mirror of
https://github.com/clearml/clearml
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133 lines
6.3 KiB
Python
133 lines
6.3 KiB
Python
import logging
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from clearml import Task
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from clearml.automation import (
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DiscreteParameterRange, HyperParameterOptimizer, RandomSearch,
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UniformIntegerParameterRange)
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# trying to load Bayesian optimizer package
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try:
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from clearml.automation.optuna import OptimizerOptuna # noqa
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aSearchStrategy = OptimizerOptuna
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except ImportError as ex:
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try:
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from clearml.automation.hpbandster import OptimizerBOHB # noqa
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aSearchStrategy = OptimizerBOHB
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except ImportError as ex:
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logging.getLogger().warning(
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'Apologies, it seems you do not have \'optuna\' or \'hpbandster\' installed, '
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'we will be using RandomSearch strategy instead')
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aSearchStrategy = RandomSearch
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def job_complete_callback(
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job_id, # type: str
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objective_value, # type: float
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objective_iteration, # type: int
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job_parameters, # type: dict
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top_performance_job_id # type: str
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):
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print('Job completed!', job_id, objective_value, objective_iteration, job_parameters)
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if job_id == top_performance_job_id:
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print('WOOT WOOT we broke the record! Objective reached {}'.format(objective_value))
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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='Hyper-Parameter Optimization',
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task_name='Automatic Hyper-Parameter Optimization',
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task_type=Task.TaskTypes.optimizer,
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reuse_last_task_id=False)
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# experiment template to optimize in the hyper-parameter optimization
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args = {
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'template_task_id': None,
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'run_as_service': False,
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}
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args = task.connect(args)
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# Get the template task experiment that we want to optimize
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if not args['template_task_id']:
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args['template_task_id'] = Task.get_task(
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project_name='examples', task_name='Keras HP optimization base').id
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# Set default queue name for the Training tasks themselves.
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# later can be overridden in the UI
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execution_queue = '1xGPU'
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# Example use case:
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an_optimizer = HyperParameterOptimizer(
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# This is the experiment we want to optimize
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base_task_id=args['template_task_id'],
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# here we define the hyper-parameters to optimize
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# Notice: The parameter name should exactly match what you see in the UI: <section_name>/<parameter>
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# For Example, here we see in the base experiment a section Named: "General"
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# under it a parameter named "batch_size", this becomes "General/batch_size"
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# If you have `argparse` for example, then arguments will appear under the "Args" section,
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# and you should instead pass "Args/batch_size"
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hyper_parameters=[
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UniformIntegerParameterRange('General/layer_1', min_value=128, max_value=512, step_size=128),
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UniformIntegerParameterRange('General/layer_2', min_value=128, max_value=512, step_size=128),
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DiscreteParameterRange('General/batch_size', values=[96, 128, 160]),
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DiscreteParameterRange('General/epochs', values=[30]),
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],
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# this is the objective metric we want to maximize/minimize
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objective_metric_title='epoch_accuracy',
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objective_metric_series='epoch_accuracy',
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# now we decide if we want to maximize it or minimize it (accuracy we maximize)
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objective_metric_sign='max',
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# let us limit the number of concurrent experiments,
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# this in turn will make sure we do dont bombard the scheduler with experiments.
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# if we have an auto-scaler connected, this, by proxy, will limit the number of machine
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max_number_of_concurrent_tasks=2,
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# this is the optimizer class (actually doing the optimization)
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# Currently, we can choose from GridSearch, RandomSearch or OptimizerBOHB (Bayesian optimization Hyper-Band)
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# more are coming soon...
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optimizer_class=aSearchStrategy,
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# Select an execution queue to schedule the experiments for execution
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execution_queue=execution_queue,
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# If specified all Tasks created by the HPO process will be created under the `spawned_project` project
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spawn_project=None, # 'HPO spawn project',
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# If specified only the top K performing Tasks will be kept, the others will be automatically archived
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save_top_k_tasks_only=None, # 5,
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# Optional: Limit the execution time of a single experiment, in minutes.
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# (this is optional, and if using OptimizerBOHB, it is ignored)
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time_limit_per_job=10.,
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# Check the experiments every 12 seconds is way too often, we should probably set it to 5 min,
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# assuming a single experiment is usually hours...
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pool_period_min=0.2,
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# set the maximum number of jobs to launch for the optimization, default (None) unlimited
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# If OptimizerBOHB is used, it defined the maximum budget in terms of full jobs
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# basically the cumulative number of iterations will not exceed total_max_jobs * max_iteration_per_job
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total_max_jobs=10,
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# set the minimum number of iterations for an experiment, before early stopping.
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# Does not apply for simple strategies such as RandomSearch or GridSearch
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min_iteration_per_job=10,
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# Set the maximum number of iterations for an experiment to execute
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# (This is optional, unless using OptimizerBOHB where this is a must)
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max_iteration_per_job=30,
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)
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# if we are running as a service, just enqueue ourselves into the services queue and let it run the optimization
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if args['run_as_service']:
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# if this code is executed by `clearml-agent` the function call does nothing.
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# if executed locally, the local process will be terminated, and a remote copy will be executed instead
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task.execute_remotely(queue_name='services', exit_process=True)
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# report every 12 seconds, this is way too often, but we are testing here J
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an_optimizer.set_report_period(2.2)
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# start the optimization process, callback function to be called every time an experiment is completed
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# this function returns immediately
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an_optimizer.start(job_complete_callback=job_complete_callback)
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# set the time limit for the optimization process (2 hours)
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an_optimizer.set_time_limit(in_minutes=120.0)
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# wait until process is done (notice we are controlling the optimization process in the background)
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an_optimizer.wait()
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# optimization is completed, print the top performing experiments id
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top_exp = an_optimizer.get_top_experiments(top_k=3)
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print([t.id for t in top_exp])
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# make sure background optimization stopped
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an_optimizer.stop()
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print('We are done, good bye')
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