As you can see in the image

anyway this got resolved\

I will check

Also it is returning empty list if I am using tags
model = Model.query_models(project_name = global_config.PROJECT_NAME, model_name="model training", tags = ['$all','best'])

[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m To update, run: [0m[32;49mpip install --upgrade pip[0m

1687953244763 si-sajalv:0 ERROR User aborted: stopping task (3)

1687953245766 si-sajalv:0 DEBUG Current configuration (clearml_agent v1.5.2, location: /tmp/clearml.conf):
----------------------
sdk.storage.cache.default_base_dir = /clearml_agent_cache
sdk.storage.cache.size.min_free_bytes = 10GB
sdk.storage.direct_access.0.url = file://*
`sdk.metrics.file_history_si...

I ll explain you what happened, I ran " None " this code, so all the steps of pipeline ran

so the individual part of pipeline ran, but in dashboard when I am seeing the pipeline it is running continuously and not ending even after all the tasks are completed.
the above part is from the console of the pipeline

Environment setup completed successfully
Starting Task Execution:
2023-06-28 12:31:52
ClearML results page: None
2023-06-28 12:31:58
ClearML pipeline page: None
2023-06-28 12:32:05
Launching the next 1 steps
`Launching step [stag...

Hey, I seem to have resolve this issue, but stuck in another.
Apparently even after all the tasks got completed of a pipeline, the pipeline is still running, I had to abort it manually. Am I missing any code to stop it after all tasks execution?

Hey @<1537605940121964544:profile|EnthusiasticShrimp49> I updated clearml but now the issue is my pipeline is stuck here.
Previously it was working fine till the above mentioned issue and I made no change except the mentioned.

clearml==1.9.1
clearml-agent==1.5.2

I am not self hosting the server, using the one provided by clearml side

@<1537605940121964544:profile|EnthusiasticShrimp49> is this a code issue or some bug?

It ran, thanks.. but that original problem persisits. Pipeline is running once all the tasks completed.

@<1523701205467926528:profile|AgitatedDove14>
I am using pipeline by task and using pipe.add_parameter method to add the parameter through ui

pipe.add_parameter('random_state',2024) #model training
pipe.add_parameter('epochs',10)
pipe.add_parameter('learning_rate',0.001)

and then overriding the parameters using parameter_overide

pipe.add_step(
name='model_training',
parents=['preprocess_data'],
base_task_project=global_config.PROJECT_NAME,
` bas...

Ignore default, I am trying to run with another docker, but it is also stuck as same

@<1523701205467926528:profile|AgitatedDove14> I am a bit lost, can you elaborate?

Image 1 shows original pipeline, image 2 show cloned pipeline and image 3 show parameters in cloned pipeline's run

It was stuck here. I had to abort manually. All the tasks completed though.

sure, I ll check and update

one

@<1523701205467926528:profile|AgitatedDove14>
Clearml version - 1.12.1

In the pipeline example None this specifically, at line 83
parameter_override={'General/num_boost_round': 250,
'General/test_size': 0.5,
'General/random_state': random_state}
these are fixed and cannot be changed by user when the pipeline is cloned, I am trying to make thes...

yes

yes

It resolved.
I was doing 2 things wrong, defining params before task.init and while using task.connect(params) I was naming it and trying to set params using General/param_name

thanks for the support

Noo, in general when I am running this command clearml-agent daemon --queue services --docker clearml-pipeline:0.6 everything is expected to happen inside docker container. But what is happening is when I am reaching my task eg task1, inside pipeline task1 console is giving me this error. Which means it is expecting docker inside the container in which it is already running.

Hope I am clear now

from clearml import Task
from clearml.automation import PipelineController
pipe = PipelineController(name='PIPE_TEST_3',project='PIPE_TEST_3',version="0.0.1",add_pipeline_tags=False)

pipe.add_parameter("url",
" None ",
"dataset_url"
)

pipe.set_default_execution_queue('services')

`pipe.add_step(name="stage_da...

Another issue that I am facing is I am unable to call different functions from other modules present in the same directory.