Hi ContemplativeCockroach39
Assuming you wrap your model with a flask app (or using any other serving solution), usually you need:
Get the model Add some metrics on runtime performance package in a dockerGetting a pretrained model is straight forward one you know either the creating Task or the Model ID
` from clearml import Task, Model
model_file_from_task = Task.get_task(task_id).models['output'][-1].get_local_copy()

or

model_file_from_model = Model(model_id=<moedl_id>).get_local_copy() Add performance metrics : from clearml import Task
task = Task.init(project_name='inference', task_name='runtime')
task.get_logger().report_scalar(title='performance', series='latency', value=0.123, iteration=some_counter_here) Once you run it once you have a Task of the inference code in the system, you can either enqueue to a clearml-agent, or package as a standalone docker. Packaging to a docker clearml-agent build --id <task_id_here> --docker --target docker_image_name `