From 5f7cf1ba16602b0e1f02448c10de475ebc6e356e Mon Sep 17 00:00:00 2001 From: GitHub Action Date: Fri, 29 Mar 2024 08:28:20 +0000 Subject: [PATCH] Update documentation --- test_build/_modules/otx/core/config/hpo.html | 4 +- test_build/_modules/otx/core/utils/cache.html | 5 +- test_build/_modules/otx/engine/engine.html | 71 +++++++----- test_build/_modules/otx/hpo/hpo_base.html | 13 --- test_build/_modules/otx/hpo/hpo_runner.html | 9 +- test_build/_modules/otx/hpo/hyperband.html | 5 +- .../additional_features/hpo.rst.txt | 107 +++++++++++++----- .../action/action_classification.rst.txt | 2 +- .../action/action_detection.rst.txt | 2 +- .../multi_class_classification.rst.txt | 2 +- .../object_detection/object_detection.rst.txt | 2 +- .../semantic_segmentation.rst.txt | 2 +- .../visual_prompting/fine_tuning.rst.txt | 2 +- .../explanation/additional_features/hpo.html | 99 +++++++++++----- .../action/action_classification.html | 21 ++++ .../algorithms/action/action_detection.html | 16 +++ .../multi_class_classification.html | 26 +++++ .../object_detection/object_detection.html | 46 ++++++++ .../segmentation/semantic_segmentation.html | 41 +++++++ .../visual_prompting/fine_tuning.html | 21 ++++ .../_autosummary/otx.core.config.hpo.html | 2 +- .../reference/_autosummary/otx.engine.html | 80 +++++++------ .../guide/reference/_autosummary/otx.hpo.html | 4 +- test_build/searchindex.js | 2 +- 24 files changed, 420 insertions(+), 164 deletions(-) diff --git a/test_build/_modules/otx/core/config/hpo.html b/test_build/_modules/otx/core/config/hpo.html index 249227f6baf..44a95ec8cd7 100644 --- a/test_build/_modules/otx/core/config/hpo.html +++ b/test_build/_modules/otx/core/config/hpo.html @@ -258,11 +258,9 @@

Source code for otx.core.config.hpo

     save_path: str | None = None
     mode: Literal["max", "min"] = "max"
     num_trials: int | None = None
-    num_workers: int = 1
+    num_workers: int = torch.cuda.device_count() if torch.cuda.is_available() else 1
     expected_time_ratio: int | float | None = 4
     maximum_resource: int | float | None = None
-    subset_ratio: float | int | None = None
-    min_subset_size: int = 500
     prior_hyper_parameters: dict | list[dict] | None = None
     acceptable_additional_time_ratio: float | int = 1.0
     minimum_resource: int | float | None = None
diff --git a/test_build/_modules/otx/core/utils/cache.html b/test_build/_modules/otx/core/utils/cache.html
index 381fe0ba8d4..5bbf36eea62 100644
--- a/test_build/_modules/otx/core/utils/cache.html
+++ b/test_build/_modules/otx/core/utils/cache.html
@@ -283,6 +283,7 @@ 
Source code for otx.core.utils.cache
 
     def __init__(self, **kwargs) -> None:
         self._cached_args = {**kwargs}
+        self.is_trainer_args_identical = False
 
 

 [docs]
@@ -309,7 +310,9 @@ 
Source code for otx.core.utils.cache
         Returns:
             bool: True if any of the cached arguments need to be updated, False otherwise.
         """
-        return any(key in self._cached_args and self._cached_args[key] != value for key, value in kwargs.items())

+        return not self.is_trainer_args_identical or any(
+            key in self._cached_args and self._cached_args[key] != value for key, value in kwargs.items()
+        )

 
 
     @property
diff --git a/test_build/_modules/otx/engine/engine.html b/test_build/_modules/otx/engine/engine.html
index 8236a7df375..2b7f98e269b 100644
--- a/test_build/_modules/otx/engine/engine.html
+++ b/test_build/_modules/otx/engine/engine.html
@@ -304,23 +304,26 @@ 
Source code for otx.engine.engine
     Example:
         The following examples show how to use the Engine class.
 
-        Auto-Configuration with data_root
-        >>> engine = Engine(
-        ...     data_root=<dataset/path>,
-        ... )
-
-        Create Engine with Custom OTXModel
-        >>> engine = Engine(
-        ...     data_root=<dataset/path>,
-        ...     model=OTXModel(...),
-        ...     checkpoint=<checkpoint/path>,
-        ... )
-
-        Create Engine with Custom OTXDataModule
-        >>> engine = Engine(
-        ...     model = OTXModel(...),
-        ...     datamodule = OTXDataModule(...),
-        ... )
+        Auto-Configuration with data_root::
+
+            engine = Engine(
+                data_root=<dataset/path>,
+            )
+
+        Create Engine with Custom OTXModel::
+
+            engine = Engine(
+                data_root=<dataset/path>,
+                model=OTXModel(...),
+                checkpoint=<checkpoint/path>,
+            )
+
+        Create Engine with Custom OTXDataModule::
+
+            engine = Engine(
+                model = OTXModel(...),
+                datamodule = OTXDataModule(...),
+            )
     """
 
     def __init__(
@@ -404,7 +407,7 @@ 
Source code for otx.engine.engine
         resume: bool = False,
         metric: Metric | MetricCallable | None = None,
         run_hpo: bool = False,
-        hpo_config: HpoConfig | None = None,
+        hpo_config: HpoConfig = HpoConfig(),  # noqa: B008 https://github.com/omni-us/jsonargparse/issues/423
         **kwargs,
     ) -> dict[str, Any]:
         """Trains the model using the provided LightningModule and OTXDataModule.
@@ -461,8 +464,6 @@ 
Source code for otx.engine.engine
         """
         metric = metric if metric is not None else self._auto_configurator.get_metric()
         if run_hpo:
-            if hpo_config is None:
-                hpo_config = HpoConfig()
             best_config, best_trial_weight = execute_hpo(engine=self, **locals())
             if best_config is not None:
                 update_hyper_parameter(self, best_config)
@@ -554,6 +555,10 @@ 
Source code for otx.engine.engine
         is_ir_ckpt = Path(str(checkpoint)).suffix in [".xml", ".onnx"]
         if is_ir_ckpt and not isinstance(model, OVModel):
             model = self._auto_configurator.get_ov_model(model_name=str(checkpoint), label_info=datamodule.label_info)
+            if self.device.accelerator != "cpu":
+                msg = "IR model supports inference only on CPU device. The device is changed automatic."
+                warn(msg, stacklevel=1)
+                self.device = DeviceType.cpu  # type: ignore[assignment]
 
         # NOTE: Re-initiate datamodule for OVModel as model API supports its own data pipeline.
         if isinstance(model, OVModel):
@@ -776,6 +781,7 @@ 
Source code for otx.engine.engine
             ...     datamodule=OTXDataModule(),
             ...     checkpoint=<checkpoint/path>,
             ... )
+
         CLI Usage:
             To optimize a model, run
                 ```python
@@ -1001,17 +1007,17 @@ 
Source code for otx.engine.engine
             ...     data_root=<dataset/path>,
             ... )
 
-            If you want to override configuration from default config
-            >>> overriding = {
-            ...     "data.config.train_subset.batch_size": 2,
-            ...     "data.config.test_subset.subset_name": "TESTING",
-            ... }
-            >>> engine = Engine(
-            ...     model_name="atss_mobilenetv2",
-            ...     task="DETECTION",
-            ...     data_root=<dataset/path>,
-            ...     **overriding,
-            ... )
+            If you want to override configuration from default config:
+                >>> overriding = {
+                ...     "data.config.train_subset.batch_size": 2,
+                ...     "data.config.test_subset.subset_name": "TESTING",
+                ... }
+                >>> engine = Engine(
+                ...     model_name="atss_mobilenetv2",
+                ...     task="DETECTION",
+                ...     data_root=<dataset/path>,
+                ...     **overriding,
+                ... )
         """
         default_config = DEFAULT_CONFIG_PER_TASK.get(task)
         model_path = str(default_config).split("/")
@@ -1047,6 +1053,7 @@ 
Source code for otx.engine.engine
     def work_dir(self, work_dir: PathLike) -> None:
         self._work_dir = work_dir
         self._cache.update(default_root_dir=work_dir)
+        self._cache.is_trainer_args_identical = False
 
     @property
     def device(self) -> DeviceConfig:
@@ -1059,6 +1066,7 @@ 
Source code for otx.engine.engine
             device = DeviceType.xpu
         self._device = DeviceConfig(accelerator=device)
         self._cache.update(accelerator=self._device.accelerator, devices=self._device.devices)
+        self._cache.is_trainer_args_identical = False
 
     @property
     def trainer(self) -> Trainer:
@@ -1088,6 +1096,7 @@ 
Source code for otx.engine.engine
 
             kwargs = self._cache.args
             self._trainer = Trainer(**kwargs)
+            self._cache.is_trainer_args_identical = True
             self._trainer.task = self.task
             self.work_dir = self._trainer.default_root_dir
 
diff --git a/test_build/_modules/otx/hpo/hpo_base.html b/test_build/_modules/otx/hpo/hpo_base.html
index 6c007c5ebab..8dd6e594519 100644
--- a/test_build/_modules/otx/hpo/hpo_base.html
+++ b/test_build/_modules/otx/hpo/hpo_base.html
@@ -279,10 +279,6 @@ 
Source code for otx.hpo.hpo_base
                                                             HPO use time about exepected_time_ratio *
                                                             train time after HPO times.
         maximum_resource (int | float | None, optional): Maximum resource to use for training each trial.
-        subset_ratio (float | int | None, optional): ratio to how many train dataset to use for each trial.
-                                     The lower value is, the faster the speed is.
-                                     But If it's too low, HPO can be unstable.
-        min_subset_size (int, optional) : Minimum size of subset. Default value is 500.
         resume (bool, optional): resume flag decide to use previous HPO results.
                                  If HPO completed, you can just use optimized hyper parameters.
                                  If HPO stopped in middle, you can resume in middle.
@@ -303,8 +299,6 @@ 
Source code for otx.hpo.hpo_base
         full_dataset_size: int = 0,
         expected_time_ratio: int | float | None = None,
         maximum_resource: int | float | None = None,
-        subset_ratio: float | int | None = None,
-        min_subset_size: int = 500,
         resume: bool = False,
         prior_hyper_parameters: dict | list[dict] | None = None,
         acceptable_additional_time_ratio: float | int = 1.0,
@@ -318,11 +312,6 @@ 
Source code for otx.hpo.hpo_base
         if num_trials is not None:
             check_positive(num_trials, "num_trials")
         check_positive(num_workers, "num_workers")
-        if subset_ratio is not None and not 0 < subset_ratio <= 1:
-            error_msg = (
-                f"subset_ratio should be greater than 0 and lesser than or equal to 1. Your value is {subset_ratio}"
-            )
-            raise ValueError(error_msg)
 
         if save_path is None:
             save_path = tempfile.mkdtemp(prefix="OTX-hpo-")
@@ -335,8 +324,6 @@ 
Source code for otx.hpo.hpo_base
         self.full_dataset_size = full_dataset_size
         self.expected_time_ratio = expected_time_ratio
         self.maximum_resource: int | float | None = maximum_resource
-        self.subset_ratio = subset_ratio
-        self.min_subset_size = min_subset_size
         self.resume = resume
         self.hpo_status: dict = {}
         self.acceptable_additional_time_ratio = acceptable_additional_time_ratio
diff --git a/test_build/_modules/otx/hpo/hpo_runner.html b/test_build/_modules/otx/hpo/hpo_runner.html
index 6eabc6a0c4e..8300d65e1ac 100644
--- a/test_build/_modules/otx/hpo/hpo_runner.html
+++ b/test_build/_modules/otx/hpo/hpo_runner.html
@@ -285,8 +285,6 @@ 
Source code for otx.hpo.hpo_runner
                                                    It's used for CPUResourceManager. Defaults to None.
         num_gpu_for_single_trial (int | None, optional): How many GPUs are used for a single trial.
                                                          It's used for GPUResourceManager. Defaults to None.
-        available_gpu (str | None, optional): How many GPUs are available. It's used for GPUResourceManager.
-                                              Defaults to None.
     """
 
     def __init__(
@@ -296,7 +294,6 @@ 
Source code for otx.hpo.hpo_runner
         resource_type: Literal["gpu", "cpu"] = "gpu",
         num_parallel_trial: int | None = None,
         num_gpu_for_single_trial: int | None = None,
-        available_gpu: str | None = None,
     ) -> None:
         self._hpo_algo = hpo_algo
         self._train_func = train_func
@@ -308,7 +305,6 @@ 
Source code for otx.hpo.hpo_runner
             resource_type,
             num_parallel_trial,
             num_gpu_for_single_trial,
-            available_gpu,
         )
         self._main_pid = os.getpid()
 
@@ -484,7 +480,6 @@ 
Source code for otx.hpo.hpo_runner
     resource_type: Literal["gpu", "cpu"] = "gpu",
     num_parallel_trial: int | None = None,
     num_gpu_for_single_trial: int | None = None,
-    available_gpu: str | None = None,
 ) -> None:
     """Run the HPO loop.
 
