Remove cuml.common.cuda

python/cuml/cuml/__init__.py

@@ -24,14 +24,14 @@
     libcuml.load_library()
     del libcuml
 
-import cuml.common.cuda as cuda
+from pylibraft.common import Handle
+
 import cuml.feature_extraction
 from cuml._version import __git_commit__, __version__
 from cuml.cluster.agglomerative import AgglomerativeClustering
 from cuml.cluster.dbscan import DBSCAN
 from cuml.cluster.hdbscan import HDBSCAN
 from cuml.cluster.kmeans import KMeans
-from cuml.common.handle import Handle
 from cuml.common.pointer_utils import device_of_gpu_matrix
 from cuml.datasets.arima import make_arima
 from cuml.datasets.blobs import make_blobs
@@ -116,7 +116,6 @@ def __getattr__(name):
     "AutoARIMA",
     "Base",
     "CD",
-    "cuda",
     "DBSCAN",
     "ElasticNet",
     "ExponentialSmoothing",

python/cuml/cuml/common/CMakeLists.txt

@@ -13,8 +13,6 @@
 # =============================================================================
 
 set(cython_sources "")
-add_module_gpu_default("cuda.pyx")
-add_module_gpu_default("handle.pyx")
 add_module_gpu_default("pointer_utils.pyx")
 
 if(NOT SINGLEGPU)

python/cuml/cuml/ensemble/randomforest_shared.pxd

@@ -32,8 +32,6 @@ from cuml.internals.base import Base
 
 from pylibraft.common.handle cimport handle_t
 
-cimport cuml.common.cuda
-
 
 cdef extern from "treelite/c_api.h":
     ctypedef void* TreeliteModelHandle

python/cuml/cuml/ensemble/randomforestclassifier.pyx

@@ -41,7 +41,6 @@ from libc.stdint cimport uint64_t, uintptr_t
 from libcpp cimport bool
 from pylibraft.common.handle cimport handle_t
 
-cimport cuml.common.cuda
 from cuml.ensemble.randomforest_shared cimport *
 from cuml.internals.logger cimport level_enum
 

python/cuml/cuml/ensemble/randomforestregressor.pyx

@@ -44,8 +44,6 @@ from libc.stdint cimport uint64_t, uintptr_t
 from libcpp cimport bool
 from pylibraft.common.handle cimport handle_t
 
-cimport cuml.common.cuda
-
 
 cdef extern from "cuml/ensemble/randomforest.hpp" namespace "ML":
 

python/cuml/cuml/internals/base.pyx

@@ -39,7 +39,6 @@ from cupy import ndarray as cp_ndarray
 import cuml
 import cuml.accel
 import cuml.common
-import cuml.common.cuda
 import cuml.internals
 import cuml.internals.input_utils
 import cuml.internals.logger as logger
@@ -218,15 +217,15 @@ class Base(TagsMixin,
 
         # stream and handle example:
 
-        stream = cuml.common.cuda.Stream()
+        stream = pylibraft.common.Stream()
         handle = pylibraft.common.Handle(stream=stream)
 
         algo = MyAlgo(handle=handle)
         algo.fit(...)
         result = algo.predict(...)
 
         # final sync of all gpu-work launched inside this object
-        # this is same as `cuml.cuda.Stream.sync()` call, but safer in case
+        # this is same as `pylibraft.common.Stream.sync()` call, but safer in case
         # the default stream inside the `raft::handle_t` is being used
         base.handle.sync()
         del base  # optional!

python/cuml/cuml/legacy/fil/fil.pyx

@@ -44,8 +44,6 @@ from rmm.pylibrmm.memory_resource cimport (
 
 import treelite.sklearn as tl_skl
 
-cimport cuml.common.cuda
-
 
 cdef extern from "treelite/c_api.h":
     cdef struct TreelitePyBufferFrame:

python/cuml/cuml/manifold/t_sne.pyx

@@ -51,7 +51,6 @@ from libc.stdint cimport int64_t, uintptr_t
 from libc.stdlib cimport free
 from libcpp cimport bool
 
