Hi, I found the problem using the example Martin gave. Apparently you cannot use pipe.start_locally() at all when trying to clone the task and work completely remote (I thought it would treat the agent as local instead when I send it to a queue). It works with the combination of pipe.set_default_execution_queue('agent') and pipe.start(queue = 'agent2(EC2)') . However, must I really have two clearml-agents for complete automation? To the best of my knowledge, setting both ...

This is the cloned one

Yup, the pipeline stops instantly with "Launching the next 0 steps".

The configuration tab -> configuration objects -> pipeline is empty

Sorry for the comments haha. Trying to note down whatever I learn as much as I can

Still the same problem 😕 . I used pipe.set_default_execution_queue('agent') and pipe.start_locally() for my pipeline.py. When cloning, I enqueue the pipeline to another agent: Task.enqueue(task = clone_task.id, queue_name= 'agent(EC2)')

I also tried cloning an individual task, That surprisingly works. Not sure why my pipeline doesn't.

Now I just need to wait for this to finish and clone it later 🤞

Let me test it out. I thought if I run it remotely with pipe.start_locally(run_pipeline_steps_locally=True) , the local becomes remote instead

` from clearml import PipelineController

pipe = PipelineController(name="clearmlsample_pipeline",
project="clearmlsample",
version="1.0.0")

pipe.add_parameter('seed', 2222, description='random seed to standardize randomness')
pipe.add_parameter('n_trials', 10, description='trials to run during optimization')

pipe.add_step(
name='get_data', # can be named anything
# connect pipeline to task (obtain data from Task.init in python fi...

Yes the configuration and stages (DAG visualization) were there right until the agent finish cloning the environment. Then it goes missing.

I dont know why 😢 . Updated to 1.7.2, was staring at the configuration object, there was stuff in it until it reached "Starting Task Execution:" in the logs. It went missing after

Working on the 1.7.2 right now

Seems to be working, its in the first stage 😮

All right testing with:
pipe.set_default_execution_queue('agent') pipe.start_locally() pipe.start(queue= 'agent')

Yes, similar but via Github Actions for automation. Just wanted to know if there is an easier way to connect to clearml instead of creating a new workflow for any CI/CD purpose.

Aside from that, I tried cloning my task (pipeline) and enqueuing it to a clearml-agent.
` filter = {'status': ['published'], 'order_by': ['-last_update'], 'type': ['controller']}
pipeline_task = Task.get_tasks(project_name='clearmlsample/clearmlsample_pipeline/.pipeline',
task_filter = f...

Original one