[doc] Small cleanup and updates for documentation. (#11532)

- Update broken links.
- Merge GPU demos into Python demos. We already use GPU in python demos, no need to have a
  separate category.
- Fix GPU cover type demo.

Author: trivialfis

demo/gpu_acceleration/README.rst

@@ -45,5 +45,6 @@
 # Repeat for CPU algorithm
 clf = xgb.XGBClassifier(device="cpu", n_estimators=num_round)
 start = time.time()
+clf.fit(X_train, y_train, eval_set=[(X_test, y_test)])
 cpu_res = clf.evals_result()
 print("CPU Training Time: %s seconds" % (str(time.time() - start)))

demo/gpu_acceleration/cover_type.py demo/guide-python/cover_type.pydemo/gpu_acceleration/cover_type.py renamed to demo/guide-python/cover_type.py

@@ -250,15 +250,13 @@ def is_readthedocs_build():
         "../demo/guide-python",
         "../demo/dask",
         "../demo/aft_survival",
-        "../demo/gpu_acceleration",
         "../demo/rmm_plugin",
     ],
     # path to where to save gallery generated output
     "gallery_dirs": [
         "python/examples",
         "python/dask-examples",
         "python/survival-examples",
-        "python/gpu-examples",
         "python/rmm-examples",
     ],
     "matplotlib_animations": True,

demo/gpu_acceleration/tree_shap.py demo/guide-python/gpu_tree_shap.pydemo/gpu_acceleration/tree_shap.py renamed to demo/guide-python/gpu_tree_shap.py

@@ -42,7 +42,7 @@ with a new release branch.
 
 R CRAN Package
 --------------
-Before submitting a release, one should test the package on `R-hub <https://builder.r-hub.io/>`__ and `win-builder <https://win-builder.r-project.org/>`__ first.  Please note that the R-hub Windows instance doesn't have the exact same environment as the one hosted on win-builder.
+Before submitting a release, one should test the package on `R-hub <https://r-hub.github.io/rhub/>`__ and `win-builder <https://win-builder.r-project.org/>`__ first.  Please note that the R-hub Windows instance doesn't have the exact same environment as the one hosted on win-builder.
 
 According to the `CRAN policy <https://cran.r-project.org/web/packages/policies.html>`__:
 

doc/conf.py

@@ -43,7 +43,7 @@ XGBoost makes use of `GPUTreeShap <https://github.com/rapidsai/gputreeshap>`_ as
   shap_values = booster.predict(dtrain, pred_contribs=True)
   shap_interaction_values = model.predict(dtrain, pred_interactions=True)
 
-See :ref:`sphx_glr_python_gpu-examples_tree_shap.py` for a worked example.
+See :ref:`sphx_glr_python_examples_gpu_tree_shap.py` for a worked example.
 
 Multi-node Multi-GPU Training
 =============================

doc/contrib/release.rst

@@ -16,5 +16,4 @@ Contents
   examples/index
   dask-examples/index
   survival-examples/index
-  gpu-examples/index
   rmm-examples/index

doc/gpu/index.rst

@@ -250,8 +250,8 @@ a few things you need to do:
 
 
 Ray Tune supports various
-`search algorithms and libraries (e.g. BayesOpt, Tree-Parzen estimators) <https://docs.ray.io/en/latest/tune/key-concepts.html#search-algorithms>`_,
-`smart schedulers like successive halving <https://docs.ray.io/en/latest/tune/key-concepts.html#trial-schedulers>`_,
+`search algorithms and libraries (e.g. BayesOpt, Tree-Parzen estimators) <https://docs.ray.io/en/latest/tune/key-concepts.html#tune-search-algorithms>`_,
+`smart schedulers like successive halving <https://docs.ray.io/en/latest/tune/key-concepts.html#tune-schedulers>`_,
 and other features. Please refer to the `Ray Tune documentation <http://tune.io>`_
 for more information.