Hi @<1523701205467926528:profile|AgitatedDove14> , sorry for the delayed reply. So what you’re saying is to first kick off a new run and then rename the underlying Pipeline Task, which will cause that particular run to become a new pipeline name? But you have to do this only after you’ve started the run.

What would be most ideal would be to be able to right-click on a pipeline run and have a “clone” option, like you can with a task, where you can start a new run with a new name in a single ...

Correction: it works when I am running the code in my local VSCode session. I still don't get resource logging when I run in an agent. 🤔 . And on a similar topic, I have a separate task that is logging metrics with tensorboard. When running locally, I see the metrics appear in the "scalars" tab in ClearML, but when running in an agent, nothing. Any suggestions on where to look?

That could happen with any task when it’s cloned. To be honest, the cron and trigger schedulers probably deserve their own UI panel since they operate differently than other tasks. Ideally, a user would be able to add and remove jobs from the schedulers purely through the UI.

Ahhh okay, thank you. Perhaps in the future, it would be great to allow this from the UI as well?

I’m using SDK version 1.10.2 and yes, it’s self-hosted. Here is the version info for the server:

 WebApp: 1.9.1-312 • Server: 1.9.1-312 • API: 2.23 

Thanks!

Okay so I discovered that setting -e CLEARML_AGENT_PACKAGE_PYTORCH_RESOLVE=none solves the issue.

That said, if someone could explain to me why this error was occurring and why it only happens in the case of cloning, I'd love to understand. Thanks!

Hi @<1523701205467926528:profile|AgitatedDove14> , I've actually hit on something accidentally that might be a clue. I have noticed that when running inside an agent, there is a bug wherein both Task.current_task() and Logger.current_logger() return None . If these are being used by the clearml package under the hood, this could be the reason we aren't seeing the metrics.

As a workaround, I created this utility function, which works for explicit logging (though it doesn't c...

To be clear Task.init() was called initially. I had to call it again later in the code in order to get the current task object instead of Task.current_task() , which only seems to work locally. That's the part that is not intuitive.

Hi @<1523701205467926528:profile|AgitatedDove14> , on the resource logging: I tried with a sleep test and it works when I'm running it from my local machine, but when I run remotely in an agent, i do not see resource logging.

And, similarly, with tensorboard logging, it works fine when running from my machine, but not when running remotely in an agent. For this, I've decided to just re-write the logging code to use ClearML's built-in logging methods, which work fine in the agent. Would stil...

Hi @<1523701205467926528:profile|AgitatedDove14> , sure. I just need to scrape them for any sensitive info then i'll post to this thread. Thanks for your reply.

Hi Martin, I see . That makes sense though I would have expected the behavior to be the same when running remotely the first time as well . In any case, this solved the issue for me . Thanks for looking at it

Unfortunately, it's turning out to be quite time consuming to manually remove all of the private info in here. Is there a particular section of the log that would be useful to see? I can try to focus on just sharing that part.

Here's my example script:

from random import randint

from clearml import Task

if __name__ == "__main__":
    task: Task = Task.init(
        project_name="clearml-examples", task_name="try-to-make-logging-work"
    )
    task.execute_remotely(queue_name="5da90f42dd4c40edab972a4bef8eab04")
    logger = task.get_logger()
    for i in range(10):
        logger.report_scalar("example plot", series="random", value=randint(0, 100), iteration=i)

Ah interesting, okay. I'll try adding a sleep in here for testing it out. Thanks

Yes, that did make it work in this case, thank you.

Sure. I can send it on Monday. Thank you.

Hi @<1523701205467926528:profile|AgitatedDove14> , CLEARML_TASK_ID is set inside the agent's process, which is how I was able to get the task by running Task.get_task(environ["CLEARML_TASK_ID") . However I believe I've sorted out how to make both the resource logging and the tensorboard logging work in the agent. It seems that using Task.current_task() to get the task object does not work when running remotely, but calling Task.init() again does work. And after having called ...

The bash setup script option doesn’t work because that runs before the repo is cloned. I could add the git clone step there, but not sure how to access the git credentials stored in the agent.

Hi @<1523701070390366208:profile|CostlyOstrich36> , thanks for your reply. I’ll try both and see what happens.

@<1576381444509405184:profile|ManiacalLizard2> ,that’s interesting. So you actually need the imports to be in a certain order. That’s definitely new and a bit of an anti-pattern as it goes against recommended import statement order (built-in packages imported first) but if it works, that’s good news at least. I’ll try that as well. Thanks!

Hi Max, thanks very much for your message! I understand what you’re saying now, though I suppose this is not my issue since I’m not setting any of the decorator values with variables. I’ll post a query in the main channel with code snippets to see if anyone has ideas. Thank you!

The requirements option using git+https does work, at least for the main install_requires dependences in my setup.cfg. It didn’t install extra dependencies of i tried to do something like pip install my-package[with-optional] @ git+ None

I actually have a question about your original code snipped, @<1556450111259676672:profile|PlainSeaurchin97> . I have been trying to figure out a way to access the task object when running remotely so that I can instantiate the logger but when I tried task_id = os.getenv("CLEARML_TASK_ID") , it’s returning None . I also tried Task.current_task() and also got None back. What is the recommended way to access the Task object from within the remote agent?

Thanks very much! Yeah, it tends to fill up the console

Hi @<1523701070390366208:profile|CostlyOstrich36> , I would expect the loss_func parameter to be FocalLoss instead of ['FocalLoss', 'FocalLoss', 'FocalLoss', 'FocalLoss'] (and same for the validation_split_name parameter. I will try to put together an example, though it might take a little time before I can do it.

Hi @<1523701205467926528:profile|AgitatedDove14> , thanks so the code to be executed by the task needs to be provided to the Task.create() method as script=some/path.py or does it work to have something like

def my_node_task_factory(node: PipelineController.Node) -> Task:
  task = Task.create(...)
  my_function()
  return task 

@<1523701225533476864:profile|ObedientDolphin41> , I was searching for anyone having an issue like me and found this thread. I have created a simple pipeline using decorators and when I try to clone it in the UI, I get that base_task_id is empty error. It works fine when triggered programmatically from my machine. I’m wondering if you could elaborate on how you utilized the get_configuration_object and set_configuration_object methods to solve this? In my case, I’m not setting a...

Sure, I can do that

@<1523701205467926528:profile|AgitatedDove14> : FYI here it is None

I believe you should be able to set the queue_name parameter to None to accomplish this.