mirror of
https://github.com/clearml/clearml
synced 2025-02-01 01:26:49 +00:00
46 lines
1.7 KiB
Python
46 lines
1.7 KiB
Python
from random import random, sample
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from trains import Task
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# define random search space,
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# This is a simple random search
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# (can be integrated with 'bayesian-optimization' 'hpbandster' etc.)
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space = {
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'batch_size': lambda: sample([64, 96, 128, 160, 192], 1)[0],
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'layer_1': lambda: sample(range(128, 512, 32), 1)[0],
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'layer_2': lambda: sample(range(128, 512, 32), 1)[0],
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}
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# number of random samples to test from 'space'
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total_number_of_experiments = 3
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# execution queue to add experiments to
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execution_queue_name = 'default'
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# Select base template task
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# Notice we can be more imaginative and use task_id which will eliminate the need to use project name
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template_task = Task.get_task(project_name='examples', task_name='Keras AutoML base')
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for i in range(total_number_of_experiments):
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# clone the template task into a new write enabled task (where we can change parameters)
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cloned_task = Task.clone(source_task=template_task,
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name=template_task.name+' {}'.format(i), parent=template_task.id)
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# get the original template parameters
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cloned_task_parameters = cloned_task.get_parameters()
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# override with random samples form grid
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for k in space.keys():
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cloned_task_parameters[k] = space[k]()
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# put back into the new cloned task
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cloned_task.set_parameters(cloned_task_parameters)
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print('Experiment {} set with parameters {}'.format(i, cloned_task_parameters))
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# enqueue the task for execution
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Task.enqueue(cloned_task.id, queue_name=execution_queue_name)
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print('Experiment id={} enqueue for execution'.format(cloned_task.id))
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# we are done, the next step is to watch the experiments graphs
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print('Done')
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