dmlc · trivialfis · Jan 7, 2025 · Jan 7, 2025 · Jan 7, 2025 · Jan 7, 2025
diff --git a/doc/R-package/adding_parameters.rst b/doc/R-package/adding_parameters.rst
@@ -1,5 +1,3 @@
-.. _index_base:
-
 Developer guide: parameters from core library
 =============================================
 

diff --git a/doc/build.rst b/doc/build.rst
diff --git a/doc/contrib/donate.rst b/doc/contrib/donate.rst
@@ -32,7 +32,6 @@ All expenses incurred for hosting CI will be submitted to the fiscal host with r
 * Cloud expenses for the cloud test farm
 * Cost of domain https://xgboost-ci.net
 * Annual subscription for RunsOn
-* Hosting cost of the User Forum (https://discuss.xgboost.ai)
 
 Administration of cloud CI infrastructure
 -----------------------------------------

diff --git a/doc/contrib/python_packaging.rst b/doc/contrib/python_packaging.rst
@@ -74,7 +74,7 @@ built at the time of install. So ``pip install`` with the binary wheel
 completes quickly:
 
 .. code-block:: console
-  
+
   $ pip install xgboost-2.0.0-py3-none-linux_x86_64.whl  # Completes quickly
 
 .. rubric:: Footnotes

diff --git a/doc/gpu/index.rst b/doc/gpu/index.rst
@@ -103,4 +103,4 @@ Many thanks to the following contributors (alphabetical order):
 * Sriram Chandramouli
 * Vinay Deshpande
 
-Please report bugs to the XGBoost issues list: https://github.com/dmlc/xgboost/issues.  For general questions please visit our user form: https://discuss.xgboost.ai/.
+Please report bugs to the XGBoost `issues list <https://github.com/dmlc/xgboost/issues>`__.
diff --git a/doc/jvm/xgboost4j_spark_gpu_tutorial.rst b/doc/jvm/xgboost4j_spark_gpu_tutorial.rst
@@ -216,8 +216,9 @@ and the prediction for each instance.
 Submit the application
 **********************
 
-Assuming you have configured the Spark standalone cluster with GPU support. Otherwise, please
-refer to `spark standalone configuration with GPU support <https://nvidia.github.io/spark-rapids/docs/get-started/getting-started-on-prem.html#spark-standalone-cluster>`_.
+Assuming you have configured the Spark standalone cluster with GPU support. Otherwise,
+please refer to `spark standalone configuration with GPU support
+<https://docs.nvidia.com/spark-rapids/user-guide/latest/getting-started/on-premise.html>`__.
 
 Starting from XGBoost 2.1.0, stage-level scheduling is automatically enabled. Therefore,
 if you are using Spark standalone cluster version 3.4.0 or higher, we strongly recommend
@@ -257,4 +258,4 @@ Spark Standalone cluster.
 For details about other ``RAPIDS Accelerator`` other configurations, please refer to the `configuration <https://nvidia.github.io/spark-rapids/docs/configs.html>`_.
 
 For ``RAPIDS Accelerator Frequently Asked Questions``, please refer to the
-`frequently-asked-questions <https://nvidia.github.io/spark-rapids/docs/FAQ.html#frequently-asked-questions>`_.
+`frequently-asked-questions <https://docs.nvidia.com/spark-rapids/user-guide/latest/faq.html>`_.
diff --git a/doc/tutorials/dask.rst b/doc/tutorials/dask.rst
@@ -237,7 +237,7 @@ For most of the use cases with GPUs, the `Dask-CUDA <https://docs.rapids.ai/api/
 Working with other clusters
 ***************************
 
-Using Dask's ``LocalCluster`` is convenient for getting started quickly on a local machine. Once you're ready to scale your work, though, there are a number of ways to deploy Dask on a distributed cluster. You can use `Dask-CUDA <https://docs.rapids.ai/api/dask-cuda/stable/quickstart.html>`_, for example, for GPUs and you can use Dask Cloud Provider to `deploy Dask clusters in the cloud <https://docs.dask.org/en/stable/deploying.html#cloud>`_. See the `Dask documentation for a more comprehensive list <https://docs.dask.org/en/stable/deploying.html#distributed-computing>`_.
+Using Dask's ``LocalCluster`` is convenient for getting started quickly on a local machine. Once you're ready to scale your work, though, there are a number of ways to deploy Dask on a distributed cluster. You can use `Dask-CUDA <https://docs.rapids.ai/api/dask-cuda/stable/quickstart.html>`_, for example, for GPUs and you can use Dask Cloud Provider to `deploy Dask clusters in the cloud <https://docs.dask.org/en/stable/deploying.html#cloud>`_. See the `Dask documentation for a more comprehensive list <https://docs.dask.org/en/stable/deploying.html>`__.
 
