Hi, I'm looking at ClearML as an option to automate our training pipelines. However, from reading the documentation I'm confused if ClearML can do what we want. In essence, I would like to understand the methods of queuing a Task at a Worker .

Say I have a script, experiment_a.py that when executed trains some model for a dataset, and I have a ClearML Worker in Docker mode waiting for tasks in the queue default . How would I as a user, enqueue and modify my script experiment_a.py such that it can be executed as a Task on the Worker ?

E.g. adding the following lines to the top of my script (like here https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/pytorch_mnist.py ):
` from clearml import Task

task = Task.init(project_name='experiment_a', task_name='experiment_a', task_type=Task.TaskTypes.training) Will of course create a new task and start the training locally when executed; not on the Worker . I see no examples in the documentation how what I need such that I can enqueue my script as a task at my Worker ` for execution? Can anyone tell me if that's possible, and what the steps are to do so?