I don't, Ultralytics just output a model in project/weights/best.pt. They don't expose a way to change that value. I'm happy to rename that file manually from the code, but likely it was already uploaded by ClearML automatically

Hi @<1744891825086271488:profile|RoundElephant20> , thanks for the help. By uploading with StorageManager, will the model be registered in the ClearML Artifact section ?

that's great, thanks !

ah, okay that make sense, I'll look more into the difference between pro / enterprise. Thanks for the info !

also, I see that clearml-serving support pytorch, is there any chance for support for TensorRT ?

I mean I'm hosting it myself, it's on app.clear.ml

okay ! Right now in my workflow, I have upload, finalize and publish all happening one after the other without extra logic. From my tests it also looks like I can use a finalized but unpublished dataset without any problem. Should I be handling this differently ?

Oh, wow for some reason I thought I read somewhere in the documentation that the sync was taking care of upload and finalize. Or maybe that was for the CLI ? Anyway that's what I was missing, thank you !

@<1537605940121964544:profile|EnthusiasticShrimp49> A follow up question about metrics - My pytorch (lightning) experiments are logging to tensorboard and ClearML is automatically picking this up and uploading scalars and debug_images. If I use the set_default_upload_destination that you mentionned, would that still properly use my URI even though I am not calling Logger.current_logger().report_image directly ?

Also, I reset than deleted ~80% of the experiment that I had 2 days ago...

No ! The way I delete those is like so:
Experiment view -> Reset (one or more) experiment -> expriment is now in draft
Archive experiment
Open archive -> Delete

I get no feedback at all from the operation, but I can see the experiments are no longer available on clearml

okay I'll look into it, thanks !

I made sure to delete them from the archived tab

@<1523701070390366208:profile|CostlyOstrich36> Is there a way to migrate datasets and experiments to another workspace ?

Thanks, that is exactly the kind of info I was looking for ! If debug images are counting in the metrics quota that would explain how we reached the limit so quickly.

okay sounds good. Thanks !!

okay cool, I'm currently trying to migrate our stack to run from the git repository and using ClearML Datasets. I am still having an issue with relative imports in python, we were previously modifying PYTHONPATH in the container, but now I need to modify it manually on the host. I saw there is some documentation about that here , but I'm not sure I understand that correctly since it do...

Thanks for the reply ! I am using the enterprise version, do you have a link to some docs for the autoscaler ? On the orchestration tab I can see AWS and GCP but not Azure. (also, I was previously able to see Clearml GPUs, but it looks like they're not available anymore ?)

Great, thanks a lot for the help !

For more info, I am using jsonargparse to expose my params to clearml, but it looks like it's also picking up the params directly from YOLO

Hi @<1523701070390366208:profile|CostlyOstrich36> , Here's sample code:

from ultralytics import YOLO
from clearml import Task, Dataset
from jsonargparse import CLI

def train_yolo(ds_name: str=None):
    dataset_path = Dataset.get(dataset_name=ds_name).get_local_copy() 
    task = Task.current_task()
    
    if task == None:
        task = Task.init(project_name="YOLO", task_name=ds_name)
    
    model = YOLO("yolov8n")
    model.train(data=dataset_path)
    
if __name__ == "__main_...

okay I'll try that. Although I am using parameters from the argparser to set the task name and project. Can I init with dummy values and update those after ?

okay, and after I can use something like task.set_name("args.ds_name") ?

awesome thanks !

That's correct, I'm on the community server for now. What about for the SDK and CLI ? If they have their own credentials, can they also use clearml-data and clearml.Dataset.get() to access my dataset ?
It feels a bit off at the moment to have all the pipelines / tasks / datasets that we will use under "Anthony Courchesne's Workspace" (even though I saw I can rename it)