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ultralytics/utils/tuner.py

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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from ultralytics.cfg import TASK2DATA, TASK2METRIC, get_cfg, get_save_dir
+from ultralytics.utils import DEFAULT_CFG, DEFAULT_CFG_DICT, LOGGER, NUM_THREADS, checks, colorstr
+
+
+def run_ray_tune(
+    model,
+    space: dict = None,
+    grace_period: int = 10,
+    gpu_per_trial: int = None,
+    max_samples: int = 10,
+    **train_args,
+):
+    """
+    Run hyperparameter tuning using Ray Tune.
+
+    Args:
+        model (YOLO): Model to run the tuner on.
+        space (dict, optional): The hyperparameter search space. If not provided, uses default space.
+        grace_period (int, optional): The grace period in epochs of the ASHA scheduler.
+        gpu_per_trial (int, optional): The number of GPUs to allocate per trial.
+        max_samples (int, optional): The maximum number of trials to run.
+        **train_args (Any): Additional arguments to pass to the `train()` method.
+
+    Returns:
+        (ray.tune.ResultGrid): A ResultGrid containing the results of the hyperparameter search.
+
+    Examples:
+        >>> from ultralytics import YOLO
+        >>> model = YOLO("yolo11n.pt")  # Load a YOLO11n model
+
+        Start tuning hyperparameters for YOLO11n training on the COCO8 dataset
+        >>> result_grid = model.tune(data="coco8.yaml", use_ray=True)
+    """
+    LOGGER.info("💡 Learn about RayTune at https://docs.ultralytics.com/integrations/ray-tune")
+    if train_args is None:
+        train_args = {}
+
+    try:
+        checks.check_requirements("ray[tune]")
+
+        import ray
+        from ray import tune
+        from ray.air import RunConfig
+        from ray.air.integrations.wandb import WandbLoggerCallback
+        from ray.tune.schedulers import ASHAScheduler
+    except ImportError:
+        raise ModuleNotFoundError('Ray Tune required but not found. To install run: pip install "ray[tune]"')
+
+    try:
+        import wandb
+
+        assert hasattr(wandb, "__version__")
+    except (ImportError, AssertionError):
+        wandb = False
+
+    checks.check_version(ray.__version__, ">=2.0.0", "ray")
+    default_space = {
+        # 'optimizer': tune.choice(['SGD', 'Adam', 'AdamW', 'NAdam', 'RAdam', 'RMSProp']),
+        "lr0": tune.uniform(1e-5, 1e-1),
+        "lrf": tune.uniform(0.01, 1.0),  # final OneCycleLR learning rate (lr0 * lrf)
+        "momentum": tune.uniform(0.6, 0.98),  # SGD momentum/Adam beta1
+        "weight_decay": tune.uniform(0.0, 0.001),  # optimizer weight decay
+        "warmup_epochs": tune.uniform(0.0, 5.0),  # warmup epochs (fractions ok)
+        "warmup_momentum": tune.uniform(0.0, 0.95),  # warmup initial momentum
+        "box": tune.uniform(0.02, 0.2),  # box loss gain
+        "cls": tune.uniform(0.2, 4.0),  # cls loss gain (scale with pixels)
+        "hsv_h": tune.uniform(0.0, 0.1),  # image HSV-Hue augmentation (fraction)
+        "hsv_s": tune.uniform(0.0, 0.9),  # image HSV-Saturation augmentation (fraction)
+        "hsv_v": tune.uniform(0.0, 0.9),  # image HSV-Value augmentation (fraction)
+        "degrees": tune.uniform(0.0, 45.0),  # image rotation (+/- deg)
+        "translate": tune.uniform(0.0, 0.9),  # image translation (+/- fraction)
+        "scale": tune.uniform(0.0, 0.9),  # image scale (+/- gain)
+        "shear": tune.uniform(0.0, 10.0),  # image shear (+/- deg)
+        "perspective": tune.uniform(0.0, 0.001),  # image perspective (+/- fraction), range 0-0.001
+        "flipud": tune.uniform(0.0, 1.0),  # image flip up-down (probability)
+        "fliplr": tune.uniform(0.0, 1.0),  # image flip left-right (probability)
+        "bgr": tune.uniform(0.0, 1.0),  # image channel BGR (probability)
+        "mosaic": tune.uniform(0.0, 1.0),  # image mosaic (probability)
+        "mixup": tune.uniform(0.0, 1.0),  # image mixup (probability)
+        "cutmix": tune.uniform(0.0, 1.0),  # image cutmix (probability)
+        "copy_paste": tune.uniform(0.0, 1.0),  # segment copy-paste (probability)
+    }
+
+    # Put the model in ray store
+    task = model.task
+    model_in_store = ray.put(model)
+
+    def _tune(config):
+        """Train the YOLO model with the specified hyperparameters and return results."""