@@ -497,10 +492,8 @@ 
Source code for otx.hpo.hpo_runner
                                                    It's used for CPUResourceManager. Defaults to None.
         num_gpu_for_single_trial (int | None, optional): How many GPUs are used for a single trial.
                                                          It's used for GPUResourceManager. Defaults to None.
-        available_gpu (str | None, optional): How many GPUs are available. It's used for GPUResourceManager.
-                                              Defaults to None.
     """
-    hpo_loop = HpoLoop(hpo_algo, train_func, resource_type, num_parallel_trial, num_gpu_for_single_trial, available_gpu)
+    hpo_loop = HpoLoop(hpo_algo, train_func, resource_type, num_parallel_trial, num_gpu_for_single_trial)
     hpo_loop.run()

 
 

diff --git a/test_build/_modules/otx/hpo/hyperband.html b/test_build/_modules/otx/hpo/hyperband.html
index f5890f5431f..ac9a8d92b17 100644
--- a/test_build/_modules/otx/hpo/hyperband.html
+++ b/test_build/_modules/otx/hpo/hyperband.html
@@ -912,7 +912,7 @@ 
Source code for otx.hpo.hyperband
 
     def _make_trial(self, hyper_parameter: dict) -> AshaTrial:
         trial_id = self._get_new_trial_id()
-        trial = AshaTrial(trial_id, hyper_parameter, self._get_train_environment())
+        trial = AshaTrial(trial_id, hyper_parameter)
         self._trials[trial_id] = trial
         return trial
 
@@ -921,9 +921,6 @@ 
Source code for otx.hpo.hyperband
         self._next_trial_id += 1
         return str(trial_id)
 
-    def _get_train_environment(self) -> dict:
-        return {"subset_ratio": self.subset_ratio}
-
 

 [docs]
     def get_next_sample(self) -> AshaTrial | None:
diff --git a/test_build/_sources/guide/explanation/additional_features/hpo.rst.txt b/test_build/_sources/guide/explanation/additional_features/hpo.rst.txt
index a679cf84e38..0304ef8c02d 100644
--- a/test_build/_sources/guide/explanation/additional_features/hpo.rst.txt
+++ b/test_build/_sources/guide/explanation/additional_features/hpo.rst.txt
@@ -55,20 +55,52 @@ ASHA also includes a technique called Hyperband, which is used to determine how
 How to configure hyper-parameter optimization
 *********************************************
 
-You can configure HPO by modifying the ``hpo_config.yaml`` file. This file contains everything related to HPO, including the hyperparameters to optimize, the HPO algorithm, and more. The ``hpo_config.yaml`` file already exists with default values in the same directory where ``configs.yaml`` resides. Here is the default ``hpo_config.yaml`` file for classification:
+You can configure HPO using argument named *hpo_config*.
+It's *HpoConfig* dataclass which inlcudes hyperparameters to optimize, expected time ratio and more.
+If HPO is executed without confgiguration, default value of *HpoConfig* is used and learning rate and batch size are set as hyper parameterse to optimize.
+Here is how to configure using *hpo_config*.
+
+.. tab-set::
+
+    .. tab-item:: API
+
+        .. code-block:: python
+
+            from otx.engine import Engine
+            from otx.core.config.hpo import HpoConfig
+
+            hpo_config = HpoConfig(
+                search_space={
+                    "optimizer.lr" : {
+                        "type" : "uniform",
+                        "min" : 0.001,
+                        "max" : 0.1,
+                    }
+                },
+                expected_time_ratio=6,
+            )
+
+            engine = Engine(data_root="")
+            engine.train(run_hpo=True, hpo_config=hpo_config)
+
+    .. tab-item:: CLI
+
+        .. code-block:: shell
+
+            (otx) ...$ otx train ... --run_hpo True  --hpo_config.expected_time_ratio 6
 
-.. code-block::
 
-    Need to Update!
+As above, you can set HPO configuration by both API and CLI. Including ones here, you can configure various parameters of HPO.
+You can configure almost parameters using CLI except search space of hyper parameters.
+But search space requires dictionray format, so it can be set only on API.
+Here is explanation of all HPO configuration.
 
-As you can see, there are a few attributes required to run HPO.
-Fortunately, there are not many attributes, so it's not difficult to write your own ``hpo_config.yaml`` file. The more detailed description is as follows:
 
-- **hp_space** (*List[Dict[str, Any]]*, `required`) - Hyper parameter search space to find. It should be list of dictionary. Each dictionary has a hyperparameter name as the key and param_type and range as the values. You can optimize any learning parameters of each task.
+- **search_space** (*list[dict[str, Any]]*, `required`) - Hyper parameter search space to find. It should be list of dictionary. Each dictionary has a hyperparameter name as the key and param_type and range as the values. You can optimize any learning parameters of each task.
 
   - **Keys of each hyper parameter**
 
-    - **param_type** (*str*, `required`) : Hyper parameter search space type. It must be one of the following:
+    - **type** (*str*, `required`) : Hyper parameter search space type. It must be one of the following:
 
       - uniform : Samples a float value uniformly between the lower and upper bounds.
       - quniform : Samples a quantized float value uniformly between the lower and upper bounds.
@@ -76,40 +108,57 @@ Fortunately, there are not many attributes, so it's not difficult to write your
       - qloguniform : Samples a quantized float value after scaling the search space by logarithm scale.
       - choice : Samples a categorical value.
 
-    - **range** (*List[Any]*, `required`)
+    - **range** (*list*, `required`)
 
-      - uniform : List[Union[float, int]]
+      - uniform : list[float | int]
 
-        - min (*Union[float, int]*, `required`) : The lower bound of search space.
-        - max (*Union[float, int]*, `required`) : The upper bound of search space.
+        - min (*float | int*, `required`) : The lower bound of search space.
+        - max (*float | int*, `required`) : The upper bound of search space.
 
-      - quniform : List[Union[float, int]]
+      - quniform : list[float | int]
 
-        - min (*Union[float, int]*, `required`) : The lower bound of search space.
-        - max (*Union[float, int]*, `required`) : The upper bound of search space.
-        - step (*Union[float, int]*, `required`) : The unit value of search space.
+        - min (*float | int*, `required`) : The lower bound of search space.
+        - max (*float | int*, `required`) : The upper bound of search space.
+        - step (*float | int*, `required`) : The unit value of search space.
 
-      - loguniform : List[Union[float, int])
+      - loguniform : list[float | int]
 
-        - min (*Union[float, int]*, `required`) : The lower bound of search space.
-        - max (*Union[float, int]*, `required`) : The upper bound of search space.
-        - log_base (*Union[float, int]*, *default=10*) : The logarithm base.
+        - min (*float | int*, `required`) : The lower bound of search space.
+        - max (*float | int*, `required`) : The upper bound of search space.
+        - log_base (*float | int*, *default=10*) : The logarithm base.
 
       - qloguniform : List[Union[float, int]]
 
-        - min (*Union[float, int]*, `required`) : The lower bound of search space
-        - max (*Union[float, int]*, `required`) : The upper bound of search space
-        - step (*Union[float, int]*, `required`) : The unit value of search space
-        - log_base (*Union[float, int]*, *default=10*) : The logarithm base.
+        - min (*float | int*, `required`) : The lower bound of search space
+        - max (*float | int*, `required`) : The upper bound of search space
+        - step (*float | int*, `required`) : The unit value of search space
+        - log_base (*float | int*, *default=10*) : The logarithm base.
+
+      - choice : *list | tuple*
+
+        - vaule : values to choose as candidates.
+
+
+- **save_path** (*str*, *default='None'*) Path to save a HPO result.
+
+- **mode** (*str*, *default='max'*) - Optimization mode for the metric. It determines whether the metric should be maximized or minimized. The possible values are 'max' and 'min', respectively.
+
+- **num_workers** (*int*, *default=1*) How many trials will be executed in parallel.
+
+- **expected_time_ratio** (*int*, *default=4*) How many times to use for HPO compared to training time.
+
+- **maximum_resource** (*int*, *default=None*) - Maximum number of training epochs for each trial. When the training epochs reaches this value, the trial stop to train.
+
+- **minimum_resource** (*int*, *default=None*) - Minimum number of training epochs for each trial. Each trial will run at least this epochs, even if the performance of the model is not improving.
 
-      - choice : List[Any]
+- **prior_hyper_parameters** (*dict | list[dict]*, *default=None*) Hyper parameters to try first.
 
-        - vaule : values to be chosen from candidates.
+- **acceptable_additional_time_ratio** (*float | int*, *default=1.0*) How much ratio of additional time is acceptable.
 
-- **metric** (*str*, *default='mAP*') - Name of the metric that will be used to evaluate the performance of each trial. The hyperparameter optimization algorithm will aim to maximize or minimize this metric depending on the value of the mode hyperparameter. The default value is 'mAP'.
+- **reduction_factor** (*int*, *default=3*) How many trials to promote to next rung. Only top 1 / reduction_factor of rung trials can be promoted.
 
-- **mode** (*str*, *default='max*') - Optimization mode for the metric. It determines whether the metric should be maximized or minimized. The possible values are 'max' and 'min', respectively. The default value is 'max'.
+- **asynchronous_bracket** (*bool*, *default=True*) Whether to operate SHA asynchronously.
 
-- **maximum_resource** (*int*, *default=None*) - Maximum number of training epochs for each trial. When the number of training epochs reaches this value, the training of the trial will stop. The default value is None.
+- **asynchronous_sha** (*bool*, *default=True*) Whether SHAs(brackets) are running parallelly or not.
 
