Remove redundant check.

python-package/xgboost/dask/__init__.py

@@ -199,20 +199,6 @@ def _start_tracker(
     return env
 
 
-def _assert_dask_support() -> None:
-    try:
-        import dask  # pylint: disable=W0621,W0611
-    except ImportError as e:
-        raise ImportError(
-            "Dask needs to be installed in order to use this module"
-        ) from e
-
-    if platform.system() == "Windows":
-        msg = "Windows is not officially supported for dask/xgboost,"
-        msg += " contribution are welcomed."
-        LOGGER.warning(msg)
-
-
 class CommunicatorContext(collective.CommunicatorContext):
     """A context controlling collective communicator initialization and finalization."""
 
@@ -299,7 +285,6 @@ def __init__(
         feature_weights: Optional[_DaskCollection] = None,
         enable_categorical: bool = False,
     ) -> None:
-        _assert_dask_support()
         client = _xgb_get_client(client)
 
         self.feature_names = feature_names
@@ -1071,7 +1056,6 @@ def train(  # pylint: disable=unused-argument
                          'eval': {'logloss': ['0.480385', '0.357756']}}}
 
     """
-    _assert_dask_support()
     client = _xgb_get_client(client)
     args = locals()
     return client.sync(
@@ -1451,7 +1435,6 @@ def predict(  # pylint: disable=unused-argument
         shape.
 
     """
-    _assert_dask_support()
     client = _xgb_get_client(client)
     return client.sync(_predict_async, global_config=config.get_config(), **locals())
 
@@ -1574,7 +1557,6 @@ def inplace_predict(  # pylint: disable=unused-argument
         shape.
 
     """
-    _assert_dask_support()
     client = _xgb_get_client(client)
     # When used in asynchronous environment, the `client` object should have
     # `asynchronous` attribute as True.  When invoked by the skl interface, it's
@@ -1681,7 +1663,6 @@ def predict(
         base_margin: Optional[_DaskCollection] = None,
         iteration_range: Optional[IterationRange] = None,
     ) -> Any:
-        _assert_dask_support()
         return self.client.sync(
             self._predict_async,
             X,
@@ -1717,7 +1698,6 @@ def apply(
         X: _DataT,
         iteration_range: Optional[IterationRange] = None,
     ) -> Any:
-        _assert_dask_support()
         return self.client.sync(self._apply_async, X, iteration_range=iteration_range)
 
     def __await__(self) -> Awaitable[Any]:
@@ -1862,7 +1842,6 @@ def fit(
         base_margin_eval_set: Optional[Sequence[_DaskCollection]] = None,
         feature_weights: Optional[_DaskCollection] = None,
     ) -> "DaskXGBRegressor":
-        _assert_dask_support()
         args = {k: v for k, v in locals().items() if k not in ("self", "__class__")}
         return self._client_sync(self._fit_async, **args)
 
@@ -1972,7 +1951,6 @@ def fit(
         base_margin_eval_set: Optional[Sequence[_DaskCollection]] = None,
         feature_weights: Optional[_DaskCollection] = None,
     ) -> "DaskXGBClassifier":
-        _assert_dask_support()
         args = {k: v for k, v in locals().items() if k not in ("self", "__class__")}
         return self._client_sync(self._fit_async, **args)
 
@@ -2008,7 +1986,6 @@ def predict_proba(
         base_margin: Optional[_DaskCollection] = None,
         iteration_range: Optional[IterationRange] = None,
     ) -> Any:
-        _assert_dask_support()
         return self._client_sync(
             self._predict_proba_async,
             X=X,
@@ -2161,7 +2138,6 @@ def fit(
         base_margin_eval_set: Optional[Sequence[_DaskCollection]] = None,
         feature_weights: Optional[_DaskCollection] = None,
     ) -> "DaskXGBRanker":
-        _assert_dask_support()
         args = {k: v for k, v in locals().items() if k not in ("self", "__class__")}
         return self._client_sync(self._fit_async, **args)
 
@@ -2224,7 +2200,6 @@ def fit(
         base_margin_eval_set: Optional[Sequence[_DaskCollection]] = None,
         feature_weights: Optional[_DaskCollection] = None,
     ) -> "DaskXGBRFRegressor":
-        _assert_dask_support()
         args = {k: v for k, v in locals().items() if k not in ("self", "__class__")}
         _check_rf_callback(self.early_stopping_rounds, self.callbacks)
         super().fit(**args)
@@ -2285,7 +2260,6 @@ def fit(
         base_margin_eval_set: Optional[Sequence[_DaskCollection]] = None,
         feature_weights: Optional[_DaskCollection] = None,
     ) -> "DaskXGBRFClassifier":
-        _assert_dask_support()
         args = {k: v for k, v in locals().items() if k not in ("self", "__class__")}
         _check_rf_callback(self.early_stopping_rounds, self.callbacks)
         super().fit(**args)