Document.

demo/guide-python/external_memory.py

@@ -53,8 +53,8 @@ def __init__(self, device: str, file_paths: List[Tuple[str, str]]) -> None:
 
         self._file_paths = file_paths
         self._it = 0
-        # XGBoost will generate some cache files under current directory with the prefix
-        # "cache"
+        # XGBoost will generate some cache files under the current directory with the
+        # prefix "cache"
         super().__init__(cache_prefix=os.path.join(".", "cache"))
 
     def load_file(self) -> Tuple[np.ndarray, np.ndarray]:
@@ -81,7 +81,7 @@ def next(self, input_data: Callable) -> int:
             # return 0 to let XGBoost know this is the end of iteration
             return 0
 
-        # input_data is a function passed in by XGBoost who has the similar signature to
+        # input_data is a function passed in by XGBoost and has the similar signature to
         # the ``DMatrix`` constructor.
         X, y = self.load_file()
         input_data(data=X, label=y)
@@ -151,7 +151,7 @@ def main(tmpdir: str, args: argparse.Namespace) -> None:
         import rmm
         from rmm.allocators.cupy import rmm_cupy_allocator
 
-        # It's important to use RMM for GPU-based external memory for good performance.
+        # It's important to use RMM for GPU-based external memory to improve performance.
         # If XGBoost is not built with RMM support, a warning will be raised.
         mr = rmm.mr.PoolMemoryResource(rmm.mr.CudaAsyncMemoryResource())
         rmm.mr.set_current_device_resource(mr)

doc/jvm/xgboost_spark_migration.rst

@@ -55,9 +55,9 @@ When submitting the XGBoost application to the Spark cluster, you only need to s
       --jars xgboost-spark_2.12-3.0.0.jar \
       ... \
 
-**************
+***************
 XGBoost Ranking
-**************
+***************
 
 Learning to rank using XGBoostRegressor has been replaced by a dedicated `XGBoostRanker`, which is specifically designed
 to support ranking algorithms.

doc/parameter.rst

@@ -247,17 +247,17 @@ Parameters for Non-Exact Tree Methods
 
 * ``external_memory_concat_pages``, [default = ``false``]
 
-  This parameter is only used for the ``hist`` tree method with ``device=cuda``. Before
-  3.0, pages were always concatenated.
+  This parameter is only used for the ``hist`` tree method with ``device=cuda`` and
+  ``subsample != 1.0``. Before 3.0, pages were always concatenated.
 
   .. versionadded:: 3.0.0
 
   Whether the GPU-based ``hist`` tree method should concatenate the training data into a
   single batch instead of fetching data on-demand when external memory is used. For GPU
-  devices that don't support address translation services, external memory training can be
+  devices that don't support address translation services, external memory training is
   expensive. This parameter can be used in combination with subsampling to reduce overall
-  memory usage without significant overhead.  See :doc:`/tutorials/external_memory` for
-  more information
+  memory usage without significant overhead. See :doc:`/tutorials/external_memory` for
+  more information.
 
 .. _cat-param:
 

doc/python/python_api.rst

@@ -28,6 +28,10 @@ Core Data Structure
     :members:
     :show-inheritance:
 
+.. autoclass:: xgboost.ExtMemQuantileDMatrix
+    :members:
+    :show-inheritance:
+
 .. autoclass:: xgboost.Booster
     :members:
     :show-inheritance: