Hi SubstantialElk6

If you like a new task, you can clone as HugePelican43 suggested.
You can also continue reporting to your task with continue_last_task parameter in your Task.init call:

from clearml import Task task = Task.init(project_name="YOUR PROJECT NAME", task_name="YOUR TASK NAME", continue_last_task=True)You also can specify the task id to continue (from the docs - https://allegro.ai/clearml/docs/rst/references/clearml_python_ref/task_module/task_task.html?highlight=continue_last_task ):

continue_last_task ( bool ) –
Continue the execution of a previously executed Task (experiment)
Note
When continuing the executing of a previously executed Task, all previous artifacts / models/ logs are intact. New logs will continue iteration/step based on the previous-execution maximum iteration value. For example:

The last train/loss scalar reported was iteration 100, the next report will be iteration 101.

The values are:
True - Continue the the last Task ID. specified explicitly by reuse_last_task_id or implicitly with the same logic as reuse_last_task_id False - Overwrite the execution of previous Task (default). A string - You can also specify a Task ID (string) to be continued. This is equivalent to continue_last_task=True and reuse_last_task_id=a_task_id_string.