Hi Martin

Thank you very much for your answer. I have the dataset already uploaded and it is visible by datasets. Also the dataset is downloaded and stored by .clearml. If i try to accses the data with the following code I get an Permission denied error.

......
File "C:\Users\junke\AppData\Local\Programs\Python\Python310\lib\gzip.py", line 174, in init
fileobj = self.myfileobj = builtins.open(filename, mode or 'rb')
PermissionError: [Errno 13] Permission denied: 'C:/Users/junke/.clearml/cache/storage_manager/datasets/ds_30892c41582b4537bb9508f3c09ae9ed'
.........
File "C:\Users\junke\Dropbox\MIR\Ausbildung\MAS Automation Management\001_Module\005_Masterarbeit\Software\003_Test_Laptop_Yoga\venv\lib\site-packages\ultralytics\engine\trainer.py", line 120, in init
raise RuntimeError(emojis(f"Dataset '{clean_url(self.args.data)}' error ❌ {e}")) from e
RuntimeError: Dataset ' None ' error [Errno 13] Permission denied: 'C:/Users/junke/.clearml/cache/storage_manager/datasets/ds_30892c41582b4537bb9508f3c09ae9ed'

However if I accses the data directly with:
data = r"C:\Users\junke\.clearml\cache\storage_manager\datasets\ds_30892c41582b4537bb9508f3c09ae9ed\0013_Datenset\data.yaml"

there is no errormessage and the data can be accssed.

import pandas as pd
from ultralytics import YOLO
from clearml import Task, Dataset

# Creating a ClearML Task
task = Task.init(
    project_name="Training_MASAM_Modell_N",
    task_name="Datensatz_0013_Freeze_15",
    output_uri=True
)
model = YOLO("yolov8n.pt")

dataset_name = "0013_Dataset"
dataset_project = "Vehicle_Dataset"

dataset_path = Dataset.get(
    dataset_name=dataset_name,
    dataset_project=dataset_project,
    alias="0013_Dataset"
).get_local_copy()

data=dataset_path
#data = r"C:\Users\junke\.clearml\cache\storage_manager\datasets\ds_30892c41582b4537bb9508f3c09ae9ed\0013_Datenset\data.yaml"

def main():
    results = model.train(data=data,
                          epochs=80,
                          device=0,             # 0 = GPU
                          imgsz=640,
                          patience=50,          # Epochen die gewartet werden bis das Training vorzeitig beendet wird, wenn keine Verbesserung erkannt wird
                          batch=16,             # Anzahl der Bilder pro batch
                          save=True,
                          resume=False,         # Start Training vom letzten Checkpunkt (Wenn z.B. wegen Fehler abgebrochen wurde)
                          freeze=None,          # Freeze first n Layers, oder Liste von Layern
                          pretrained=True       # Benuetze ein vortrainiertes Modell; default=True
                          )
if __name__ == '__main__':
    main()