from clearml import Task
# Connecting ClearML with the current process,
# from here on everything is logged automatically
from clearml.automation import HyperParameterOptimizer, UniformParameterRange
from clearml.automation.optuna import OptimizerOptuna

task = Task.init(project_name='Hyper-Parameter Optimization',
                 task_name='YOLOv5',
                 task_type=Task.TaskTypes.optimizer,
                 reuse_last_task_id=False)

# Example use case:
optimizer = HyperParameterOptimizer(
    # This is the experiment we want to optimize
    base_task_id='<your_template_task_id>',
    # here we define the hyper-parameters to optimize
    # Notice: The parameter name should exactly match what you see in the UI: <section_name>/<parameter>
    # For Example, here we see in the base experiment a section Named: "General"
    # under it a parameter named "batch_size", this becomes "General/batch_size"
    # If you have `argparse` for example, then arguments will appear under the "Args" section,
    # and you should instead pass "Args/batch_size"
    hyper_parameters=[
        UniformParameterRange('Hyperparameters/lr0', min_value=1e-5, max_value=1e-1),
        UniformParameterRange('Hyperparameters/lrf', min_value=0.01, max_value=1.0),
        UniformParameterRange('Hyperparameters/momentum', min_value=0.6, max_value=0.98),
        UniformParameterRange('Hyperparameters/weight_decay', min_value=0.0, max_value=0.001),
        UniformParameterRange('Hyperparameters/warmup_epochs', min_value=0.0, max_value=5.0),
        UniformParameterRange('Hyperparameters/warmup_momentum', min_value=0.0, max_value=0.95),
        UniformParameterRange('Hyperparameters/warmup_bias_lr', min_value=0.0, max_value=0.2),
        UniformParameterRange('Hyperparameters/box', min_value=0.02, max_value=0.2),
        UniformParameterRange('Hyperparameters/cls', min_value=0.2, max_value=4.0),
        UniformParameterRange('Hyperparameters/cls_pw', min_value=0.5, max_value=2.0),
        UniformParameterRange('Hyperparameters/obj', min_value=0.2, max_value=4.0),
        UniformParameterRange('Hyperparameters/obj_pw', min_value=0.5, max_value=2.0),
        UniformParameterRange('Hyperparameters/iou_t', min_value=0.1, max_value=0.7),
        UniformParameterRange('Hyperparameters/anchor_t', min_value=2.0, max_value=8.0),
        UniformParameterRange('Hyperparameters/fl_gamma', min_value=0.0, max_value=4.0),
        UniformParameterRange('Hyperparameters/hsv_h', min_value=0.0, max_value=0.1),
        UniformParameterRange('Hyperparameters/hsv_s', min_value=0.0, max_value=0.9),
        UniformParameterRange('Hyperparameters/hsv_v', min_value=0.0, max_value=0.9),
        UniformParameterRange('Hyperparameters/degrees', min_value=0.0, max_value=45.0),
        UniformParameterRange('Hyperparameters/translate', min_value=0.0, max_value=0.9),
        UniformParameterRange('Hyperparameters/scale', min_value=0.0, max_value=0.9),
        UniformParameterRange('Hyperparameters/shear', min_value=0.0, max_value=10.0),
        UniformParameterRange('Hyperparameters/perspective', min_value=0.0, max_value=0.001),
        UniformParameterRange('Hyperparameters/flipud', min_value=0.0, max_value=1.0),
        UniformParameterRange('Hyperparameters/fliplr', min_value=0.0, max_value=1.0),
        UniformParameterRange('Hyperparameters/mosaic', min_value=0.0, max_value=1.0),
        UniformParameterRange('Hyperparameters/mixup', min_value=0.0, max_value=1.0),
        UniformParameterRange('Hyperparameters/copy_paste', min_value=0.0, max_value=1.0)],
    # this is the objective metric we want to maximize/minimize
    objective_metric_title='metrics',
    objective_metric_series='mAP_0.5',
    # 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=1,
    # this is the optimizer class (actually doing the optimization)
    # Currently, we can choose from GridSearch, RandomSearch or OptimizerBOHB (Bayesian optimization Hyper-Band)
    optimizer_class=OptimizerOptuna,
    # If specified only the top K performing Tasks will be kept, the others will be automatically archived
    save_top_k_tasks_only=5,  # 5,
    compute_time_limit=None,
    total_max_jobs=20,
    min_iteration_per_job=None,
    max_iteration_per_job=None,
)

# report every 10 seconds, this is way too often, but we are testing here
optimizer.set_report_period(10 / 60)
# You can also use the line below instead to run all the optimizer tasks locally, without using queues or agent
# an_optimizer.start_locally(job_complete_callback=job_complete_callback)
# set the time limit for the optimization process (2 hours)
optimizer.set_time_limit(in_minutes=120.0)
# Start the optimization process in the local environment
optimizer.start_locally()
# wait until process is done (notice we are controlling the optimization process in the background)
optimizer.wait()
# make sure background optimization stopped
optimizer.stop()

print('We are done, good bye')