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- 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')
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