-- **minimum_resource** (*int*, *default=None*) - Minimum number of training epochs for each trial. Each trial will run for at least this many epochs, even if the performance of the model is not improving. The default value is None.
+**reduction_factor**, **asynchronous_bracket** and **asynchronous_sha** are HyperBand hyper parameters. If you want to know them more, please refer `ASHA `_.
diff --git a/test_build/_sources/guide/explanation/algorithms/action/action_classification.rst.txt b/test_build/_sources/guide/explanation/algorithms/action/action_classification.rst.txt
index c570da5b842..be1b81b2a72 100644
--- a/test_build/_sources/guide/explanation/algorithms/action/action_classification.rst.txt
+++ b/test_build/_sources/guide/explanation/algorithms/action/action_classification.rst.txt
@@ -30,7 +30,7 @@ Models
 Currently OpenVINO™ Training Extensions supports `X3D `_ and `MoViNet `_ for action classification.
 
 +----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+---------------------+-------------------------+
-| Recipe ID                                                                                                                                                                            | Name    | Complexity (GFLOPs) | Model size (MB)         |
+| Recipe ID                                                                                                                                                                              | Name    | Complexity (GFLOPs) | Model size (MB)         |
 +========================================================================================================================================================================================+=========+=====================+=========================+
 | `Custom_Action_Classification_X3D `_                         | X3D     | 2.49                | 3.79                    |
 +----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+---------------------+-------------------------+
diff --git a/test_build/_sources/guide/explanation/algorithms/action/action_detection.rst.txt b/test_build/_sources/guide/explanation/algorithms/action/action_detection.rst.txt
index b36b2bb0bec..1eae4fae0d3 100644
--- a/test_build/_sources/guide/explanation/algorithms/action/action_detection.rst.txt
+++ b/test_build/_sources/guide/explanation/algorithms/action/action_detection.rst.txt
@@ -27,7 +27,7 @@ Models
 We support the following ready-to-use model recipes for transfer learning:
 
 +-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------+---------------------+-------------------------+
-| Recipe ID                                                                                                                                                                             | Name          | Complexity (GFLOPs) | Model size (MB)         |
+| Recipe ID                                                                                                                                                                               | Name          | Complexity (GFLOPs) | Model size (MB)         |
 +=========================================================================================================================================================================================+===============+=====================+=========================+
 | `Custom_Action_Detection_X3D_FAST_RCNN `_                | x3d_fast_rcnn | 13.04               | 8.32                    |
 +-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------+---------------------+-------------------------+
diff --git a/test_build/_sources/guide/explanation/algorithms/classification/multi_class_classification.rst.txt b/test_build/_sources/guide/explanation/algorithms/classification/multi_class_classification.rst.txt
index 3600828570b..0966c047a15 100644
--- a/test_build/_sources/guide/explanation/algorithms/classification/multi_class_classification.rst.txt
+++ b/test_build/_sources/guide/explanation/algorithms/classification/multi_class_classification.rst.txt
@@ -56,7 +56,7 @@ Models
 We support the following ready-to-use model recipes:
 
 +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------+---------------------+-----------------+
-| Recipe ID                                                                                                                                                                                                      | Name                  | Complexity (GFLOPs) | Model size (MB) |
+| Recipe ID                                                                                                                                                                                                        | Name                  | Complexity (GFLOPs) | Model size (MB) |
 +==================================================================================================================================================================================================================+=======================+=====================+=================+
 | `Custom_Image_Classification_MobileNet-V3-large-1x `_             | MobileNet-V3-large-1x | 0.44                | 4.29            |
 +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------+---------------------+-----------------+
diff --git a/test_build/_sources/guide/explanation/algorithms/object_detection/object_detection.rst.txt b/test_build/_sources/guide/explanation/algorithms/object_detection/object_detection.rst.txt
index 6f13b3dd25d..3dd3fbc0349 100644
--- a/test_build/_sources/guide/explanation/algorithms/object_detection/object_detection.rst.txt
+++ b/test_build/_sources/guide/explanation/algorithms/object_detection/object_detection.rst.txt
@@ -57,7 +57,7 @@ Models
 We support the following ready-to-use model recipes:
 
 +------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------+---------------------+-----------------+
-| Recipe ID                                                                                                                                                | Name                | Complexity (GFLOPs) | Model size (MB) |
+| Recipe ID                                                                                                                                                  | Name                | Complexity (GFLOPs) | Model size (MB) |
 +============================================================================================================================================================+=====================+=====================+=================+
 | `Custom_Object_Detection_YOLOX `_            |      YOLOX-TINY     | 6.5                 | 20.4            |
 +------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------+---------------------+-----------------+
diff --git a/test_build/_sources/guide/explanation/algorithms/segmentation/semantic_segmentation.rst.txt b/test_build/_sources/guide/explanation/algorithms/segmentation/semantic_segmentation.rst.txt
index a7dc5e43206..e631301702d 100644
--- a/test_build/_sources/guide/explanation/algorithms/segmentation/semantic_segmentation.rst.txt
+++ b/test_build/_sources/guide/explanation/algorithms/segmentation/semantic_segmentation.rst.txt
@@ -56,7 +56,7 @@ Models
 We support the following ready-to-use model recipes:
 
 +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------+---------------------+-----------------+
-| Recipe ID                                                                                                                                                                          | Name                   | Complexity (GFLOPs) | Model size (MB) |
+| Recipe ID                                                                                                                                                                            | Name                   | Complexity (GFLOPs) | Model size (MB) |
 +======================================================================================================================================================================================+========================+=====================+=================+
 | `Custom_Semantic_Segmentation_Lite-HRNet-s-mod2_OCR `_    | Lite-HRNet-s-mod2      | 1.44                | 3.2             |
 +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------+---------------------+-----------------+
diff --git a/test_build/_sources/guide/explanation/algorithms/visual_prompting/fine_tuning.rst.txt b/test_build/_sources/guide/explanation/algorithms/visual_prompting/fine_tuning.rst.txt
index 7bc211dccdf..32550c0e479 100644
--- a/test_build/_sources/guide/explanation/algorithms/visual_prompting/fine_tuning.rst.txt
+++ b/test_build/_sources/guide/explanation/algorithms/visual_prompting/fine_tuning.rst.txt
@@ -58,7 +58,7 @@ Models
 We support the following model recipes in experimental phase:
 
 +------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------+---------------------+-----------------+
-|                                                                                        Recipe ID                                                         |     Name     | Complexity (GFLOPs) | Model size (MB) |
+|                                                                                        Recipe ID                                                           |     Name     | Complexity (GFLOPs) | Model size (MB) |
 +============================================================================================================================================================+==============+=====================+=================+
 | `Visual_Prompting_SAM_Tiny_ViT `_   | SAM_Tiny_ViT | 38.55               | 47              |
 +------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------+---------------------+-----------------+
diff --git a/test_build/guide/explanation/additional_features/hpo.html b/test_build/guide/explanation/additional_features/hpo.html
index 4ff302f60e5..e7e3a6be302 100644
--- a/test_build/guide/explanation/additional_features/hpo.html
+++ b/test_build/guide/explanation/additional_features/hpo.html
@@ -416,18 +416,51 @@ 
Algorithm
 
How to configure hyper-parameter optimization#

-
You can configure HPO by modifying the hpo_config.yaml file. This file contains everything related to HPO, including the hyperparameters to optimize, the HPO algorithm, and more. The hpo_config.yaml file already exists with default values in the same directory where configs.yaml resides. Here is the default hpo_config.yaml file for classification:

-
Need to Update!
+
You can configure HPO using argument named hpo_config.
+It’s HpoConfig dataclass which inlcudes hyperparameters to optimize, expected time ratio and more.
+If HPO is executed without confgiguration, default value of HpoConfig is used and learning rate and batch size are set as hyper parameterse to optimize.
+Here is how to configure using hpo_config.

+

+
+

+
from otx.engine import Engine
+from otx.core.config.hpo import HpoConfig
+
+hpo_config = HpoConfig(
+    search_space={
+        "optimizer.lr" : {
+            "type" : "uniform",
+            "min" : 0.001,
+            "max" : 0.1,
+        }
+    },
+    expected_time_ratio=6,
+)
+
+engine = Engine(data_root="<path_to_data_root>")
+engine.train(run_hpo=True, hpo_config=hpo_config)
+

+

+

+
+

+
(otx) ...$ otx train ... --run_hpo True  --hpo_config.expected_time_ratio 6
 

 

-
As you can see, there are a few attributes required to run HPO.
-Fortunately, there are not many attributes, so it’s not difficult to write your own hpo_config.yaml file. The more detailed description is as follows:

+

+

+
As above, you can set HPO configuration by both API and CLI. Including ones here, you can configure various parameters of HPO.
+You can configure almost parameters using CLI except search space of hyper parameters.
+But search space requires dictionray format, so it can be set only on API.
+Here is explanation of all HPO configuration.

 
    
-
  • hp_space (List[Dict[str, Any]], required) - Hyper parameter search space to find. It should be list of dictionary. Each dictionary has a hyperparameter name as the key and param_type and range as the values. You can optimize any learning parameters of each task.
    
+
  • search_space (list[dict[str, Any]], required) - Hyper parameter search space to find. It should be list of dictionary. Each dictionary has a hyperparameter name as the key and param_type and range as the values. You can optimize any learning parameters of each task.
    
 
      
 
    • Keys of each hyper parameter
      
 
        
-
      • param_type (str, required) : Hyper parameter search space type. It must be one of the following:
        
+
      • type (str, required) : Hyper parameter search space type. It must be one of the following:
        
 
          
 
        • uniform : Samples a float value uniformly between the lower and upper bounds.
          • 
 
        • quniform : Samples a quantized float value uniformly between the lower and upper bounds.
          • 
@@ -436,39 +469,39 @@ 
          How to configure hyper-parameter optimizationASHA.
          
 
 
 
diff --git a/test_build/guide/explanation/algorithms/action/action_classification.html b/test_build/guide/explanation/algorithms/action/action_classification.html
index 36a69282048..d1b507b05c6 100644
--- a/test_build/guide/explanation/algorithms/action/action_classification.html
+++ b/test_build/guide/explanation/algorithms/action/action_classification.html
@@ -393,6 +393,27 @@ 
          Dataset Format
 
          Models#
          
 
          Currently OpenVINO™ Training Extensions supports X3D and MoViNet for action classification.
          
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
          Recipe ID
          Name
          Complexity (GFLOPs)
          Model size (MB)
          Custom_Action_Classification_X3D
          X3D
          2.49
          3.79
          Custom_Action_Classificaiton_MoViNet
          MoViNet
          2.71
          3.10
          
 
          To see which models are available for the task, the following command can be executed:
          
 
          (otx) ...$ otx find --task ACTION_CLASSIFICATION
 
          
diff --git a/test_build/guide/explanation/algorithms/action/action_detection.html b/test_build/guide/explanation/algorithms/action/action_detection.html
index d185b9e7057..62ab4748ac4 100644
--- a/test_build/guide/explanation/algorithms/action/action_detection.html
+++ b/test_build/guide/explanation/algorithms/action/action_detection.html
@@ -391,6 +391,22 @@ 
          Dataset Format
 
          Models#
          
 
          We support the following ready-to-use model recipes for transfer learning:
          
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
          Recipe ID
          Name
          Complexity (GFLOPs)
          Model size (MB)
          Custom_Action_Detection_X3D_FAST_RCNN
          x3d_fast_rcnn
          13.04
          8.32
          
 
          To see which models are available for the task, the following command can be executed:
          
 
          (otx) ...$ otx find --task ACTION_DETECTION
 
          
diff --git a/test_build/guide/explanation/algorithms/classification/multi_class_classification.html b/test_build/guide/explanation/algorithms/classification/multi_class_classification.html
index cae1596c06d..2bea3952a28 100644
--- a/test_build/guide/explanation/algorithms/classification/multi_class_classification.html
+++ b/test_build/guide/explanation/algorithms/classification/multi_class_classification.html
@@ -424,6 +424,32 @@ 
          Dataset Format
 
          Models#
          
 
          We support the following ready-to-use model recipes:
          
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
          Recipe ID
          Name
          Complexity (GFLOPs)
          Model size (MB)
          Custom_Image_Classification_MobileNet-V3-large-1x
          MobileNet-V3-large-1x
          0.44
          4.29
          Custom_Image_Classification_EfficinetNet-B0
          EfficientNet-B0
          0.81
          4.09
          Custom_Image_Classification_EfficientNet-V2-S
          EfficientNet-V2-S
          5.76
          20.23
          
 
          EfficientNet-V2-S has more parameters and Flops and needs more time to train, meanwhile providing superior classification performance. MobileNet-V3-large-1x is the best choice when training time and computational cost are in priority, nevertheless, this recipe provides competitive accuracy as well.
 EfficientNet-B0 consumes more Flops compared to MobileNet, providing better performance on large datasets, but may be not so stable in case of a small amount of training data.
          
