use paddle.is_compile_with_cuda (#27586)

* modify doc

python/paddle/__init__.py

@@ -257,6 +257,7 @@ from .tensor.stat import numel  #DEFINE_ALIAS
 from .device import get_cudnn_version
 from .device import set_device
 from .device import get_device
+from .device import is_compiled_with_cuda  #DEFINE_ALIAS
 from .device import is_compiled_with_xpu
 from .device import XPUPlace
 # from .tensor.tensor import Tensor        #DEFINE_ALIAS

python/paddle/device.py

@@ -18,6 +18,7 @@ import re
 from paddle.fluid import core
 from paddle.fluid import framework
 from paddle.fluid.dygraph.parallel import ParallelEnv
+from paddle.fluid.framework import is_compiled_with_cuda  #DEFINE_ALIAS
 
 __all__ = [
     'get_cudnn_version',
@@ -31,7 +32,7 @@ __all__ = [
     #            'cuda_places',
     #            'CUDAPinnedPlace',
     #            'CUDAPlace',
-    #            'is_compiled_with_cuda'
+    'is_compiled_with_cuda'
 ]
 
 _cudnn_version = None

python/paddle/fluid/framework.py

@@ -360,13 +360,13 @@ def is_compiled_with_cuda():
     """
     Whether this whl package can be used to run the model on GPU.
 
-    Returns (bool): support gpu or not.
+    Returns (bool): `True` if CUDA is currently available, otherwise `False`.
 
     Examples:
         .. code-block:: python
 
-            import paddle.fluid as fluid
-            support_gpu = fluid.is_compiled_with_cuda()
+            import paddle
+            support_gpu = paddle.is_compiled_with_cuda()
     """
     return core.is_compiled_with_cuda()