Optionally skip cupy on windows. (dmlc#10611)

python-package/xgboost/testing/__init__.py

@@ -45,6 +45,7 @@
     get_cancer,
     get_digits,
     get_sparse,
+    make_batches,
     memory,
 )
 
@@ -161,7 +162,16 @@ def no_cudf() -> PytestSkip:
 
 
 def no_cupy() -> PytestSkip:
-    return no_mod("cupy")
+    skip_cupy = no_mod("cupy")
+    if not skip_cupy["condition"] and system() == "Windows":
+        import cupy as cp
+
+        # Cupy might run into issue on Windows due to missing compiler
+        try:
+            cp.array([1, 2, 3]).sum()
+        except Exception:  # pylint: disable=broad-except
+            skip_cupy["condition"] = True
+    return skip_cupy
 
 
 def no_dask_cudf() -> PytestSkip:
@@ -247,34 +257,6 @@ def as_arrays(
         return X, y, w
 
 
-def make_batches(
-    n_samples_per_batch: int,
-    n_features: int,
-    n_batches: int,
-    use_cupy: bool = False,
-    *,
-    vary_size: bool = False,
-) -> Tuple[List[np.ndarray], List[np.ndarray], List[np.ndarray]]:
-    X = []
-    y = []
-    w = []
-    if use_cupy:
-        import cupy
-
-        rng = cupy.random.RandomState(1994)
-    else:
-        rng = np.random.RandomState(1994)
-    for i in range(n_batches):
-        n_samples = n_samples_per_batch + i * 10 if vary_size else n_samples_per_batch
-        _X = rng.randn(n_samples, n_features)
-        _y = rng.randn(n_samples)
-        _w = rng.uniform(low=0, high=1, size=n_samples)
-        X.append(_X)
-        y.append(_y)
-        w.append(_w)
-    return X, y, w
-
-
 def make_regression(
     n_samples: int, n_features: int, use_cupy: bool
 ) -> Tuple[ArrayLike, ArrayLike, ArrayLike]:

python-package/xgboost/testing/data.py

@@ -9,6 +9,7 @@
     Callable,
     Dict,
     Generator,
+    List,
     NamedTuple,
     Optional,
     Tuple,
@@ -501,6 +502,36 @@ def get_mq2008(
     )
 
 
+def make_batches(  # pylint: disable=too-many-arguments,too-many-locals
+    n_samples_per_batch: int,
+    n_features: int,
+    n_batches: int,
+    use_cupy: bool = False,
+    *,
+    vary_size: bool = False,
+    random_state: int = 1994,
+) -> Tuple[List[np.ndarray], List[np.ndarray], List[np.ndarray]]:
+    """Make batches of dense data."""
+    X = []
+    y = []
+    w = []
+    if use_cupy:
+        import cupy  # pylint: disable=import-error
+
+        rng = cupy.random.RandomState(random_state)
+    else:
+        rng = np.random.RandomState(random_state)
+    for i in range(n_batches):
+        n_samples = n_samples_per_batch + i * 10 if vary_size else n_samples_per_batch
+        _X = rng.randn(n_samples, n_features)
+        _y = rng.randn(n_samples)
+        _w = rng.uniform(low=0, high=1, size=n_samples)
+        X.append(_X)
+        y.append(_y)
+        w.append(_w)
+    return X, y, w
+
+
 RelData = Tuple[sparse.csr_matrix, npt.NDArray[np.int32], npt.NDArray[np.int32]]