 
          To see which models are available for the task, the following command can be executed:
          
diff --git a/test_build/guide/explanation/algorithms/object_detection/object_detection.html b/test_build/guide/explanation/algorithms/object_detection/object_detection.html
index 518f9054a96..431cf75e4ff 100644
--- a/test_build/guide/explanation/algorithms/object_detection/object_detection.html
+++ b/test_build/guide/explanation/algorithms/object_detection/object_detection.html
@@ -421,6 +421,52 @@ 
          Dataset Format
 
          Models#
          
 
          We support the following ready-to-use model recipes:
          
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
          Recipe ID
          Name
          Complexity (GFLOPs)
          Model size (MB)
          Custom_Object_Detection_YOLOX
          YOLOX-TINY
          6.5
          20.4
          Object_Detection_YOLOX_S
          YOLOX_S
          33.51
          46.0
          Object_Detection_YOLOX_L
          YOLOX_L
          194.57
          207.0
          Object_Detection_YOLOX_X
          YOLOX_X
          352.42
          378.0
          Custom_Object_Detection_Gen3_SSD
          SSD
          9.4
          7.6
          Custom_Object_Detection_Gen3_ATSS
          MobileNetV2-ATSS
          20.6
          9.1
          Object_Detection_ResNeXt101_ATSS
          ResNeXt101-ATSS
          434.75
          344.0
          
 
          Above table can be found using the following command
          
 
          (otx) ...$ otx find --task DETECTION
 
          
diff --git a/test_build/guide/explanation/algorithms/segmentation/semantic_segmentation.html b/test_build/guide/explanation/algorithms/segmentation/semantic_segmentation.html
index 8020e0170a0..fb11e2816d0 100644
--- a/test_build/guide/explanation/algorithms/segmentation/semantic_segmentation.html
+++ b/test_build/guide/explanation/algorithms/segmentation/semantic_segmentation.html
@@ -415,6 +415,47 @@ 
          Dataset Format
 
          Models#
          
 
          We support the following ready-to-use model recipes:
          
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
          Recipe ID
          Name
          Complexity (GFLOPs)
          Model size (MB)
          Custom_Semantic_Segmentation_Lite-HRNet-s-mod2_OCR
          Lite-HRNet-s-mod2
          1.44
          3.2
          Custom_Semantic_Segmentation_Lite-HRNet-18-mod2_OCR
          Lite-HRNet-18-mod2
          2.82
          4.3
          Custom_Semantic_Segmentation_Lite-HRNet-x-mod3_OCR
          Lite-HRNet-x-mod3
          9.20
          5.7
          Custom_Semantic_Segmentation_SegNext_T
          SegNext-t
          6.07
          4.23
          Custom_Semantic_Segmentation_SegNext_S
          SegNext-s
          15.35
          13.9
          Custom_Semantic_Segmentation_SegNext_B
          SegNext-b
          32.08
          27.56
          
 
          All of these models are members of the same Lite-HRNet backbones family. They differ in the trade-off between accuracy and inference/training speed. Lite-HRNet-x-mod3 is the recipe with heavy-size architecture for accurate predictions but it requires long training.
 Whereas the Lite-HRNet-s-mod2 is the lightweight architecture for fast inference and training. It is the best choice for the scenario of a limited amount of data. The Lite-HRNet-18-mod2 model is the middle-sized architecture for the balance between fast inference and training time.
          
 
          Use SegNext model which can achieve superior perfomance while preserving fast inference and fast training.
          
diff --git a/test_build/guide/explanation/algorithms/visual_prompting/fine_tuning.html b/test_build/guide/explanation/algorithms/visual_prompting/fine_tuning.html
index 04c6b9145fe..40957945868 100644
--- a/test_build/guide/explanation/algorithms/visual_prompting/fine_tuning.html
+++ b/test_build/guide/explanation/algorithms/visual_prompting/fine_tuning.html
@@ -418,6 +418,27 @@ 
          Dataset Format
 
          Models#
          
 
          We support the following model recipes in experimental phase:
          
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
          Recipe ID
          Name
          Complexity (GFLOPs)
          Model size (MB)
          Visual_Prompting_SAM_Tiny_ViT
          SAM_Tiny_ViT
          38.55
          47
          Visual_Prompting_SAM_ViT_B
          SAM_ViT_B
          454.76
          363
          
 
          To check feasibility of SAM, we did experiments using three public datasets with each other domains: WGISD and Trashcan, and checked F1-score.
 We used sampled training data from Trashcan and full training data (=110) from WGISD.
          
 
diff --git a/test_build/guide/reference/_autosummary/otx.core.config.hpo.html b/test_build/guide/reference/_autosummary/otx.core.config.hpo.html
index 3c8a939f709..ac735bea679 100644
--- a/test_build/guide/reference/_autosummary/otx.core.config.hpo.html
+++ b/test_build/guide/reference/_autosummary/otx.core.config.hpo.html
@@ -387,7 +387,7 @@
 
          
 
          
 
          
-class otx.core.config.hpo.HpoConfig(search_space: dict[str, dict[str, Any]] | None = None, save_path: str | None = None, mode: Literal['max', 'min'] = 'max', num_trials: int | None = None, num_workers: int = 1, expected_time_ratio: int | float | None = 4, maximum_resource: int | float | None = None, subset_ratio: int | float | None = None, min_subset_size: int = 500, prior_hyper_parameters: dict | list[dict] | None = None, acceptable_additional_time_ratio: float | int = 1.0, minimum_resource: int | float | None = None, reduction_factor: int = 3, asynchronous_bracket: bool = True, asynchronous_sha: bool = True)[source]#
          
+class otx.core.config.hpo.HpoConfig(search_space: dict[str, dict[str, Any]] | None = None, save_path: str | None = None, mode: Literal['max', 'min'] = 'max', num_trials: int | None = None, num_workers: int = 1, expected_time_ratio: int | float | None = 4, maximum_resource: int | float | None = None, prior_hyper_parameters: dict | list[dict] | None = None, acceptable_additional_time_ratio: float | int = 1.0, minimum_resource: int | float | None = None, reduction_factor: int = 3, asynchronous_bracket: bool = True, asynchronous_sha: bool = True)[source]#
 
          Bases: object
          
 
          DTO for HPO configuration.
          
 
          
diff --git a/test_build/guide/reference/_autosummary/otx.engine.html b/test_build/guide/reference/_autosummary/otx.engine.html
index 872e6dfc068..f0619b87f68 100644
--- a/test_build/guide/reference/_autosummary/otx.engine.html
+++ b/test_build/guide/reference/_autosummary/otx.engine.html
@@ -393,21 +393,27 @@
 
          This class defines the Engine for OTX, which governs each step of the OTX workflow.
          
 
          Example
          
 
          The following examples show how to use the Engine class.
          
-
          Auto-Configuration with data_root
->>> engine = Engine(
-…     data_root=<dataset/path>,
-… )
          
-
          Create Engine with Custom OTXModel
->>> engine = Engine(
-…     data_root=<dataset/path>,
-…     model=OTXModel(…),
-…     checkpoint=<checkpoint/path>,
-… )
          
-
          Create Engine with Custom OTXDataModule
->>> engine = Engine(
-…     model = OTXModel(…),
-…     datamodule = OTXDataModule(…),
-… )
          
+
          Auto-Configuration with data_root:
          
+
          engine = Engine(
+    data_root=<dataset/path>,
+)
+
          
+
          
+
          Create Engine with Custom OTXModel:
          
+
          engine = Engine(
+    data_root=<dataset/path>,
+    model=OTXModel(...),
+    checkpoint=<checkpoint/path>,
+)
+
          
+
          
+
          Create Engine with Custom OTXDataModule:
          
+
          engine = Engine(
+    model = OTXModel(...),
+    datamodule = OTXDataModule(...),
+)
+
          
+
          
 
          Initializes the OTX Engine.
          
 
          
 
          Parameters:
          
@@ -591,17 +597,21 @@
 ... )
 

 

-
If you want to override configuration from default config
->>> overriding = {
-…     “data.config.train_subset.batch_size”: 2,
-…     “data.config.test_subset.subset_name”: “TESTING”,
-… }
->>> engine = Engine(
-…     model_name=”atss_mobilenetv2”,
-…     task=”DETECTION”,
-…     data_root=<dataset/path>,
-…     **overriding,
-… )

+

+
If you want to override configuration from default config:
>>> overriding = {
+...     "data.config.train_subset.batch_size": 2,
+...     "data.config.test_subset.subset_name": "TESTING",
+... }
+>>> engine = Engine(
+...     model_name="atss_mobilenetv2",
+...     task="DETECTION",
+...     data_root=<dataset/path>,
+...     **overriding,
+... )
+

+

+

+

 
 
 

@@ -638,14 +648,14 @@
 

 

 
CLI Usage:

-
To optimize a model, run
```python
+
To optimize a model, run
```python
 otx optimize

 

 
–checkpoint <CKPT_PATH, str>
 –model <CONFIG | CLASS_PATH_OR_NAME> –data_root <DATASET_PATH, str>
 –model.model_name=<PATH_TO_IR_XML, str>

 

-
```

+
```

 

 

 

@@ -686,13 +696,13 @@
 

 
CLI Usage:
    
 
  1. 
-
    you can pick a model.
    ```python
+
    you can pick a model.
    ```python
 otx predict
    
 
    
 
    –config <CONFIG_PATH> –data_root <DATASET_PATH, str>
 –checkpoint <CKPT_PATH, str>
    
 
    
-
    ```
    
+
    ```
    
 
    
 
    
 
    2. 
@@ -740,13 +750,13 @@
 
    
 
    CLI Usage:
      
 
    1. 
-
      you can pick a model.
      ```python
+
      you can pick a model.
      ```python
 otx test
      
 
      
 
      –model <CONFIG | CLASS_PATH_OR_NAME> –data_root <DATASET_PATH, str>
 –checkpoint <CKPT_PATH, str>
      
 
      
-
      ```
      
+
      ```
      
 
      
 
      
 
      2. 
@@ -764,7 +774,7 @@
 
 
      
 
      
-train(max_epochs: int = 10, seed: int | None = None, deterministic: bool | Literal['warn'] = False, precision: _PRECISION_INPUT | None = '32', val_check_interval: int | float | None = None, callbacks: list[Callback] | Callback | None = None, logger: Logger | Iterable[Logger] | bool | None = None, resume: bool = False, metric: Metric | MetricCallable | None = None, run_hpo: bool = False, hpo_config: HpoConfig | None = None, **kwargs) → dict[str, Any][source]#
      
+train(max_epochs: int = 10, seed: int | None = None, deterministic: bool | Literal['warn'] = False, precision: _PRECISION_INPUT | None = '32', val_check_interval: int | float | None = None, callbacks: list[Callback] | Callback | None = None, logger: Logger | Iterable[Logger] | bool | None = None, resume: bool = False, metric: Metric | MetricCallable | None = None, run_hpo: bool = False, hpo_config: HpoConfig = HpoConfig(search_space=None, save_path=None, mode='max', num_trials=None, num_workers=1, expected_time_ratio=4, maximum_resource=None, prior_hyper_parameters=None, acceptable_additional_time_ratio=1.0, minimum_resource=None, reduction_factor=3, asynchronous_bracket=True, asynchronous_sha=True), **kwargs) → dict[str, Any][source]#
 
      Trains the model using the provided LightningModule and OTXDataModule.
      
