Yeah, but after doing that a message pops up showing a list of artifacts from the task that could not be deleted

Makes sense, thanks!

I mean what should I write in a script to import the APIClient? (sorry if I'm not explaining myself properly 😅 )

Glad to hear that! Thanks!

Sure, I will post a mock example in a while

Nice, in the meantime as a workaround I will implement a temporary parsing code at the beginning of step functions

I have also tried with type hints and it still broadcasts to string. Very weird...

Thanks! I will try it

Well, instead of plain functions or files I use components because I need some of those steps to run on one machine and some on another. And it works perfectly fine (ignoring some minor bugs like this one). So I'm actually inserting component-decorated functions into 'helper_functions' parameter

BTW I would really appreciate it if you let me know when you get it fixed 🙏

My idea is to take advantage of the capability of getting parameters connected to a task from another task to read the path where the artifacts are stored locally, so I don't have to define it again in each script corresponding to a different task.

Now it's okey. I have found a more intuitive way to get around. I was facing the classic 'xy' problem :)

Sure, it would be very intuitive if the command to stop an agent would be as easy as:
clearml-agent daemon --stop AGENT_PID

But this path actually does not exist in my system, so how should I fix that?

Thanks to you for fixing it so quickly!

Done!

Anyway, is there any way to retrieve the information stored in the RESULTS tab of ClearML Web UI?

It's my own module (called 'tf_funcs.py')

Sure! That definitely makes sense. Where can I specify callbacks in the PipelineDecorator API?

Well, I am thinking in the case that there are several pipelines in the system and that when filtering a task by its name and project I can get several tasks. How could I build a filter for Task.get_task(task_filter=...) that returns only the task whose parent task is the pipeline task?

When you said clearml-agent initial setup are you talking about the agent section in the clearml.conf or the CLI instructions? If it is the second case I am starting the agent with the basic command:
clearml-agent daemon --queue defaultIs there any other settings I should specify to the agent?

Mmmm you are right. Even if I had 1000 components spread in different project modules, only those components that are imported in the script where the pipeline is defined would be included in the DAG plot, is that right?

Well the 'state.json' file is actually removed after the exception is raised

But I was actually asking about accessing the Pipeline task ID, not the tasks corresponding to the components.

Okay, so the idea behind the new decorator is not to group all the defined steps under the same script so that they share the same environment, but rather to simplify the process of creating scripts for each step and avoid manually calling Task.init on those scripts.

Regarding virtual environment creation from caching, I will keep running benchmarks (from what you say it might be due to high workload in the servers we use)

So far I've been unlucky in the attempt of clearml recog...

Oh, I see. I guess somehow I can retrieve that information via Task.logger , since it is stored in JSON format? Thanks!

Mmm I see. However I think that only the components used for that pipeline should be shown, as it may be the case that you have defined, say, 1000 components, and you only use 10 in a pipeline. I think that listing them all would just clutter up the results tab for that pipeline task

Or perhaps the complementary scenario with a continue_on_failed_steps parameter which may be a list containing only the steps that can be ignored in case of failure.

So great! It would be a feature that would make the work much easier instead of having to clone the task and launch it with different parameters. It could even be considered more pythonic. Do you have an immediate solution in mind to keep moving forward before the new release is ready? :)

I have found it is not possible to start a pipeline B after a pipeline A. Following the previous example, I have added one more pipeline to the script:
` from clearml import Task
from clearml.automation.controller import PipelineDecorator

@PipelineDecorator.component(return_values=["msg"], execution_queue="model_trainings")
def step_1(msg: str):
msg += "\nI've survived step 1!"
return msg

@PipelineDecorator.component(return_values=["msg"], execution_queue="model_trainings")
def st...