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