 
      
 
      Parameters:
      
@@ -823,13 +833,13 @@
 
      
 
 
    2. 
-
      Of course, you can override the various values with commands.
      ```python
+
      Of course, you can override the various values with commands.
      ```python
 otx train
      
 
      
 
      –data_root <DATASET_PATH>
 –max_epochs <EPOCHS, int> –checkpoint <CKPT_PATH, str>
      
 
      
-
      ```
      
+
      ```
      
 
      
 
      
 
      3. 
diff --git a/test_build/guide/reference/_autosummary/otx.hpo.html b/test_build/guide/reference/_autosummary/otx.hpo.html
index 456fdf55804..377e6cb489c 100644
--- a/test_build/guide/reference/_autosummary/otx.hpo.html
+++ b/test_build/guide/reference/_autosummary/otx.hpo.html
@@ -530,7 +530,7 @@
 
 
      
 
      
-otx.hpo.run_hpo_loop(hpo_algo: HpoBase, train_func: Callable, resource_type: Literal['gpu', 'cpu'] = 'gpu', num_parallel_trial: int | None = None, num_gpu_for_single_trial: int | None = None, available_gpu: str | None = None) → None[source]#
      
+otx.hpo.run_hpo_loop(hpo_algo: HpoBase, train_func: Callable, resource_type: Literal['gpu', 'cpu'] = 'gpu', num_parallel_trial: int | None = None, num_gpu_for_single_trial: int | None = None) → None[source]#
 
      Run the HPO loop.
      
 
      
 
      Parameters:
      
@@ -543,8 +543,6 @@
 It’s used for CPUResourceManager. Defaults to None.
      
 
    3. num_gpu_for_single_trial (int | None, optional) – How many GPUs are used for a single trial.
 It’s used for GPUResourceManager. Defaults to None.
      4. 
-
    4. available_gpu (str | None, optional) – How many GPUs are available. It’s used for GPUResourceManager.
-Defaults to None.
      5. 
 
 
      
 
      
