Merge branch 'branch-25.02' into ridge-alpha-0

conda/environments/all_cuda-118_arch-x86_64.yaml

@@ -47,7 +47,7 @@ dependencies:
 - nbsphinx
 - ninja
 - nltk
-- numba>=0.57
+- numba>=0.59.1,<0.61.0a0
 - numpy>=1.23,<3.0a0
 - numpydoc
 - nvcc_linux-64=11.8

conda/environments/all_cuda-125_arch-x86_64.yaml

@@ -44,7 +44,7 @@ dependencies:
 - nbsphinx
 - ninja
 - nltk
-- numba>=0.57
+- numba>=0.59.1,<0.61.0a0
 - numpy>=1.23,<3.0a0
 - numpydoc
 - packaging

dependencies.yaml

@@ -234,7 +234,7 @@ dependencies:
         packages:
           - dask-cuda==25.2.*,>=0.0.0a0
           - joblib>=0.11
-          - numba>=0.57
+          - numba>=0.59.1,<0.61.0a0
           - numpy>=1.23,<3.0a0
             # TODO: Is scipy really a hard dependency, or should
             # we make it optional (i.e. an extra for pip

python/cuml/cuml/tests/dask/test_dask_logistic_regression.py

@@ -34,7 +34,12 @@
 dask_cudf = gpu_only_import("dask_cudf")
 cudf = gpu_only_import("cudf")
 
-pytestmark = pytest.mark.mg
+pytestmark = [
+    pytest.mark.mg,
+    pytest.mark.skip(
+        reason="pytest hang https://github.com/rapidsai/cuml/issues/6247"
+    ),
+]
 
 
 def _prep_training_data(c, X_train, y_train, partitions_per_worker):

python/cuml/cuml/tests/test_input_utils.py

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019-2024, NVIDIA CORPORATION.
+# Copyright (c) 2019-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -35,6 +35,7 @@
 np = cpu_only_import("numpy")
 
 nbcuda = gpu_only_import_from("numba", "cuda")
+cudf_pandas_active = gpu_only_import_from("cudf.pandas", "LOADED")
 pdDF = cpu_only_import_from("pandas", "DataFrame")
 
 
@@ -446,11 +447,14 @@ def test_tocupy_missing_values_handling():
     assert str(array.dtype) == "float64"
     assert cp.isnan(array[1])
 
-    with pytest.raises(ValueError):
-        df = cudf.Series(data=[7, None, 3])
-        array, n_rows, n_cols, dtype = input_to_cupy_array(
-            df, fail_on_null=True
-        )
+    # cudf.pandas now mimics pandas better for handling None, so we don't
+    # need to fail and raise this error when cudf.pandas is active.
+    if not cudf_pandas_active:
+        with pytest.raises(ValueError):
+            df = cudf.Series(data=[7, None, 3])
+            array, n_rows, n_cols, dtype = input_to_cupy_array(
+                df, fail_on_null=True
+            )
 
 
 @pytest.mark.cudf_pandas

python/cuml/cuml/tests/test_kneighbors_classifier.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+# Copyright (c) 2019-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -218,6 +218,8 @@ def test_predict_large_n_classes(datatype):
     assert array_equal(y_hat.astype(np.int32), y_test.astype(np.int32))
 
 
+# Ignore FutureWarning: Using `__dataframe__` is deprecated
+@pytest.mark.filterwarnings("ignore::FutureWarning")
 @pytest.mark.parametrize("n_samples", [100])
 @pytest.mark.parametrize("n_features", [40])
 @pytest.mark.parametrize("n_neighbors", [4])

python/cuml/cuml/tests/test_metrics.py

@@ -163,6 +163,8 @@ def test_r2_score(datatype, use_handle):
     np.testing.assert_almost_equal(score, 0.98, decimal=7)
 
 
+# Ignore FutureWarning: Using `__dataframe__` is deprecated
+@pytest.mark.filterwarnings("ignore::FutureWarning")
 def test_sklearn_search():
     """Test ensures scoring function works with sklearn machinery"""
     import numpy as np

python/cuml/pyproject.toml

@@ -99,7 +99,7 @@ dependencies = [
     "dask-cuda==25.2.*,>=0.0.0a0",
     "dask-cudf==25.2.*,>=0.0.0a0",
     "joblib>=0.11",
-    "numba>=0.57",
+    "numba>=0.59.1,<0.61.0a0",
     "numpy>=1.23,<3.0a0",
     "nvidia-cublas",
     "nvidia-cufft",