-cimport cuml.common.cuda
 from cuml.metrics.distance_type cimport DistanceType
 
 

python/cuml/cuml/metrics/cluster/adjusted_rand_index.pyx

@@ -25,8 +25,6 @@ from cuml.common import input_to_cuml_array
 from libc.stdint cimport uintptr_t
 from pylibraft.common.handle cimport handle_t
 
-cimport cuml.common.cuda
-
 
 cdef extern from "cuml/metrics/metrics.hpp" namespace "ML::Metrics":
 

python/cuml/cuml/metrics/cluster/entropy.pyx

@@ -29,8 +29,6 @@ from cuml.internals.input_utils import input_to_cupy_array
 from libc.stdint cimport uintptr_t
 from pylibraft.common.handle cimport handle_t
 
-cimport cuml.common.cuda
-
 
 cdef extern from "cuml/metrics/metrics.hpp" namespace "ML::Metrics":
     double entropy(const handle_t &handle,

python/cuml/cuml/neighbors/kneighbors_classifier.pyx

@@ -35,8 +35,6 @@ from libc.stdint cimport int64_t, uintptr_t
 from libcpp.vector cimport vector
 from pylibraft.common.handle cimport handle_t
 
-cimport cuml.common.cuda
-
 
 cdef extern from "cuml/neighbors/knn.hpp" namespace "ML":
 

python/cuml/cuml/neighbors/kneighbors_regressor.pyx

@@ -32,8 +32,6 @@ from libc.stdint cimport int64_t, uintptr_t
 from libcpp.vector cimport vector
 from pylibraft.common.handle cimport handle_t
 
-cimport cuml.common.cuda
-
 
 cdef extern from "cuml/neighbors/knn.hpp" namespace "ML":
 

python/cuml/cuml/neighbors/nearest_neighbors.pyx

@@ -45,7 +45,6 @@ from libcpp cimport bool
 from libcpp.vector cimport vector
 from pylibraft.common.handle cimport handle_t
 
-cimport cuml.common.cuda
 from cuml.metrics.distance_type cimport DistanceType
 from cuml.metrics.raft_distance_type cimport DistanceType as RaftDistanceType
 from cuml.neighbors.ann cimport *

python/cuml/cuml/tests/test_svm.py

@@ -452,10 +452,7 @@ def get_memsize(svc):
     "n_iter", [unit_param(10), quality_param(100), stress_param(1000)]
 )
 @pytest.mark.parametrize("n_cols", [1000])
-@pytest.mark.parametrize("use_handle", [True, False])
-def test_svm_memleak(
-    params, n_rows, n_iter, n_cols, use_handle, dataset="blobs"
-):
+def test_svm_memleak(params, n_rows, n_iter, n_cols, dataset="blobs"):
     """
     Test whether there is any memory leak.
 
@@ -464,8 +461,7 @@ def test_svm_memleak(
 
     """
     X_train, X_test, y_train, y_test = make_dataset(dataset, n_rows, n_cols)
-    stream = cuml.cuda.Stream()
-    handle = cuml.Handle(stream=stream)
+    handle = cuml.Handle()
     # Warmup. Some modules that are used in SVC allocate space on the device
     # and consume memory. Here we make sure that this allocation is done
     # before the first call to get_memory_info.
@@ -519,8 +515,7 @@ def test_svm_memleak_on_exception(
         n_samples=n_rows, n_features=n_cols, random_state=137, centers=2
     )
     X_train = X_train.astype(np.float32)
-    stream = cuml.cuda.Stream()
-    handle = cuml.Handle(stream=stream)
+    handle = cuml.Handle()
 
     # Warmup. Some modules that are used in SVC allocate space on the device
     # and consume memory. Here we make sure that this allocation is done