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https://github.com/clearml/clearml
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28 lines
1.2 KiB
Python
28 lines
1.2 KiB
Python
from clearml import Task
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from clearml.automation.controller import PipelineController
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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': '${stage_data.artifacts.dataset.url}',
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'General/test_size': 0.25})
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pipe.add_step(name='stage_train', parents=['stage_process', ],
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base_task_project='examples', base_task_name='pipeline step 3 train model',
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parameter_override={'General/dataset_task_id': '${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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