from clearml.automation import UniformParameterRange, UniformIntegerParameterRange, DiscreteParameterRange
from clearml.automation import HyperParameterOptimizer
from clearml.automation.optuna import OptimizerOptuna

from clearml import Task

task = Task.init(
    project_name='SpoterEmbedding',
    task_name='Automatic Hyper-Parameter Optimization',
    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="4504e0b3ec6745249d3d4c94d3d40652",  
      # setting the hyperparameters to optimize
      hyper_parameters=[
        # epochs
        UniformIntegerParameterRange('Args/epochs', 200, 800),
        # learning rate
        UniformParameterRange('Args/lr', 0.000001, 0.1),
        # optimizer
        DiscreteParameterRange('Args/optimizer', ['ADAM', 'SGD']),
        # vector length
        UniformIntegerParameterRange('Args/vector_length', 10, 100),
          ],
      # setting the objective metric we want to maximize/minimize
      objective_metric_title='silhouette_coefficient',
      objective_metric_series='val',
      objective_metric_sign='max',  

      # setting optimizer  
      optimizer_class=OptimizerOptuna,
  
      # configuring optimization parameters
      execution_queue='default',  
      optimization_time_limit=360, 
      compute_time_limit=480, 
      total_max_jobs=20,
      min_iteration_per_job=0,
      max_iteration_per_job=150000,
      )

task.execute_remotely(queue_name='default', exit_process=True)