 In the example below, a ``KubeCluster`` is used for `deploying Dask on Kubernetes <https://docs.dask.org/en/stable/deploying-kubernetes.html>`_:
 

diff --git a/doc/tutorials/input_format.rst b/doc/tutorials/input_format.rst
@@ -32,7 +32,7 @@ Auxiliary Files for Additional Information
 
 Group Input Format
 ==================
-For `ranking task <https://github.com/dmlc/xgboost/tree/master/demo/rank>`_, XGBoost supports the group input format. In ranking task, instances are categorized into *query groups* in real world scenarios. For example, in the learning to rank web pages scenario, the web page instances are grouped by their queries. XGBoost requires an file that indicates the group information. For example, if the instance file is the ``train.txt`` shown above,  the group file should be named ``train.txt.group`` and be of the following format:
+For ranking task, XGBoost supports the group input format. In ranking task, instances are categorized into *query groups* in real world scenarios. For example, in the learning to rank web pages scenario, the web page instances are grouped by their queries. XGBoost requires an file that indicates the group information. For example, if the instance file is the ``train.txt`` shown above,  the group file should be named ``train.txt.group`` and be of the following format:
 
 .. code-block:: none
   :caption: ``train.txt.group``

diff --git a/jvm-packages/create_jni.py b/jvm-packages/create_jni.py
@@ -16,9 +16,6 @@
 
 CONFIG = {
     "USE_OPENMP": "ON",
-    "USE_HDFS": "OFF",
-    "USE_AZURE": "OFF",
-    "USE_S3": "OFF",
     "USE_CUDA": "OFF",
     "USE_NCCL": "OFF",
     "JVM_BINDINGS": "ON",
@@ -70,10 +67,9 @@ def normpath(path):
         return normalized
 
 
-def native_build(args):
+def native_build(cli_args: argparse.Namespace) -> None:
+    CONFIG["USE_OPENMP"] = cli_args.use_openmp
     if sys.platform == "darwin":
-        # Enable of your compiler supports OpenMP.
-        CONFIG["USE_OPENMP"] = "OFF"
         os.environ["JAVA_HOME"] = (
             subprocess.check_output("/usr/libexec/java_home").strip().decode()
         )
@@ -184,5 +180,6 @@ def native_build(args):
         "--log-capi-invocation", type=str, choices=["ON", "OFF"], default="OFF"
     )
     parser.add_argument("--use-cuda", type=str, choices=["ON", "OFF"], default="OFF")
+    parser.add_argument("--use-openmp", type=str, choices=["ON", "OFF"], default="ON")
     cli_args = parser.parse_args()
     native_build(cli_args)
diff --git a/jvm-packages/pom.xml b/jvm-packages/pom.xml
@@ -56,6 +56,7 @@
         <maven.wagon.http.retryHandler.count>5</maven.wagon.http.retryHandler.count>
         <log.capi.invocation>OFF</log.capi.invocation>
         <use.cuda>OFF</use.cuda>
+        <use.openmp>ON</use.openmp>
         <cudf.version>24.10.0</cudf.version>
         <spark.rapids.version>24.10.0</spark.rapids.version>
         <spark.rapids.classifier>cuda12</spark.rapids.classifier>

diff --git a/jvm-packages/xgboost4j/pom.xml b/jvm-packages/xgboost4j/pom.xml
@@ -98,6 +98,8 @@
                             <argument>${log.capi.invocation}</argument>
                             <argument>--use-cuda</argument>
                             <argument>${use.cuda}</argument>
+                            <argument>--use-openmp</argument>
+                            <argument>${use.openmp}</argument>
                           </arguments>
                           <workingDirectory>${user.dir}</workingDirectory>
                           <skip>${skip.native.build}</skip>