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https://github.com/clearml/clearml
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50 lines
1.8 KiB
Python
50 lines
1.8 KiB
Python
from clearml import Task
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from time import sleep
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# Initialize the Task Pipe's first Task used to start the Task Pipe
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task = Task.init(
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"examples", "Simple Controller Task", task_type=Task.TaskTypes.controller
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)
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# Create a hyper-parameter dictionary for the task
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param = dict()
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# Connect the hyper-parameter dictionary to the task
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param = task.connect(param)
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# In this example we pass next task's name as a parameter
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param["next_task_name"] = "Toy Base Task"
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# This is a parameter name in the next task we want to change
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param["param_name"] = "Example_Param"
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# This is the parameter value in the next task we want to change
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param["param_name_new_value"] = 3
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# The queue where we want the template task (clone) to be sent to
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param["execution_queue_name"] = "default"
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# Simulate the work of a Task
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print("Processing....")
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sleep(2.0)
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print("Done processing :)")
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# Get a reference to the task to pipe to.
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next_task = Task.get_task(
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project_name=task.get_project_name(), task_name=param["next_task_name"]
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)
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# Clone the task to pipe to. This creates a task with status Draft whose parameters can be modified.
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cloned_task = Task.clone(source_task=next_task, name="Auto generated cloned task")
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# Get the original parameters of the Task, modify the value of one parameter,
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# and set the parameters in the next Task
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cloned_task_parameters = cloned_task.get_parameters()
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cloned_task_parameters[param["param_name"]] = param["param_name_new_value"]
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cloned_task.set_parameters(cloned_task_parameters)
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# Enqueue the Task for execution. The enqueued Task must already exist in the clearml platform
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print(
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"Enqueue next step in pipeline to queue: {}".format(param["execution_queue_name"])
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)
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Task.enqueue(cloned_task.id, queue_name=param["execution_queue_name"])
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# We are done. The next step in the pipe line is in charge of the pipeline now.
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print("Done")
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