Thanks so much @<1523701205467926528:profile|AgitatedDove14> !

Actually this is not how it works, pip will install in any way it sees fit, and it is not consistent between versions (it has to do with dependency resolving)

Oh I see. What a pain. 🤣

You can configure the agent to first install specific packages, and only then others, just add the package names here:

That's an interesting solution. I'll keep that in mind as I work more with ClearML.

Thanks for your help Martin!

@<1523701087100473344:profile|SuccessfulKoala55> You wouldn't happen to know what's going on here. :D

Ah, that makes sense. What is supposed to be hidden changes depending on the section your in, which makes sense. Now there needs to a packman sprite easter egg hidden somewhere else.

Project 2:

2024-01-22 17:21:56
task 6518c3cd13394aa4abbc8f0dc34eb763 pulled from 8a69a982f5824762aeac7b000fbf2161 by worker bigbrother:10
2024-01-22 17:22:03
Current configuration (clearml_agent v1.7.0, location: /tmp/.clearml_agent.bojpliyx.cfg):
----------------------
agent.worker_id = bigbrother:10
agent.worker_name = bigbrother
agent.force_git_ssh_protocol = true
agent.python_binary = /home/natephysics/anaconda3/bin/python
agent.package_manager.type = pip
agent.package_manager.pip_v...

The original file sizes are the same but the compressed sizes seem to be different.

✨ It works ✨

Thanks @<1523701205467926528:profile|AgitatedDove14> 😁

Stability is for wimps. I live on the edge of brining down production at any moment, like a real developer. But thanks for the update! 🙃

1707128614082 bigbrother:gpu0 INFO task 59d23c5919b04fd6947c1e463fa8c78c pulled from 9890a035b8f84872ab18d7ff207c26c6 by worker bigbrother:gpu0

Current configuration (clearml_agent v1.7.0, location: /tmp/.clearml_agent.vo_oc47r.cfg):
----------------------
agent.worker_id = bigbrother:gpu0
agent.worker_name = bigbrother
agent.force_git_ssh_protocol = true
agent.python_binary = /home/natephysics/anaconda3/bin/python
agent.package_manager.type = pip
agent.package_manager.pip_version.0 = ...

Results:

I first tried uncommenting enable_git_ask_pass: false but it didn't resolve the issue.

I then cleared the cache in the vcs-cache folder, and that did fix the issue. This is the second time the cache seemed to have been the root cause of the problem. At some point I did move from token-based auth to ssh keys. Would this require clearing the cache for any project that was cached prior to the auth change?

In the debugger I can see that before starting the scheduler the test task is added:

ScheduleJob(name='Snitch-TaskScheduler', base_task_id='', base_function=<function main.<locals>.scheduler_function.<locals>.<lambda> at 0x7f05e1ab3600>, queue='services', target_project='DevOps', single_instance=False, task_parameters=None, task_overrides=None, clone_task=True, _executed_instances=None, execution_limit_hours=None, recurring=True, starting_time=datetime.datetime(2024, 1, 17, 10, 50, 28,...

Alright, I deleted everything in the ClearML web-app waited a day tried again, it seems to be showing a configuration object in the configuration section of the scheduler task again. I honestly don't know what changed. Maybe some strange caching on the server side that got cleaned up.

@<1523701205467926528:profile|AgitatedDove14> Question: Does the schedule_function option in the TaskScheduler.add_task() method run at the time the task is scheduled to execute? So if I pass a functi...

Strange, the code seems to work perfectly when I run it locally. To make it more confusing, the queue that I enqueue it to when I run it remotely is using agents from the same server that I'm running it locally from.

From the logs it looks like the HPO application finds a worker from the queue, attempts to serialize the config sent to the worker, and crashes because of the version conflict with Pyro4. But I don't think we control any of that. I might be misunderstanding something. 🙃

@<1523701435869433856:profile|SmugDolphin23> Yeah, I just wanted to validate it was worth spending the time. Since there is already a parameter that takes callable (i.e. schedule_function ) it might make sense that we reuse the parameter. If it returns a str we validate that it's a task and if it does we can run the task as if we originally passed it as the task_id in .add_task() . This would only be a breaking change if the callable that was passed happened to return a task_id ...

Looks good 😄

Should I post this in dev?

Yes, but only because you asked so nicely 😚

Yes, I'm experimenting with this. I actually wrote my own process to do this so I just had to adapt it as a callable to pass to the scheduler. However, I'm running into an issue and I don't think this is a user error this time. When I start the scheduler, it starts running, shows up in the web-app, but then an error message in the web-app pops up Fetch parents failed and the Scheduler task disappears from the web-app. I can't even see an error log because the task is gone.

I'm running th...

Maybe the sleep between scheduler.mark_completed() and scheduler.delete() is too short? But I don't get why deleting the old scheduler task would break the new scheduler. I'm going to try testing by running the scheduler locally.

On the original model.

I'm not sure why the logs were incomplete. I think part of the reason it wasn't pulling from the repo was that it was pulling from cache. I cleared the clearml cache for that project and reran it. This should be the full log.

Sure. I'm in Europe but we can also test things async.

I see, that could work.

@<1539780284646428672:profile|PoisedElephant79> Are you sure you're not simply referring to the get operation? That seems to exclude archived datasets. But I don't see anything like that for the list_datasets operation.

This turns out to be a layer-8 error . task.execute_remotely does work but there was a bug in my code and I wasn't correctly setting the reuse_task flag when run. Sorry to bother the both of you with my mistake.

No error. Just a new task each time.

Sounds good. 👍

None ✅

I had 2 datasets on archive and 0 unarchived. When I ran the following command:

Dataset.list_datasets(dataset_project=self.task.get_project_name(), only_completed=True)

It returned two entrees for the two datasets I had on archive.