diff --git a/test_build/searchindex.js b/test_build/searchindex.js
index 87dace0b6d2..28b4e7a72ce 100644
--- a/test_build/searchindex.js
+++ b/test_build/searchindex.js
@@ -1 +1 @@
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28, 38, 45, 94, 126, 133, 144, 146], "lower": [1, 4], "finish": [1, 4], "subprocess": 1, "load": [1, 5, 28, 40, 45, 51, 65, 74, 84, 95, 100, 102, 103, 106, 117, 126, 133, 146], "dure": [1, 2, 7, 9, 12, 21, 35, 43, 126, 136, 137], "while": [1, 7, 8, 12, 17, 21, 22, 26, 47, 51, 71, 74, 138, 139, 140, 141, 142, 144, 145, 146], "high": [1, 3, 7, 28, 47, 51, 79, 142, 146], "signific": 1, "cpu": [1, 19, 28, 51, 60, 127, 133, 138, 140, 141, 142, 144, 145, 146], "simplifi": [1, 26, 133], "manual": [1, 60, 144, 145, 146], "determin": [1, 4, 7, 12, 54, 64, 79], "optim": [1, 5, 9, 10, 12, 13, 15, 16, 19, 21, 22, 23, 26, 30, 34, 45, 50, 56, 79, 84, 95, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 126, 127, 133, 134, 139], "valu": [1, 4, 7, 8, 12, 21, 27, 28, 35, 49, 51, 52, 56, 59, 61, 71, 74, 79, 94, 108, 109, 110, 111, 112, 113, 114, 118, 124, 126, 127, 133, 134, 138, 139, 141, 142, 144, 145, 146], "statu": [1, 57, 127], "core": [1, 3, 7, 27, 138, 139, 140, 141, 142, 144, 145, 146], "config": 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21, 22, 23, 25, 26, 27, 28, 30, 45, 51, 53, 56, 59, 60, 61, 79, 126, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146], "batch": [2, 3, 7, 15, 23, 26, 28, 30, 43, 47, 51, 53, 65, 79, 86, 87, 94, 97, 98, 99, 101, 102, 103, 105, 106, 123, 133, 138, 139, 140, 141, 142, 144, 145, 146], "For": [2, 7, 8, 9, 10, 12, 13, 15, 16, 19, 21, 22, 23, 25, 27, 28, 29, 51, 60, 78, 91, 92, 93, 94, 102, 103, 134, 136, 137, 138, 139, 140, 141, 142, 144, 145, 146], "default": [2, 4, 7, 8, 12, 26, 27, 28, 35, 38, 39, 43, 47, 49, 51, 56, 60, 61, 62, 71, 74, 75, 76, 78, 79, 94, 101, 116, 122, 123, 126, 127, 133, 134, 136, 138, 139, 141, 142, 146], "squar": [2, 7, 12, 51], "root": [2, 7, 22, 26, 28, 61, 119, 126, 141, 142, 144, 146], "old": 2, "new": [2, 17, 23, 25, 26, 30, 42, 49, 74, 99, 133, 137], "ratio": [2, 7, 52, 71, 74], "class_incremental_sampl": 2, "classincrementalsampl": 2, "otxdataset": [2, 69], "class_incr_sampl": 2, "batch_siz": [2, 23, 28, 65, 126, 138, 139, 140, 141, 142, 144, 145, 146], 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51, 65, 71, 78, 79, 85, 86, 87, 91, 92, 93, 94, 99, 126, 127, 131, 136, 137, 138, 139, 141, 142, 144, 145, 146], "decod": [3, 23, 28, 48, 51, 54, 146], "imag": [3, 5, 8, 12, 13, 15, 16, 17, 19, 21, 22, 23, 25, 27, 28, 29, 30, 35, 37, 43, 47, 49, 51, 52, 53, 54, 65, 71, 74, 79, 94, 97, 98, 101, 102, 105, 109, 123, 133, 136, 137, 140, 141, 142, 144, 145, 146], "main": [3, 8, 16, 23, 25, 27, 28, 30, 55, 56, 131, 138, 139, 141, 142, 144, 145, 146], "size": [3, 8, 12, 15, 19, 21, 22, 23, 25, 28, 30, 35, 37, 43, 51, 52, 54, 65, 71, 74, 94, 99, 121, 123, 126, 133, 138, 139, 140, 141, 142, 144, 145, 146], "contain": [3, 4, 12, 28, 38, 40, 51, 56, 59, 61, 74, 79, 84, 97, 98, 99, 100, 101, 102, 105, 109, 116, 120, 126, 136, 137, 138, 140, 141, 142, 144, 146, 147], "resolut": [3, 7, 15, 16, 21, 22, 28, 47, 54, 136], "account": [3, 28, 30, 79], "non": [3, 13, 28, 38, 49, 59, 79, 84, 123], "neglig": [3, 28], "overhead": [3, 28], "pre": [3, 10, 12, 23, 25, 28, 29, 35, 51, 79], "One": 3, "maxim": [3, 4, 28, 79], "those": [3, 28], "case": [3, 4, 10, 15, 19, 21, 22, 27, 28, 48, 51, 60, 74, 79, 100, 137, 141, 142, 144, 146], "mem_cache_s": [3, 28, 65], "8gb": [3, 28], "hpo": [4, 26, 27, 30, 126], "even": [4, 19, 30, 51, 79, 99], "state": [4, 8, 35, 36, 37, 40, 45, 49, 51, 52, 53, 54, 78, 79, 82, 83, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 95, 99, 100], "art": 4, "shelf": [4, 30], "librari": [4, 12, 23, 25, 60, 65, 74, 114], "make": [4, 8, 12, 21, 26, 28, 30, 45, 74, 78, 99, 127, 133, 139, 141], "easier": 4, "easi": [4, 27, 51, 65, 139, 141], "interfac": [4, 7, 84, 133], "With": [4, 26, 28], "simpli": 4, "ad": [4, 12, 17, 19, 49, 59, 61], "constraint": [4, 133], "auto": [4, 5, 15, 26, 28, 30, 40, 65, 126, 133, 134, 138, 139, 140, 141, 142, 144, 145, 146], "intern": [4, 8, 16, 22, 25, 28, 35, 36, 37, 40, 49, 51, 52, 53, 54, 78, 79, 82, 83, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 95], "guarante": 4, "within": [4, 7, 12, 19, 21, 28, 62], "both": [4, 7, 12, 21, 23, 26, 27, 28, 35, 36, 37, 40, 49, 51, 52, 53, 54, 78, 79, 82, 83, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 95, 140, 146], "sequenti": 4, "parallel": [4, 127], "scalabl": 4, "differ": [4, 8, 12, 13, 15, 16, 17, 21, 22, 27, 40, 49, 54, 65, 78, 79, 100, 116, 134, 142, 144, 146], "multipl": [4, 7, 12, 26, 27, 30, 38, 53, 61, 78, 94, 99, 100, 133], "all": [4, 7, 12, 16, 17, 22, 26, 27, 28, 29, 30, 55, 61, 67, 78, 79, 85, 99, 109, 136, 138, 139, 140, 141, 142, 144, 145, 146], "kei": [4, 26, 52, 56, 59, 61, 120, 121], "includ": [4, 7, 9, 12, 16, 26, 29, 30, 43, 49, 59, 94, 128, 138, 139, 140, 141, 144, 145, 146], "usabl": 4, "By": [4, 7, 12, 26, 28, 29], "offer": [4, 12, 21, 23, 25, 26, 27, 30], "straightforward": 4, "intuit": [4, 26, 144, 145], "user": [4, 7, 8, 12, 19, 26, 28, 30, 126], "take": [4, 7, 8, 19, 65, 138, 140, 141, 142, 144, 145, 146], "advantag": [4, 26], "just": [4, 12, 22, 27, 28, 51, 133, 136, 141, 142, 146], "argument": [4, 27, 28, 38, 49, 56, 59, 60, 61, 117, 120, 126, 133], "below": [4, 7, 8, 9, 10, 13, 15, 16, 19, 21, 22, 23, 25, 27, 28, 29, 127, 134, 137, 138, 139, 140, 141, 142, 144, 145, 146], "run_hpo": [4, 27, 126], "abund": [4, 12], "asha": [4, 127], "choic": [4, 15, 21, 22, 30], "asynchron": [4, 84, 127], "success": 4, "halv": 4, "sha": [4, 127], "design": [4, 6, 8, 21], "comput": [4, 7, 8, 10, 12, 15, 17, 19, 21, 22, 23, 25, 30, 35, 43, 51, 78, 79, 84, 124, 140], "machin": [4, 8, 26, 28, 34], "involv": [4, 7, 8, 12, 17, 21], "trial": [4, 28, 127], "them": [4, 13, 16, 19, 26, 29, 47, 56, 99, 100], "metric": [4, 16, 19, 21, 22, 27, 56, 97, 98, 100, 101, 102, 103, 104, 105, 106, 126, 133, 138, 139, 140, 141, 142, 144, 145, 146], "mani": [4, 127], "short": [4, 61], "perform": [4, 6, 7, 9, 12, 13, 15, 19, 23, 28, 35, 84, 85, 94, 97, 98, 101, 102, 103, 105, 106, 126, 133, 138, 140, 141, 142, 144, 145, 146], "advanc": [4, 21, 26, 30], "next": [4, 59, 127], "round": 4, "subsequ": [4, 12, 21], "spent": [4, 26], "repeat": 4, "until": 4, "remain": [4, 84, 126, 136, 141], "becaus": [4, 23, 60, 74, 126, 141, 142, 144, 146], "allow": [4, 7, 8, 21, 26, 28, 30, 51, 56, 61, 126, 134, 136, 137, 138, 139, 140, 141, 142, 144, 145, 146], "independ": [4, 26, 51], "thei": [4, 19, 22, 26, 49, 79], "wait": [4, 29], "complet": [4, 28, 126], "befor": [4, 19, 26, 29, 38, 40, 45, 51, 74, 84, 106, 117, 123, 126], "techniqu": [4, 9, 12, 15, 19, 22, 23, 27], "call": [4, 19, 25, 26, 29, 35, 99, 100, 131, 134, 140, 144, 145], "hyperband": [4, 127], "much": [4, 26, 28, 30, 79, 140, 144, 145], "alloc": 4, "modifi": [4, 8, 19, 28, 40, 48, 61, 84, 116, 120], "hpo_config": [4, 126], "yaml": [4, 27, 28, 56, 64, 117, 126, 128, 130, 134, 136, 138, 139, 140, 141, 142, 144, 145, 146], "file": [4, 9, 16, 22, 27, 28, 29, 56, 61, 64, 71, 84, 90, 91, 92, 93, 94, 99, 100, 118, 124, 126, 128, 130, 131, 133, 134, 137, 138, 139, 140, 141, 142, 144, 145, 146], "everyth": [4, 141], "relat": [4, 7, 13, 15, 19, 21, 22, 26, 29, 34, 55, 60, 61, 69, 110, 116, 141, 142], "alreadi": [4, 27, 28, 51], 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"deploy-demo"]], "Export": [[137, "export"], [138, "export"], [140, "export"], [141, "export"], [142, "export"], [144, "export"], [145, "export"], [146, "export"]], "Deploy": [[137, "deploy"]], "Demonstration": [[137, "demonstration"]], "Action Classification model": [[138, "action-classification-model"]], "Setup virtual environment": [[138, "setup-virtual-environment"], [139, "setup-virtual-environment"], [141, "setup-virtual-environment"], [142, "setup-virtual-environment"], [144, "setup-virtual-environment"], [145, "setup-virtual-environment"], [146, "setup-virtual-environment"]], "Action Detection model": [[139, "action-detection-model"]], "Anomaly Detection Tutorial": [[140, "anomaly-detection-tutorial"]], "Setup the Virtual environment": [[140, "setup-the-virtual-environment"]], "Dataset Preparation": [[140, "dataset-preparation"]], "Segmentation and Classification": [[140, "segmentation-and-classification"]], "Classification  model": [[141, "classification-model"]], "Object 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"otx.core.data.OTXDataModule.hparams_initial"]], "otx.core.data": [[69, "module-otx.core.data"]], "predict_dataloader() (otx.core.data.otxdatamodule method)": [[69, "otx.core.data.OTXDataModule.predict_dataloader"]], "setup() (otx.core.data.otxdatamodule method)": [[69, "otx.core.data.OTXDataModule.setup"]], "teardown() (otx.core.data.otxdatamodule method)": [[69, "otx.core.data.OTXDataModule.teardown"]], "test_dataloader() (otx.core.data.otxdatamodule method)": [[69, "otx.core.data.OTXDataModule.test_dataloader"]], "train_dataloader() (otx.core.data.otxdatamodule method)": [[69, "otx.core.data.OTXDataModule.train_dataloader"]], "val_dataloader() (otx.core.data.otxdatamodule method)": [[69, "otx.core.data.OTXDataModule.val_dataloader"]], "otx.core.exporter": [[70, "module-otx.core.exporter"]], "otxmodelexporter (class in otx.core.exporter.base)": [[71, "otx.core.exporter.base.OTXModelExporter"]], "export() (otx.core.exporter.base.otxmodelexporter method)": [[71, "otx.core.exporter.base.OTXModelExporter.export"]], "otx.core.exporter.base": [[71, "module-otx.core.exporter.base"]], "to_exportable_code() (otx.core.exporter.base.otxmodelexporter method)": [[71, "otx.core.exporter.base.OTXModelExporter.to_exportable_code"]], "to_onnx() (otx.core.exporter.base.otxmodelexporter method)": [[71, "otx.core.exporter.base.OTXModelExporter.to_onnx"]], "to_openvino() (otx.core.exporter.base.otxmodelexporter method)": [[71, "otx.core.exporter.base.OTXModelExporter.to_openvino"]], "otx.core.exporter.exportable_code": [[72, "module-otx.core.exporter.exportable_code"]], "otx.core.exporter.exportable_code.demo": [[73, "module-otx.core.exporter.exportable_code.demo"]], "mmdeployexporter (class in otx.core.exporter.mmdeploy)": [[74, "otx.core.exporter.mmdeploy.MMdeployExporter"]], "cvt_torch2onnx_partition() (otx.core.exporter.mmdeploy.mmdeployexporter method)": [[74, "otx.core.exporter.mmdeploy.MMdeployExporter.cvt_torch2onnx_partition"]], 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"otxnativemodelexporter (class in