to the process_data it works if it was enough time to upload data

def process_data(inputs): import time import pandas as pd from clearml import PipelineController _logger = PipelineController.get_logger() df = pd.DataFrame.create(inputs) _logger.report_table('Awesome', 'Details', table_plot=df) time.sleep(10)

Yes it did work! Thank you!

Hi CostlyOstrich36

Here is the code example which does not work for me
` def process_data(inputs):
import pandas as pd
from clearml import PipelineController
_logger = PipelineController.get_logger()
df = pd.DataFrame.create(inputs)
_logger.report_table('Awesome', 'Details', table_plot=df)

pipeline = PipelineController(name='best_pipeline', project='test')
pipeline.add_function_step(name='process_data', function=process_data,
function_kwargs=dict(inputs=some_data),
cache_executed_step=True)
pipeline.add_function_step(name='next', function=next,
function_kwargs=(something="${process_data.ouput}")
pipeline.start_locally() `