[breaking] Bump Python requirement to 3.10. (#10434)

- Bump the Python requirement.
- Fix type hints.
- Use loky to avoid deadlock.
- Workaround cupy-numpy compatibility issue on Windows caused by the `safe` casting rule.
- Simplify the repartitioning logic to avoid dask errors.

demo/aft_survival/aft_survival_viz_demo.py

@@ -106,8 +106,8 @@ plt.figure(figsize=(12, 13))
 bst = xgb.train(
     params,
     dmat,
-    15,
-    [(dmat, "train")],
+    num_boost_round=15,
+    evals=[(dmat, "train")],
     evals_result=res,
     callbacks=[PlotIntermediateModel()],
 )

demo/guide-python/quantile_data_iterator.py

@@ -42,7 +42,7 @@ class IterForDMatrixDemo(xgboost.core.DataIter):
         """
         self.rows = ROWS_PER_BATCH
         self.cols = COLS
-        rng = cupy.random.RandomState(1994)
+        rng = cupy.random.RandomState(numpy.uint64(1994))
         self._data = [rng.randn(self.rows, self.cols)] * BATCHES
         self._labels = [rng.randn(self.rows)] * BATCHES
         self._weights = [rng.uniform(size=self.rows)] * BATCHES

demo/nvflare/horizontal/README.md

@@ -8,7 +8,7 @@ This directory contains a demo of Horizontal Federated Learning using
 To run the demo, first build XGBoost with the federated learning plugin enabled (see the
 [README](../../../plugin/federated/README.md)).
 
-Install NVFlare (note that currently NVFlare only supports Python 3.8):
+Install NVFlare:
 ```shell
 pip install nvflare
 ```

demo/nvflare/vertical/README.md

@@ -8,7 +8,7 @@ This directory contains a demo of Vertical Federated Learning using
 To run the demo, first build XGBoost with the federated learning plugin enabled (see the
 [README](../../../plugin/federated/README.md)).
 
-Install NVFlare (note that currently NVFlare only supports Python 3.8):
+Install NVFlare:
 ```shell
 pip install nvflare
 ```