trains_optuna3.py

optimizer = HyperParameterOptimizer(
    base_task_id=TEMPLATE_TASK_ID,  
    # setting the hyper-parameters to optimize
    hyper_parameters=[
        UniformIntegerParameterRange('number_of_epochs', min_value=2, max_value=12, step_size=2),
        UniformIntegerParameterRange('batch_size', min_value=2, max_value=16, step_size=2),
        UniformParameterRange('dropout', min_value=0, max_value=0.5, step_size=0.05),
        UniformParameterRange('base_lr', min_value=0.00025, max_value=0.01, step_size=0.00025),
    ],
    # setting the objective metric we want to maximize/minimize
    objective_metric_title='accuracy',
    objective_metric_series='total',
    objective_metric_sign='max',  

    # setting optimizer 
    optimizer_class=OptimizerOptuna,
    
    # Configuring optimization parameters
    execution_queue='dan_queue',  
    max_number_of_concurrent_tasks=2,  
    optimization_time_limit=60., 
    compute_time_limit=120, 
    total_max_jobs=20,  
    min_iteration_per_job=15000,  
    max_iteration_per_job=150000,  
)