def test_logistic_regression():
with cupy_using_allocator(dummy_allocator):
X_train, X_test, y_train, y_test = \
small_classification_dataset(np.float32)
y_train = y_train.astype(np.float32)
y_test = y_test.astype(np.float32)
culog = LogisticRegression()
culog.fit(X_train, y_train)
culog.predict(X_train)
def rmm_cupy_ary(cupy_fn, *args, **kwargs):
"""
Function to call CuPy functions with RMM memory management
Parameters
----------
cupy_fn : cupy function,
CuPy function to execute, for example cp.array
*args :
Non keyword arguments to pass to the CuPy function
**kwargs :
Keyword named arguments to pass to the CuPy function
Note: this function should be used if the result of cupy_fn creates
a new array. Functions to create a new CuPy array by reference to
existing device array (through __cuda_array_interface__) can be used
directly.
Examples
---------
.. code-block:: python
from cuml.common import rmm_cupy_ary
import cupy as cp
# Get a new array filled with 0, column major
a = rmm_cupy_ary(cp.zeros, 5, order='F')
"""
# using_allocator was introduced in CuPy 7. Once 7+ is required,
# this check can be removed alongside the else code path.
if check_min_cupy_version("7.0"):
with cupy_using_allocator(rmm.rmm_cupy_allocator):
result = cupy_fn(*args, **kwargs)
else:
temp_res = cupy_fn(*args, **kwargs)
result = \
_rmm_cupy6_array_like(temp_res,
order=_strides_to_order(temp_res.strides,
temp_res.dtype))
cp.copyto(result, temp_res)
return result
def test_naive_bayes(nlp_20news):
X, y = nlp_20news
X = sparse_scipy_to_cp(X, cp.float32).astype(cp.float32)
y = y.astype(cp.int32)
with cupy_using_allocator(dummy_allocator):
model = MultinomialNB()
model.fit(X, y)
y_hat = model.predict(X)
y_hat = model.predict(X)
y_hat = model.predict_proba(X)
y_hat = model.predict_log_proba(X)
y_hat = model.score(X, y)
del y_hat
def __init__(self):
super().__init__()
def cleanup():
cuml.global_settings.root_cm = None
self.callback(cleanup)
self.enter_context(cupy_using_allocator(rmm.rmm_cupy_allocator))
self.prev_output_type = self.enter_context(_using_mirror_output_type())
self._output_type = None
self.output_dtype = None
# Set the output type to the prev_output_type. If "input", set to None
# to allow inner functions to specify the input
self.output_type = (None if self.prev_output_type == "input" else
self.prev_output_type)
self._count = 0
self.call_stack = {}
cuml.global_settings.root_cm = self
def test_dummy_allocator():
with pytest.raises(AssertionError):
with cupy_using_allocator(dummy_allocator):
a = cp.arange(10)
del a
def cupy_rmm_wrapper(*args, **kwargs):
with cupy_using_allocator(rmm.rmm_cupy_allocator):
return func(*args, **kwargs)