+        model_to_train = ray.get(model_in_store)  # get the model from ray store for tuning
+        model_to_train.reset_callbacks()
+        config.update(train_args)
+        results = model_to_train.train(**config)
+        return results.results_dict
+
+    # Get search space
+    if not space and not train_args.get("resume"):
+        space = default_space
+        LOGGER.warning("Search space not provided, using default search space.")
+
+    # Get dataset
+    data = train_args.get("data", TASK2DATA[task])
+    space["data"] = data
+    if "data" not in train_args:
+        LOGGER.warning(f'Data not provided, using default "data={data}".')
+
+    # Define the trainable function with allocated resources
+    trainable_with_resources = tune.with_resources(_tune, {"cpu": NUM_THREADS, "gpu": gpu_per_trial or 0})
+
+    # Define the ASHA scheduler for hyperparameter search
+    asha_scheduler = ASHAScheduler(
+        time_attr="epoch",
+        metric=TASK2METRIC[task],
+        mode="max",
+        max_t=train_args.get("epochs") or DEFAULT_CFG_DICT["epochs"] or 100,
+        grace_period=grace_period,
+        reduction_factor=3,
+    )
+
+    # Define the callbacks for the hyperparameter search
+    tuner_callbacks = [WandbLoggerCallback(project="YOLOv8-tune")] if wandb else []
+
+    # Create the Ray Tune hyperparameter search tuner
+    tune_dir = get_save_dir(
+        get_cfg(
+            DEFAULT_CFG,
+            {**train_args, **{"exist_ok": train_args.pop("resume", False)}},  # resume w/ same tune_dir
+        ),
+        name=train_args.pop("name", "tune"),  # runs/{task}/{tune_dir}
+    )  # must be absolute dir
+    tune_dir.mkdir(parents=True, exist_ok=True)
+    if tune.Tuner.can_restore(tune_dir):
+        LOGGER.info(f"{colorstr('Tuner: ')} Resuming tuning run {tune_dir}...")
+        tuner = tune.Tuner.restore(str(tune_dir), trainable=trainable_with_resources, resume_errored=True)
+    else:
+        tuner = tune.Tuner(
+            trainable_with_resources,
+            param_space=space,
+            tune_config=tune.TuneConfig(
+                scheduler=asha_scheduler,
+                num_samples=max_samples,
+                trial_name_creator=lambda trial: f"{trial.trainable_name}_{trial.trial_id}",
+                trial_dirname_creator=lambda trial: f"{trial.trainable_name}_{trial.trial_id}",
+            ),
+            run_config=RunConfig(callbacks=tuner_callbacks, storage_path=tune_dir.parent, name=tune_dir.name),
+        )
+
+    # Run the hyperparameter search
+    tuner.fit()
+
+    # Get the results of the hyperparameter search
+    results = tuner.get_results()
+
+    # Shut down Ray to clean up workers
+    ray.shutdown()
+
+    return results