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 ...

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!

I think this is what you're looking for but let me know if you meant something different:

{
    "meta": {
        "id": "76fffdf3b04247fa8f0c3fc0743b3ccb",
        "trx": "76fffdf3b04247fa8f0c3fc0743b3ccb",
        "endpoint": {
            "name": "tasks.get_by_id_ex",
            "requested_version": "2.30",
            "actual_version": "1.0"
        },
        "result_code": 200,
        "result_subcode": 0,
        "result_msg": "OK",
        "error_stack": "",
        "error_data"...

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

It seems so, yes. I'm not the one who did the server migration, but as a user I believe this is when I started noticing the issue for new datasets created after the migration.

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.

@<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...

Okay well I have to supply them again for the function to work, but the values are ignored so i can just have a hard-coded version for remote.

I am still struggling to figure out how to update the parameter defaults, though. I would like to be able to do the equivalent of the PipelineController.add_parameter() so that I can supply a local config with new defaults that are used on the remote execution. Otherwise, I’m stuck with whatever defaults are in the function signature.

Server (see screenshot). Thanks!

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!

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?

SDK: clearml==1.15.1

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.

I'll check

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> , sure. I just need to scrape them for any sensitive info then i'll post to this thread. Thanks for your reply.

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.

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

Hi @<1523701435869433856:profile|SmugDolphin23> , so I need to call the pipeline function again in the remote context? I guess I thought when I start it up, my local session parses the pipeline and then transmits it to the server to run but it sounds like, it just copies the code and then i need to effectively call it again in the agent?

Are those fixed from the local environment or do i need to also supply those again in the remote context?

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 

import json
import os
import sys
from argparse import ArgumentParser
from logging import getLogger
from pathlib import Path
from typing import Callable

from clearml import PipelineDecorator, Task

from clearml_pipelines_examples.base.pipeline_settings import ExecutionMode
from clearml_pipelines_examples.pipelines.examples.train_model_on_random_data import (
    TrainModelPipelineKwargs,
    TrainModelPipelineSettings,
)
from clearml_pipelines_examples.tasks.examples import generate_dat...

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

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...

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...

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.

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.

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

Sorry, i meant the arguments that are supplied to the decorator method, itself @PipelineDecorator.pipeline() and @PipelineDecorator.component() , things like name , project , docker_args , etc.