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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') | |