Hi, I am trying to save my trained model weights in S3 bucket instead of using ClearML storage when using clearml-task for ml training remotely. I tried to use --skip-task-init in clearml-task and set task.init in my scripts, but it doesn't seem to work. The tags and output_uri doesn't apply to the remote training

clearml-task \
    --project fluoro-motion-detection \
    --name uniformer-train \
    --folder ~/algorithms-python \
    --script ~/algorithms-python/fluoro_motion_detection/src/run/train.py \
    --requirements ~/algorithms-python/fluoro_motion_detection/src/clearml/requirements.txt \
    --args hydra=$HYDRA_OVERRIDES \
    --docker mzhengtelos/algorithm-ml:poetry-project-cuda11.4.3-runtime-ubuntu20.04 \
    --docker_args "--env CLEARML_AGENT_SKIP_PIP_VENV_INSTALL=$PYTHON_ENV_DIR --env AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID --env AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY" \
    --skip-task-init\
    --queue test-gpu\

clearml_task = Task.init(project_name=cfg.clearml_project_name, task_name=cfg.clearml_task_name, tags=cfg.tags,
                         output_uri=cfg.paths.s3_clearml_dir)

got error when using --output-uri in clearml-task clearml.storage - ERROR - Failed uploading: ' LazyEval Wrapper ' object cannot be interpreted as an integer