I still don't see why you would change the type of the cloned Task, I'm assuming the original Task had the correct type, no?

Because it is easier for me that I create a training task out of the controller task by cloning it (so that parameters are prefilled and I can set the parent task id)

awesome, thank you 👍

What happens is different error but it was so weird that I thought it was related to the version installed

agent.package_manager.type = pip ... Using base prefix '/home/machine1/miniconda3/envs/py36' New python executable in /home/machine1/.trains/venvs-builds/3.6/bin/python3.6 Also creating executable in /home/machine1/.trains/venvs-builds/3.6/bin/python Installing setuptools, pip, wheel...

The experiment finished completely this time again

Yes this is correct. I am trying to create a minimal reproducable example

I would let the trains team answer this in details, but as a user moving from MLflow to trains, I can share the following insights:

MLflow and trains overlap when it comes to having a system with nice web UI to compare/log experiments/models/metrics. But MFlow lacks a crutial feature IMO which is ML/DevOps: Using MLFlow, you will have to take care of the whole maintenance of your machines, design interactions between them, etc. This is where trains shines, it provides these features out-of-t...

You mean you "aborted the task" from the UI?

Yes exactly

I'm assuming from the leftover processes ?

Most likely yes, but I don't see how clearml would have an impact here, I am more inclined to think it would be a pytorch dataloader issue, although I don't see why

From the log I see the agent is running in venv mode
Hmm please try with the latest clearml-agent (the others should not have any effect)

yes in venv mode, I'll try with the latest version as well

Ok, I got the following error when uploading the table as an artifact:
ValueError('Task object can only be updated if created or in_progress')

can it be that the merge op takes so much filesystem cache that the rest of the system becomes unresponsive?

I am running on bare metal, and cuda seems to be installed at /usr/lib/x86_64-linux-gnu/libcuda.so.460.39

Hi PompousParrot44 , you could have a Controller task running in the services queue that periodically schedules the task you want to run

AgitatedDove14 any chance you found something interesting? 🙂

AgitatedDove14 I think it’s on me to take the pytorch distributed example in the clearml repo and try to reproduce the bug, then pass it over to you 🙂

I'll try with that; https://github.com/allegroai/clearml/compare/master...H4dr1en:add-aws-params

clearml doesn't change the matplotlib backend under the hood, right? Just making sure 😄

Nice, seems to work! 🎉

@<1523701205467926528:profile|AgitatedDove14> I see other rc in pypi but no corresponding tags in the clearml-agent repo? are these releases legit?

Thanks!

Yes, in the Task being executed in the agents, I have:
from trains import Task task = Task.init(...) task.get_logger().report_text(str(task.get_parameters()))

yes, here is the error (the space at the end of the line is there)
` Applying uncommitted changes
Executing: ('git', 'apply'): b'error: corrupt patch at line 13\n'
Failed applying diff
trains_agent: ERROR: Failed applying git diff:
diff --git a/configs/2.2.2_from_scratch.yaml b/configs/2.2.2_from_scratch.yaml
index 9fece48..5816f78 100644
--- a/configs/2.2.2_from_scratch.yaml
+++ b/configs/2.2.2_from_scratch.yaml
@@ -136,7 +136,7 @@ data_processing:
optimizer:
type: 'RMSprop'
args:

• lr: 2.5e...

Awesome, thanks!

Is there any logic on the server side that could change the iteration number?

No, I want to launch the second step after the first one is finished and all its artifacts are uploaded

alright I am starting to get a better picture of this puzzle

But we can easily extend, right?

AgitatedDove14 yes 🙂

Usually one or two tags, indeed, task ids are not so convenient, but only because they are not displayed in the page, so I have to go back to another page to check the ID of each experiment. Maybe just showing the ID of each experiment in the SCALAR page would already be great, wdyt?

I don't think there is an example for this use case in the repo currently, but the code should be fairly simple (below is a rough draft of what it could look like)
` controller_task = Task.init(...)
controller_task.execute_remotely(queue_name="services", clone=False, exit_process=True)

while True:
periodic_task = Task.clone(template_task_id)
# Change parameters of {periodic_task} if necessary
Task.enqueue(periodic_task, queue="default")
time.sleep(TRIGGER_TASK_INTERVAL_SECS) `