from clearml import Task from clearml.automation.controller import PipelineController def pre_execute_callback_example(a_pipeline, a_node, current_param_override): # type (PipelineController, PipelineController.Node, dict) -> bool print('Cloning Task id={} with parameters: {}'.format(a_node.base_task_id, current_param_override)) # if we want to skip this node (and subtree of this node) we return False # return True to continue DAG execution return True def post_execute_callback_example(a_pipeline, a_node): # type (PipelineController, PipelineController.Node) -> None print('Completed Task id={}'.format(a_node.executed)) # if we need the actual Task to change Task.get_task(task_id=a_node.executed) return # Connecting ClearML with the current process, # from here on everything is logged automatically task = Task.init(project_name='examples', task_name='pipeline demo', task_type=Task.TaskTypes.controller, reuse_last_task_id=False) pipe = PipelineController(default_execution_queue='default', add_pipeline_tags=False) pipe.add_step(name='stage_data', base_task_project='examples', base_task_name='pipeline step 1 dataset artifact') pipe.add_step(name='stage_process', parents=['stage_data', ], base_task_project='examples', base_task_name='pipeline step 2 process dataset', parameter_override={'General/dataset_url': '${stage_data.artifacts.dataset.url}', 'General/test_size': 0.25}, pre_execute_callback=pre_execute_callback_example, post_execute_callback=post_execute_callback_example ) pipe.add_step(name='stage_train', parents=['stage_process', ], base_task_project='examples', base_task_name='pipeline step 3 train model', parameter_override={'General/dataset_task_id': '${stage_process.id}'}) # Starting the pipeline (in the background) pipe.start() # Wait until pipeline terminates pipe.wait() # cleanup everything pipe.stop() print('done')