Hi @<1523701087100473344:profile|SuccessfulKoala55> , thanks, and how can I get the "id" to use with update for the dataset folder case?

Yes, or even just something like task.get_size()

Would just having some python API be an option? It would be more than enough to check what is causing this, and it would be called infrequently

Hi @<1523701070390366208:profile|CostlyOstrich36> , thanks but in this case I’d like to get also the ids of the running workers, so that I can selectively stop some of them. Is it possible somehow?

Hi @<1523701087100473344:profile|SuccessfulKoala55> , I think the issue is where to put the connect_configuration call. I can't put it inside run_pipeline because it's only running remotely and it doesn't have access to the file, and I can't put it in the script before the call to run_pipeline since the task has not been initialized yet.

Also: what's the purpose of storing the pipeline arguments as artifacts then? When it runs remotely it still runs the main script as entrypoint and not the pipeline function directly, so all the arguments will be replaced by what is passed to the function during the remote execution, right?

Hi @<1523701087100473344:profile|SuccessfulKoala55> , is there any workaround?

I've upladed an example here for simiplicity: None

@<1523701435869433856:profile|SmugDolphin23> then the issue is that config is not set. I also tried with:

import yaml
import argparse
from my_pipeline.pipeline import run_pipeline
from clearml import Task

parser = argparse.ArgumentParser()
parser.add_argument('--config', type=str, required=True)

if __name__ == '__main__':
    if Task.running_locally()::
      args = parser.parse_args()
      with open(args.config) as f:
          config = yaml.load(f, yaml.FullLoader)
    else:
     ...

For instance, I have in my_pipeline/__main__.py :

import yaml
import argparse
from my_pipeline.pipeline import run_pipeline

parser = argparse.ArgumentParser()
parser.add_argument('--config', type=str, required=True)

if __name__ == '__main__':
    args = parser.parse_args()
    with open(args.config) as f:
        config = yaml.load(f, yaml.FullLoader)
    run_pipeline(config)

and in my_pipeline/pipeline.py :

@PipelineDecorator.pipeline(
    name='Main',
    project=...

Hi @<1523701205467926528:profile|AgitatedDove14> , I already tried to check manually in the web UI for some anomalous file, i.e. by downloading the log files or exporting the metrics plots, but I couldn't find anything that takes more than 100KB, and I'm already at 300MB of usage with just 15 tasks. It's not possible to get more info using some python APIs?

Basically I want to run a function in parallel, and having that function create multiple tasks. So I was thinking of setting up a pipeline to have this hierarchy main -> parallelized_function -> init_task_function . But I guess I could also just call Task.create in init_task_function and achieve the same

So the issue is that I would like too keep the list of hyperparams and metrics, if I clean them up then I would lose them. But I agree that I might be overthinking it

Hi @<1523701070390366208:profile|CostlyOstrich36> , sorry how would you use them exactly?

Yes I can read it using this. I was just wondering if there is a way to read the file downloaded directly from the UI

What is not clear to me is how you would use the callbacks to run the step locally. Are there some properties that needs to be set in the task? I see that there is a start_controller_locally option for the main @PipelineDecorator.pipeline , but I don't see it for @PipelineDecorator.component

I just have some for loop in some pipeline components, when processing some files. I know it increases the flush intervals and it’s working when run locally, I only see a new line from tqdm every ~30s. It’s just when I run the same script in docker using the agent I get a new line every ~5s

Hi, yes I’m using the same clearml.conf on the agent, in the logs I can see that console_cr_flush_period is set to 30

So the longest experiments I have takes ~800KB in logs. I have tens of plotly plots logged manually, how are they stored internally? I tried to export them to json and they don't take more than 50KB each, but maybe they take more memory internally?

I deleted a few experiments, but they had the same kind of plots and metrics. so I don't think they would release much space

Hi @<1523701087100473344:profile|SuccessfulKoala55> , I'm uploading some debug images by they are around 300KB each, and less than 10 per experiment. Also, aren't debug images counted as artifacts for the quota?

I have some git diffs logged but they are very small. For the configurations I saw that the datasets tasks have a fairly large "Dataset Content" config (~2MB), but I only have 5 dataset tasks

Same thing, it's not visible under PIPELINES

Thanks @<1523701087100473344:profile|SuccessfulKoala55> , I’ll take a look

Hi @<1523701435869433856:profile|SmugDolphin23> , I just tried it but Task.current_task() returns None even when running in remotely

Yes these are the only actions. The task is moved correctly tho, I can see it under f'{config.project_id}/.pipelines' in the UI, the issue is that it's not visible under PIPELINES . I haven't tried with tasks or fiunctions pipelines yet.

I think I found a solution using pipeline_task.move_to_project(new_project_name=f'{config.project_id}/.pipelines/{config.run_name}', system_tags =['hidden', 'pipeline'])

Hi @<1523701087100473344:profile|SuccessfulKoala55> , thanks for the answer, I'll try that. Would you suggest any other simpler way to achieve the same result? I just want to get the best model according to a logged metric.