otx.core.exporter.native)": [[75, "otx.core.exporter.native.OTXNativeModelExporter"]], "otx.core.exporter.native": [[75, "module-otx.core.exporter.native"]], "to_onnx() (otx.core.exporter.native.otxnativemodelexporter method)": [[75, "otx.core.exporter.native.OTXNativeModelExporter.to_onnx"]], "to_openvino() (otx.core.exporter.native.otxnativemodelexporter method)": [[75, "otx.core.exporter.native.OTXNativeModelExporter.to_openvino"]], "otxvisualpromptingmodelexporter (class in otx.core.exporter.visual_prompting)": [[76, "otx.core.exporter.visual_prompting.OTXVisualPromptingModelExporter"]], "export() (otx.core.exporter.visual_prompting.otxvisualpromptingmodelexporter method)": [[76, "otx.core.exporter.visual_prompting.OTXVisualPromptingModelExporter.export"]], "get_onnx_dummy_inputs() (otx.core.exporter.visual_prompting.otxvisualpromptingmodelexporter method)": [[76, "otx.core.exporter.visual_prompting.OTXVisualPromptingModelExporter.get_onnx_dummy_inputs"]], "otx.core.exporter.visual_prompting": [[76, "module-otx.core.exporter.visual_prompting"]], "to_onnx() (otx.core.exporter.visual_prompting.otxvisualpromptingmodelexporter method)": [[76, "otx.core.exporter.visual_prompting.OTXVisualPromptingModelExporter.to_onnx"]], "to_openvino() (otx.core.exporter.visual_prompting.otxvisualpromptingmodelexporter method)": [[76, "otx.core.exporter.visual_prompting.OTXVisualPromptingModelExporter.to_openvino"]], "otx.core.metrics": [[77, "module-otx.core.metrics"]], "accuracywithlabelgroup (class in otx.core.metrics.accuracy)": [[78, "otx.core.metrics.accuracy.AccuracywithLabelGroup"]], "hlabelaccuracy (class in otx.core.metrics.accuracy)": [[78, "otx.core.metrics.accuracy.HlabelAccuracy"]], "mixedhlabelaccuracy (class in otx.core.metrics.accuracy)": [[78, "otx.core.metrics.accuracy.MixedHLabelAccuracy"]], "multiclassaccuracywithlabelgroup (class in otx.core.metrics.accuracy)": [[78, "otx.core.metrics.accuracy.MulticlassAccuracywithLabelGroup"]], "multilabelaccuracywithlabelgroup (class in otx.core.metrics.accuracy)": [[78, "otx.core.metrics.accuracy.MultilabelAccuracywithLabelGroup"]], "namedconfusionmatrix (class in otx.core.metrics.accuracy)": [[78, "otx.core.metrics.accuracy.NamedConfusionMatrix"]], "col_names (otx.core.metrics.accuracy.namedconfusionmatrix property)": [[78, "otx.core.metrics.accuracy.NamedConfusionMatrix.col_names"]], "compute() (otx.core.metrics.accuracy.accuracywithlabelgroup method)": [[78, "otx.core.metrics.accuracy.AccuracywithLabelGroup.compute"]], "compute() (otx.core.metrics.accuracy.mixedhlabelaccuracy method)": [[78, "otx.core.metrics.accuracy.MixedHLabelAccuracy.compute"]], "label_info (otx.core.metrics.accuracy.accuracywithlabelgroup property)": [[78, "otx.core.metrics.accuracy.AccuracywithLabelGroup.label_info"]], "otx.core.metrics.accuracy": [[78, "module-otx.core.metrics.accuracy"]], "row_names (otx.core.metrics.accuracy.namedconfusionmatrix property)": [[78, "otx.core.metrics.accuracy.NamedConfusionMatrix.row_names"]], "update() (otx.core.metrics.accuracy.accuracywithlabelgroup method)": [[78, "otx.core.metrics.accuracy.AccuracywithLabelGroup.update"]], "update() (otx.core.metrics.accuracy.mixedhlabelaccuracy method)": [[78, "otx.core.metrics.accuracy.MixedHLabelAccuracy.update"]], "fmeasure (class in otx.core.metrics.fmeasure)": [[79, "otx.core.metrics.fmeasure.FMeasure"]], "best_confidence_threshold (otx.core.metrics.fmeasure.fmeasure property)": [[79, "otx.core.metrics.fmeasure.FMeasure.best_confidence_threshold"]], "best_nms_threshold (otx.core.metrics.fmeasure.fmeasure property)": [[79, "otx.core.metrics.fmeasure.FMeasure.best_nms_threshold"]], "bounding_box_intersection_over_union() (in module otx.core.metrics.fmeasure)": [[79, "otx.core.metrics.fmeasure.bounding_box_intersection_over_union"]], "classes (otx.core.metrics.fmeasure.fmeasure property)": [[79, 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"otx.core.model.entity.classification.OVHlabelClassificationModel.set_hlabel_info"]], "explainableotxdetmodel (class in otx.core.model.entity.detection)": [[86, "otx.core.model.entity.detection.ExplainableOTXDetModel"]], "mmdetcompatiblemodel (class in otx.core.model.entity.detection)": [[86, "otx.core.model.entity.detection.MMDetCompatibleModel"]], "otxdetectionmodel (class in otx.core.model.entity.detection)": [[86, "otx.core.model.entity.detection.OTXDetectionModel"]], "ovdetectionmodel (class in otx.core.model.entity.detection)": [[86, "otx.core.model.entity.detection.OVDetectionModel"]], "forward_explain() (otx.core.model.entity.detection.explainableotxdetmodel method)": [[86, "otx.core.model.entity.detection.ExplainableOTXDetModel.forward_explain"]], "forward_tiles() (otx.core.model.entity.detection.otxdetectionmodel method)": [[86, "otx.core.model.entity.detection.OTXDetectionModel.forward_tiles"]], "get_explain_fn() (otx.core.model.entity.detection.explainableotxdetmodel method)": [[86, "otx.core.model.entity.detection.ExplainableOTXDetModel.get_explain_fn"]], "get_num_anchors() (otx.core.model.entity.detection.explainableotxdetmodel method)": [[86, "otx.core.model.entity.detection.ExplainableOTXDetModel.get_num_anchors"]], "otx.core.model.entity.detection": [[86, "module-otx.core.model.entity.detection"]], "explainableotxinstancesegmodel (class in otx.core.model.entity.instance_segmentation)": [[87, "otx.core.model.entity.instance_segmentation.ExplainableOTXInstanceSegModel"]], "mmdetinstancesegcompatiblemodel (class in otx.core.model.entity.instance_segmentation)": [[87, "otx.core.model.entity.instance_segmentation.MMDetInstanceSegCompatibleModel"]], "otxinstancesegmodel (class in otx.core.model.entity.instance_segmentation)": [[87, "otx.core.model.entity.instance_segmentation.OTXInstanceSegModel"]], "ovinstancesegmentationmodel (class in otx.core.model.entity.instance_segmentation)": [[87, "otx.core.model.entity.instance_segmentation.OVInstanceSegmentationModel"]], "forward_explain() (otx.core.model.entity.instance_segmentation.explainableotxinstancesegmodel method)": [[87, "otx.core.model.entity.instance_segmentation.ExplainableOTXInstanceSegModel.forward_explain"]], "forward_tiles() (otx.core.model.entity.instance_segmentation.otxinstancesegmodel method)": [[87, "otx.core.model.entity.instance_segmentation.OTXInstanceSegModel.forward_tiles"]], "get_explain_fn() (otx.core.model.entity.instance_segmentation.explainableotxinstancesegmodel method)": [[87, "otx.core.model.entity.instance_segmentation.ExplainableOTXInstanceSegModel.get_explain_fn"]], "otx.core.model.entity.instance_segmentation": [[87, "module-otx.core.model.entity.instance_segmentation"]], "mmdetrotateddetmodel (class in otx.core.model.entity.rotated_detection)": [[88, "otx.core.model.entity.rotated_detection.MMDetRotatedDetModel"]], "otxrotateddetmodel (class in otx.core.model.entity.rotated_detection)": 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13, 15, 16, 19, 22, 28, 30, 38, 51, 65, 71, 78, 79, 85, 86, 87, 91, 92, 93, 94, 99, 126, 127, 131, 136, 137, 138, 139, 141, 142, 144, 145, 146], "decod": [3, 23, 28, 48, 51, 54, 146], "imag": [3, 5, 8, 12, 13, 15, 16, 17, 19, 21, 22, 23, 25, 27, 28, 29, 30, 35, 37, 43, 47, 49, 51, 52, 53, 54, 65, 71, 74, 79, 94, 97, 98, 101, 102, 105, 109, 123, 133, 136, 137, 140, 141, 142, 144, 145, 146], "main": [3, 8, 16, 23, 25, 27, 28, 30, 55, 56, 131, 138, 139, 141, 142, 144, 145, 146], "size": [3, 4, 8, 9, 10, 12, 15, 19, 21, 22, 23, 25, 28, 30, 35, 37, 43, 51, 52, 54, 65, 71, 74, 94, 99, 121, 123, 126, 133, 138, 139, 140, 141, 142, 144, 145, 146], "contain": [3, 12, 28, 38, 40, 51, 56, 59, 61, 74, 79, 84, 97, 98, 99, 100, 101, 102, 105, 109, 116, 120, 126, 136, 137, 138, 140, 141, 142, 144, 146, 147], "resolut": [3, 7, 15, 16, 21, 22, 28, 47, 54, 136], "account": [3, 28, 30, 79], "non": [3, 13, 28, 38, 49, 59, 79, 84, 123], "neglig": [3, 28], "overhead": [3, 28], "pre": [3, 10, 12, 23, 25, 28, 29, 35, 51, 79], "One": 3, "maxim": [3, 4, 28, 79], "those": [3, 28], "case": [3, 4, 10, 15, 19, 21, 22, 27, 28, 48, 51, 60, 74, 79, 100, 137, 141, 142, 144, 146], "mem_cache_s": [3, 28, 65], "8gb": [3, 28], "hpo": [4, 26, 27, 30, 126], "even": [4, 19, 30, 51, 79, 99], "state": [4, 8, 35, 36, 37, 40, 45, 49, 51, 52, 53, 54, 78, 79, 82, 83, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 95, 99, 100], "art": 4, "shelf": [4, 30], "librari": [4, 12, 23, 25, 60, 65, 74, 114], "make": [4, 8, 12, 21, 26, 28, 30, 45, 74, 78, 99, 127, 133, 139, 141], "easier": 4, "easi": [4, 27, 51, 65, 139, 141], "interfac": [4, 7, 84, 133], "With": [4, 26, 28], "simpli": 4, "ad": [4, 12, 17, 19, 49, 59, 61], "constraint": [4, 133], "auto": [4, 5, 15, 26, 28, 30, 40, 65, 126, 133, 134, 138, 139, 140, 141, 142, 144, 145, 146], "intern": [4, 8, 16, 22, 25, 28, 35, 36, 37, 40, 49, 51, 52, 53, 54, 78, 79, 82, 83, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 95], "guarante": 4, "within": [4, 7, 12, 19, 21, 28, 62], "both": [4, 7, 12, 21, 23, 26, 27, 28, 35, 36, 37, 40, 49, 51, 52, 53, 54, 78, 79, 82, 83, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 95, 140, 146], "sequenti": 4, "parallel": [4, 127], "scalabl": 4, "differ": [4, 8, 12, 13, 15, 16, 17, 21, 22, 27, 40, 49, 54, 65, 78, 79, 100, 116, 134, 142, 144, 146], "multipl": [4, 7, 12, 26, 27, 30, 38, 53, 61, 78, 94, 99, 100, 133], "all": [4, 7, 12, 16, 17, 22, 26, 27, 28, 29, 30, 55, 61, 67, 78, 79, 85, 99, 109, 136, 138, 139, 140, 141, 142, 144, 145, 146], "kei": [4, 26, 52, 56, 59, 61, 120, 121], "includ": [4, 7, 9, 12, 16, 26, 29, 30, 43, 49, 59, 94, 128, 138, 139, 140, 141, 144, 145, 146], "usabl": 4, "By": [4, 7, 12, 26, 28, 29], "offer": [4, 12, 21, 23, 25, 26, 27, 30], "straightforward": 4, "intuit": [4, 26, 144, 145], "user": [4, 7, 8, 12, 19, 26, 28, 30, 126], "take": [4, 7, 8, 19, 65, 138, 140, 141, 142, 144, 145, 146], "advantag": [4, 26], "just": [4, 12, 22, 27, 28, 51, 133, 136, 141, 142, 146], "argument": [4, 27, 28, 38, 49, 56, 59, 60, 61, 117, 120, 126, 133], "below": [4, 7, 8, 9, 10, 13, 15, 16, 19, 21, 22, 23, 25, 27, 28, 29, 127, 134, 137, 138, 139, 140, 141, 142, 144, 145, 146], "run_hpo": [4, 27, 126], "abund": [4, 12], "asha": [4, 127], "choic": [4, 15, 21, 22, 30], "asynchron": [4, 84, 127], "success": 4, "halv": 4, "sha": [4, 127], "design": [4, 6, 8, 21], "comput": [4, 7, 8, 10, 12, 15, 17, 19, 21, 22, 23, 25, 30, 35, 43, 51, 78, 79, 84, 124, 140], "machin": [4, 8, 26, 28, 34], "involv": [4, 7, 8, 12, 17, 21], "trial": [4, 28, 127], "them": [4, 13, 16, 19, 26, 29, 47, 56, 99, 100], "metric": [4, 16, 19, 21, 22, 27, 56, 97, 98, 100, 101, 