# Hyperparameter Optimization¶

This page describes ClearML automation, hyperparameter optimization example script hyper_parameter_optimizer.py example, which is an example script in the GitHub clearml repository, examples/optimization/hyper-parameter-optimization directory.

## Set the search strategy for optimization¶

We require a search strategy for the optimization, and a search strategy optimizer class to implement that strategy.

We can use one of the following search strategies:

• BOHB performs robust and efficient hyperparameter optimization at scale by combining the speed of Hyperband searches with the guidance and guarantees of convergence of Bayesian Optimization.

ClearML implements BOHB for automation with HpBandSter’s bohb.py. For more information about HpBandSter BOHB, see the HpBandSter documentation.

• Random uniform sampling of hyperparameters strategy - automation.optimization.RandomSearch

• Full grid sampling strategy of every hyper-parameter combination - Grid search automation.optimization.GridSearch.

• Custom - Use a custom class and inherit from the ClearML automation base strategy class, automation.optimization.SearchStrategy.

The search strategy class we choose will be passed to the automation.optimization.HyperParameterOptimizer object later.

Our example code attempts to import the OptimizerOptuna for the search strategy. If you do not have clearml.automation.optuna installed, it attempts to import OptimizerBOHB. If you do not have clearml.automation.hpbandster installed, it uses the RandomSearch for the search strategy.

aSearchStrategy = None

if not aSearchStrategy:
try:
from clearml.automation.optuna import OptimizerOptuna
aSearchStrategy = OptimizerOptuna
except ImportError as ex:
pass

if not aSearchStrategy:
try:
from clearml.automation.hpbandster import OptimizerBOHB
aSearchStrategy = OptimizerBOHB
except ImportError as ex:
pass

if not aSearchStrategy:
logging.getLogger().warning(
'Apologies, it seems you do not have \'optuna\' or \'hpbandster\' installed, '
'we will be using RandomSearch strategy instead')
aSearchStrategy = RandomSearch


## Define a callback¶

When the optimization starts, we will provide a callback that returns the best performing set of hyperparameters. Here, we define that call method, job_complete_callback which returns the ID of top_performance_job_id.

def job_complete_callback(
job_id,                 # type: str
objective_value,        # type: float
objective_iteration,    # type: int
job_parameters,         # type: dict
top_performance_job_id  # type: str
):
print('Job completed!', job_id, objective_value, objective_iteration, job_parameters)
if job_id == top_performance_job_id:
print('WOOT WOOT we broke the record! Objective reached {}'.format(objective_value))


Initialize the Task which will be stored in ClearML Server when the code runs. After the code runs at least once, you can reproduce and tune it.

We set the Task type as optimizer, and create a new experiment (and Task object) each time the optimizer runs (reuse_last_task_id=False).

When the code runs, you will see an experiment named Automatic Hyper-Parameter Optimization associated with the project Hyper-Parameter Optimization in the ClearML Web UI.

# Connecting CLEARML


## Setup the arguments¶

We create an arguments dictionary that contains the ID of the Task to optimize, and a Boolean indicating whether the optimizer will run as a service, see Running as a service.

In this example, we optimize the experiment named Keras HP optimization base which must have run at least once so that it is in ClearML Server.

Since the arguments dictionary is connected to the Task, after the code runs once, you can change template_task_id and optimize a different experiment, see tuning experiments.

# experiment template to optimize in the hyper-parameter optimization
args = {
'run_as_service': False,
}

# Get the template task experiment that we want to optimize


## Instantiate the optimizer object¶

Instantiate an automation.optimization.HyperParameterOptimizer object, setting the optimization parameters, beginning with the ID of the experiment to optimize.

an_optimizer = HyperParameterOptimizer(
# This is the experiment we want to optimize


Set the hyperparameter ranges to sample, instantiating them as ClearML automation objects using automation.parameters.UniformIntegerParameterRange and automation.parameters.DiscreteParameterRange.

hyper_parameters=[
UniformIntegerParameterRange('layer_1', min_value=128, max_value=512, step_size=128),
UniformIntegerParameterRange('layer_2', min_value=128, max_value=512, step_size=128),
DiscreteParameterRange('batch_size', values=[96, 128, 160]),
DiscreteParameterRange('epochs', values=[30]),
],


Set the metric to optimize and the optimization objective.

objective_metric_title='val_acc',
objective_metric_series='val_acc',
objective_metric_sign='max',


Set the number of concurrent Tasks.

max_number_of_concurrent_tasks=2,


Set the optimization strategy, see Set the search strategy for optimization.

optimizer_class=aSearchStrategy,


Specify the queue to use for remote execution. We override this, if the optimizer runs as a service.

execution_queue='1xGPU',


Specify the remaining parameters, including the time limit per Task (minutes), period for checking the optimization (minutes), maximum number of jobs to launch, minimum and maximum number of iterations for each Task.

# Optional: Limit the execution time of a single experiment, in minutes.
# (this is optional, and if using  OptimizerBOHB, it is ignored)
time_limit_per_job=10.,
# Check the experiments every 6 seconds is way too often, we should probably set it to 5 min,
# assuming a single experiment is usually hours...
pool_period_min=0.1,
# set the maximum number of jobs to launch for the optimization, default (None) unlimited
# If OptimizerBOHB is used, it defined the maximum budget in terms of full jobs
# basically the cumulative number of iterations will not exceed total_max_jobs * max_iteration_per_job
total_max_jobs=10,
# This is only applicable for OptimizerBOHB and ignore by the rest
# set the minimum number of iterations for an experiment, before early stopping
min_iteration_per_job=10,
# Set the maximum number of iterations for an experiment to execute
# (This is optional, unless using OptimizerBOHB where this is a must)
max_iteration_per_job=30,


## Running as a service¶

The optimization can run as a service, if you set the run_as_service argument to true. For more information about running as a service, see ClearML Agent services container on “Concepts and Architecture” page.

# if we are running as a service, just enqueue ourselves into the services queue and let it run the optimization
if args['run_as_service']:
# if this code is executed by clearml-agent the function call does nothing.
# if executed locally, the local process will be terminated, and a remote copy will be executed instead


## Optimize¶

The optimizer is ready. Set the report period and start it, providing the callback method to report the best performance.

# report every 12 seconds, this is way too often, but we are testing here J
an_optimizer.set_report_period(0.2)
# start the optimization process, callback function to be called every time an experiment is completed
# this function returns immediately
an_optimizer.start(job_complete_callback=job_complete_callback)
# set the time limit for the optimization process (2 hours)


Now that it is running, set a time limit for optimization, wait, get the best performance, print it, and finally stop the optimizer.

# set the time limit for the optimization process (2 hours)
an_optimizer.set_time_limit(in_minutes=90.0)
# wait until process is done (notice we are controlling the optimization process in the background)
an_optimizer.wait()
# optimization is completed, print the top performing experiments id
top_exp = an_optimizer.get_top_experiments(top_k=3)
print([t.id for t in top_exp])
# make sure background optimization stopped
an_optimizer.stop()

print('We are done, good bye')