optimization_job.py

from clearml.automation import UniformParameterRange, UniformIntegerParameterRange, DiscreteParameterRange
from clearml.automation import HyperParameterOptimizer
from clearml.automation import GridSearch
 
from clearml import Task
 
task = Task.init(project_name='HPO Code',
                task_name='basic dict optimizer',
                task_type=Task.TaskTypes.optimizer,
                reuse_last_task_id=False)
 
optimizer = HyperParameterOptimizer(
   # specifying the task to be optimized, task must be in system already so it can be cloned
   base_task_id='7b2a17a528594c7a93861e77eb056f89',
   # setting the hyper-parameters to optimize
   hyper_parameters=[
       UniformIntegerParameterRange('General/epochs', min_value=2, max_value=12, step_size=5),
       UniformParameterRange('General/lr', min_value=0.0001, max_value=0.01, step_size=0.002),
       DiscreteParameterRange('General/comments', ['Wow', 'Such Optimization', 'Very Hyper'])
   ],
   # setting the objective metric we want to maximize/minimize
   objective_metric_title='Optimization Metric',
   objective_metric_series='model_output',
   objective_metric_sign='max',
 
   # setting optimizer
   optimizer_class=GridSearch,
 
   # configuring optimization parameters
   execution_queue='default',
   max_number_of_concurrent_tasks=10,
   optimization_time_limit=60.,
   compute_time_limit=120,
   total_max_jobs=20,
   min_iteration_per_job=0,
   max_iteration_per_job=150000,
)