# Example use case:
an_optimizer = HyperParameterOptimizer(
# This is the experiment we want to optimize
base_task_id=args['template_task_id'],
# here we define the hyper-parameters to optimize
hyper_parameters=[
UniformIntegerParameterRange('General/layer_1',
min_value=128,
max_value=512,
step_size=128),
UniformIntegerParameterRange('General/layer_2',
min_value=128,
max_value=512,
step_size=128),
DiscreteParameterRange('General/batch_size', values=[96, 128, 160]),
DiscreteParameterRange('General/epochs', values=[30]),
],
# this is the objective metric we want to maximize/minimize
objective_metric_title='epoch_accuracy',
objective_metric_series='epoch_accuracy',
# now we decide if we want to maximize it or minimize it (accuracy we maximize)
objective_metric_sign='max',
# let us limit the number of concurrent experiments,
# this in turn will make sure we do dont bombard the scheduler with experiments.
# if we have an auto-scaler connected, this, by proxy, will limit the number of machine
max_number_of_concurrent_tasks=2,
# this is the optimizer class (actually doing the optimization)
# Currently, we can choose from GridSearch, RandomSearch or OptimizerBOHB (Bayesian optimization Hyper-Band)
# more are coming soon...
optimizer_class=aSearchStrategy,
# Example use case:
an_optimizer = HyperParameterOptimizer(
# This is the experiment we want to optimize
base_task_id=args['template_task_id'],
# here we define the hyper-parameters to optimize
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]),
],
# this is the objective metric we want to maximize/minimize
objective_metric_title='val_acc',
objective_metric_series='val_acc',
# now we decide if we want to maximize it or minimize it (accuracy we maximize)
objective_metric_sign='max',
# let us limit the number of concurrent experiments,
# this in turn will make sure we do dont bombard the scheduler with experiments.
# if we have an auto-scaler connected, this, by proxy, will limit the number of machine
max_number_of_concurrent_tasks=2,
# this is the optimizer class (actually doing the optimization)
# Currently, we can choose from GridSearch, RandomSearch or OptimizerBOHB (Bayesian optimization Hyper-Band)
# more are coming soon...
optimizer_class=Our_SearchStrategy,
# MODIFY THIS, MAKE SURE TO CHOOSE THE CORRECT TEMPLATE TASK ID
# experiment template to optimize in the hyper-parameter optimization
args = {
'template_task_id': '8d8ff6167a334ff59c3001c06e996eda',
'run_as_service': False,
}
args = task.connect(args)
# Example use case:
an_optimizer = HyperParameterOptimizer(
# This is the experiment we want to optimize
base_task_id=args['template_task_id'],
# here we define the hyper-parameters to optimize
hyper_parameters=[
DiscreteParameterRange('lstm_units', values=[64, 96, 128]),
DiscreteParameterRange('dense_hidden_units', values=[512, 1024, 2048]),
DiscreteParameterRange('use_roc_star', values=[True,False])
],
# this is the objective metric we want to maximize/minimize
objective_metric_title='Accuracy',
objective_metric_series='validation accuracy',
# now we decide if we want to maximize it or minimize it (accuracy we maximize)
objective_metric_sign='max',
# let us limit the number of concurrent experiments,
# this in turn will make sure we do dont bombard the scheduler with experiments.
# if we have an auto-scaler connected, this, by proxy, will limit the number of machine
max_number_of_concurrent_tasks=4,
# this is the optimizer class (actually doing the optimization)
# Currently, we can choose from GridSearch, RandomSearch or OptimizerBOHB (Bayesian optimization Hyper-Band)
# more are coming soon...