102, 103, 104, 105, 106, 126, 133, 138, 139, 140, 141, 142, 144, 145, 146], "mani": [4, 127], "short": [4, 61], "perform": [4, 6, 7, 9, 12, 13, 15, 19, 23, 28, 35, 84, 85, 94, 97, 98, 101, 102, 103, 105, 106, 126, 133, 138, 140, 141, 142, 144, 145, 146], "advanc": [4, 21, 26, 30], "next": [4, 59, 127], "round": 4, "subsequ": [4, 12, 21], "spent": [4, 26], "repeat": 4, "until": 4, "remain": [4, 84, 126, 136, 141], "becaus": [4, 23, 60, 74, 126, 141, 142, 144, 146], "allow": [4, 7, 8, 21, 26, 28, 30, 51, 56, 61, 126, 134, 136, 137, 138, 139, 140, 141, 142, 144, 145, 146], "independ": [4, 26, 51], "thei": [4, 19, 22, 26, 49, 79], "wait": [4, 29], "complet": [4, 28, 126], "befor": [4, 19, 26, 29, 38, 40, 45, 51, 74, 84, 106, 117, 123, 126], "techniqu": [4, 9, 12, 15, 19, 22, 23, 27], "call": [4, 19, 25, 26, 29, 35, 99, 100, 131, 134, 140, 144, 145], "hyperband": [4, 127], "much": [4, 26, 28, 30, 79, 140, 144, 145], "alloc": 4, "hpo_config": [4, 126], "hpoconfig": [4, 68, 126], "dataclass": [4, 26, 65], "inlcud": 4, "expect": [4, 7, 19, 29, 37, 138, 139, 141, 142, 144, 145, 146], "confgigur": 4, "parameters": 4, "here": [4, 7, 19, 26, 128, 134, 136, 138, 139, 140, 141, 142, 144, 145, 146], "search_spac": [4, 68, 126], "lr": [4, 19, 23, 27, 52, 134], "uniform": 4, "min": [4, 68], "0": [4, 7, 10, 12, 15, 19, 21, 23, 25, 26, 31, 35, 37, 38, 39, 40, 43, 45, 48, 49, 50, 51, 52, 54, 59, 60, 65, 68, 71, 74, 75, 76, 78, 79, 84, 85, 92, 93, 94, 95, 99, 101, 117, 121, 123, 125, 126, 134, 137, 138, 139, 140, 141, 142, 144, 145, 146], "001": [4, 140], "max": [4, 27, 28, 68, 79, 94, 126, 133, 134], "1": [4, 7, 9, 12, 15, 16, 19, 22, 23, 26, 31, 35, 37, 38, 39, 43, 49, 51, 52, 54, 59, 60, 65, 66, 68, 71, 74, 75, 76, 78, 79, 84, 85, 92, 93, 95, 116, 117, 121, 124, 126, 127, 134, 136, 137, 138, 139, 140, 141, 142, 144, 145, 146], "expected_time_ratio": [4, 68, 126], "6": [4, 12, 19, 22, 23, 26, 29, 60, 121, 133, 136, 137, 138], "As": [4, 7, 12, 26, 137], "abov": [4, 12, 13, 16, 19, 21, 26, 29, 134, 138, 139, 140, 141, 142, 144, 145, 146], "ones": [4, 27, 61, 74, 84], "variou": [4, 8, 12, 25, 26, 28, 30, 126], "almost": [4, 43, 131], "except": [4, 19, 116, 131], "space": [4, 8], "But": [4, 100, 146], "dictionrai": 4, "so": [4, 8, 15, 19, 23, 25, 27, 29, 30, 40, 65, 69, 78, 79, 84, 126, 142, 146], "explan": [4, 8, 30, 133, 136, 138, 139, 140, 141, 142, 144, 145, 146], "list": [4, 27, 28, 34, 38, 42, 43, 47, 48, 49, 51, 52, 56, 59, 60, 61, 64, 65, 68, 71, 74, 75, 76, 78, 79, 84, 86, 92, 94, 95, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 111, 118, 120, 121, 123, 124, 125, 126, 127, 133, 134, 137, 138, 139, 141, 142, 144, 145, 146], "dict": [4, 34, 35, 38, 40, 42, 43, 45, 47, 48, 51, 53, 56, 59, 60, 61, 65, 68, 71, 74, 75, 76, 78, 79, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 95, 99, 100, 102, 103, 116, 117, 118, 120, 121, 123, 124, 125, 126, 127, 138, 139, 141, 142, 144, 145, 146], "str": [4, 28, 34, 35, 36, 37, 38, 39, 40, 43, 45, 47, 48, 49, 51, 52, 54, 56, 57, 58, 59, 60, 61, 62, 64, 65, 68, 69, 71, 74, 75, 76, 78, 79, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 95, 99, 100, 102, 103, 108, 109, 110, 111, 112, 113, 114, 116, 117, 119, 120, 122, 124, 125, 126, 127], "ani": [4, 15, 16, 19, 21, 25, 26, 34, 35, 40, 45, 49, 51, 56, 59, 61, 64, 65, 68, 75, 76, 78, 79, 82, 84, 85, 86, 87, 88, 89, 95, 99, 100, 102, 103, 104, 117, 118, 124, 125, 126, 127, 133], "dictionari": [4, 26, 38, 40, 43, 47, 51, 56, 59, 61, 65, 79, 84, 99, 100, 116, 118, 120, 121, 124, 126], "ha": [4, 12, 15, 23, 26, 28, 43, 51, 54, 60, 69, 138, 139, 140, 141, 142, 144, 145, 146], "param_typ": 4, "rang": [4, 19, 51, 78], "must": [4, 26, 49, 51], "float": [4, 7, 35, 38, 39, 43, 45, 47, 49, 51, 52, 65, 68, 71, 74, 75, 76, 78, 79, 91, 92, 94, 95, 123, 125, 126, 127, 138, 141, 142, 146], "uniformli": 4, "upper": 4, "bound": [4, 7, 12, 19, 21, 23, 25, 30, 79, 104, 140], "quniform": 4, "quantiz": [4, 23, 25, 84, 95, 126, 140], "loguniform": 4, "scale": [4, 12, 16, 19, 23, 34, 38, 43, 64, 65, 74, 127, 134], "logarithm": 4, "qloguniform": 4, "categor": 4, "int": [4, 28, 34, 35, 36, 37, 38, 39, 40, 43, 45, 47, 48, 49, 51, 52, 53, 54, 57, 59, 60, 64, 65, 66, 68, 71, 74, 75, 76, 78, 79, 82, 83, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 95, 97, 98, 99, 100, 101, 102, 103, 105, 106, 116, 118, 121, 123, 124, 125, 126, 127, 131], "step": [4, 7, 9, 12, 19, 25, 29, 45, 59, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 126, 136, 137, 138, 139, 141, 142, 144, 145, 146], "unit": [4, 29], "log_bas": 4, "10": [4, 7, 9, 12, 23, 27, 28, 29, 60, 61, 126, 133, 134, 138, 140, 142, 144, 145, 146], "union": [4, 51, 79], "tupl": [4, 35, 37, 38, 43, 51, 52, 53, 54, 56, 60, 61, 64, 65, 71, 74, 75, 76, 78, 79, 91, 92, 93, 94, 95, 97, 98, 99, 100, 101, 102, 105, 118, 123, 125], "vaul": 4, "candid": [4, 25], "save_path": [4, 68, 126], "none": [4, 12, 16, 28, 34, 35, 36, 37, 38, 39, 40, 42, 43, 45, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 61, 62, 64, 65, 68, 69, 71, 74, 75, 76, 78, 79, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 95, 97, 98, 99, 100, 101, 102, 103, 105, 106, 116, 117, 120, 123, 124, 125, 126, 127, 131], "path": [4, 6, 8, 12, 22, 25, 26, 51, 56, 58, 61, 62, 71, 74, 75, 76, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 95, 99, 100, 119, 120, 126, 134, 136, 137, 138, 139, 140, 141, 142, 144, 145, 146], "result": [4, 7, 8, 12, 15, 16, 21, 25, 26, 28, 29, 38, 51, 71, 78, 79, 84, 126, 127, 137, 138, 139, 140, 141, 142, 144, 145, 146], "whether": [4, 12, 16, 26, 29, 38, 40, 47, 51, 53, 56, 60, 61, 62, 64, 71, 74, 75, 76, 78, 79, 94, 126, 127], "minim": [4, 21, 26, 29, 137], "respect": 4, "num_work": [4, 65, 68, 124, 126, 133, 146], "4": [4, 10, 12, 15, 19, 21, 22, 26, 27, 28, 31, 43, 52, 68, 116, 126, 134, 136, 137, 138, 139, 140, 141, 142, 144, 145, 146], "maximum_resourc": [4, 68, 126], "reach": 4, "minimum_resourc": [4, 68, 126, 127], "minimum": [4, 79, 127, 137], "least": [4, 140], "improv": [4, 5, 12, 19, 21, 22, 23, 30, 133], "prior_hyper_paramet": [4, 68, 126], "try": [4, 23, 25, 26, 29], "acceptable_additional_time_ratio": [4, 68, 126], "addit": [4, 7, 12, 15, 19, 21, 22, 23, 26, 27, 28, 29, 30, 49, 56, 59, 74, 79, 120, 126, 136, 138, 139, 140, 141, 144, 145, 146], "accept": [4, 38, 51, 65, 117], "reduction_factor": [4, 68, 126, 127], "3": [4, 7, 9, 12, 13, 19, 22, 26, 31, 47, 51, 52, 53, 60, 61, 68, 71, 74, 95, 121, 126, 127, 134, 136, 137, 138, 139, 140, 141, 142, 144, 145, 146], "promot": [4, 127], "rung": [4, 127], "top": [4, 9, 15, 51, 71, 74, 76, 127], "asynchronous_bracket": [4, 68, 126, 127], "bool": [4, 34, 35, 36, 38, 40, 42, 43, 47, 48, 50, 51, 52, 53, 56, 57, 60, 61, 62, 64, 65, 67, 68, 71, 74, 75, 76, 78, 79, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 95, 97, 98, 100, 101, 102, 103, 104, 105, 106, 117, 124, 125, 126, 127], "oper": [4, 7, 13, 35, 49, 55, 84, 121, 126, 127, 133], "asynchronous_sha": [4, 68, 126, 127], "bracket": [4, 127], "parallelli": [4, 127], "want": [4, 6, 26, 27, 28, 29, 49, 61, 126, 134, 138, 139, 141, 142, 144, 145, 146], "know": [4, 6, 28, 137], "pleas": [4, 6, 7, 10, 13, 15, 16, 19, 21, 22, 26, 28, 29, 82, 83, 85, 86, 89, 91, 92, 93, 94, 127, 137, 138, 140, 141, 142, 144, 145, 146], "refer": [4, 6, 7, 8, 9, 10, 13, 15, 16, 19, 21, 22, 25, 26, 27, 28, 29, 30, 43, 51, 53, 54, 95, 106, 127, 137, 138, 139, 140, 141, 142, 144, 145, 146], "hyperparamet": [5, 15, 19, 21, 22, 30, 69, 127, 133], "configur": [5, 6, 12, 26, 28, 30, 35, 38, 47, 51, 53, 56, 61, 65, 66, 67, 68, 74, 82, 83, 85, 86, 89, 91, 92, 93, 94, 99, 100, 101, 102, 103, 105, 106, 120, 126, 127, 133, 135, 138, 139, 140, 141, 142, 144, 145, 146], "adapt": [5, 7, 12, 15, 16, 19, 21, 22, 23, 25, 133], "explain": [5, 27, 28, 84, 85, 86, 87, 126, 133, 136, 137, 141, 142, 147], "ai": [5, 26, 28, 85, 86, 87, 100, 136], "xai": [5, 28, 85, 86, 87, 126, 133, 141, 142, 147], "tile": [5, 27, 30, 84, 86, 87, 123, 133], "class": [5, 8, 13, 14, 16, 17, 19, 21, 22, 26, 30, 34, 35, 36, 37, 38, 39, 40, 43, 45, 47, 48, 49, 50, 51, 52, 53, 54, 56, 59, 61, 62, 65, 66, 67, 68, 69, 71, 74, 75, 76, 78, 79, 80, 82, 83, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 108, 109, 110, 111, 112, 113, 114, 116, 117, 118, 120, 123, 124, 125, 126, 127, 133, 136, 141, 145], "increment": [5, 15, 17, 30, 48, 49, 133], "sampler": [5, 15, 65, 120], "balanc": [5, 7, 15, 21, 22, 39, 51, 79], "algorithm": [6, 7, 9, 10, 12, 13, 15, 16, 19, 21, 22, 23, 28, 30, 33, 127, 133, 136, 138], "ptq": [6, 23, 25, 27, 28, 82, 84, 126, 133, 138, 140, 141, 142, 144, 145, 146], "appli": [6, 7, 12, 23, 28, 29, 45, 54, 61, 78, 79, 126, 136, 137, 138, 140, 141, 142, 144, 145, 146], "retrain": [6, 17], "fine": [6, 12, 17, 24, 25, 26], "tune": [6, 7, 12, 17, 24, 25, 26, 127, 138, 139, 140, 141, 142, 144, 145, 146], "detail": [6, 7, 21, 26, 28, 47, 53, 59, 60, 127, 139, 140, 141, 142, 144, 145, 146], "about": [6, 8, 16, 19, 21, 26, 28, 30, 59, 100, 136, 137, 138, 139, 140, 141, 142, 144, 145, 146], "how": [6, 8, 9, 13, 15, 16, 19, 21, 22, 26, 27, 28, 30, 126, 127, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147], "work": [6, 7, 8, 15, 16, 19, 21, 28, 51, 56, 61, 62, 84, 126, 137, 140, 141], "familiar": 6, "document": [6, 12, 28, 29, 133, 134, 140], "convert": [6, 9, 16, 26, 51, 56, 64, 71, 74, 104, 118, 121, 136], "intermedi": [6, 8, 28, 71, 74, 75, 76, 84, 126, 138, 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otx.core.metrics.accuracy)": [[78, "otx.core.metrics.accuracy.MulticlassAccuracywithLabelGroup"]], "multilabelaccuracywithlabelgroup (class in otx.core.metrics.accuracy)": [[78, "otx.core.metrics.accuracy.MultilabelAccuracywithLabelGroup"]], "namedconfusionmatrix (class in otx.core.metrics.accuracy)": [[78, "otx.core.metrics.accuracy.NamedConfusionMatrix"]], "col_names (otx.core.metrics.accuracy.namedconfusionmatrix property)": [[78, "otx.core.metrics.accuracy.NamedConfusionMatrix.col_names"]], "compute() (otx.core.metrics.accuracy.accuracywithlabelgroup method)": [[78, "otx.core.metrics.accuracy.AccuracywithLabelGroup.compute"]], "compute() (otx.core.metrics.accuracy.mixedhlabelaccuracy method)": [[78, "otx.core.metrics.accuracy.MixedHLabelAccuracy.compute"]], "label_info (otx.core.metrics.accuracy.accuracywithlabelgroup property)": [[78, "otx.core.metrics.accuracy.AccuracywithLabelGroup.label_info"]], "otx.core.metrics.accuracy": [[78, "module-otx.core.metrics.accuracy"]], "row_names 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\ No newline at end of file