Assuming at least 4GB memory

Author: viclafargue

python/cuml/tests/dask/test_dask_tsvd.py

@@ -28,12 +28,14 @@
 )
 @pytest.mark.parametrize("input_type", ["dataframe", "array"])
 def test_pca_fit(data_info, input_type, client):
+    # Assume at least 4GB memory
+    max_gpu_memory = pytest.max_gpu_memory if pytest.max_gpu_memory else 4
 
     nrows, ncols, n_parts = data_info
-    if nrows == int(9e6) and pytest.max_gpu_memory < 48:
+    if nrows == int(9e6) and max_gpu_memory < 48:
         if pytest.adapt_stress_test:
-            nrows = nrows * pytest.max_gpu_memory // 256
-            ncols = ncols * pytest.max_gpu_memory // 256
+            nrows = nrows * max_gpu_memory // 256
+            ncols = ncols * max_gpu_memory // 256
         else:
             pytest.skip(
                 "Insufficient GPU memory for this test."

python/cuml/tests/test_dbscan.py

@@ -62,12 +62,15 @@ def test_dbscan(
     out_dtype,
     algorithm,
 ):
+    # Assume at least 4GB memory
+    max_gpu_memory = pytest.max_gpu_memory if pytest.max_gpu_memory else 4
+
     if algorithm == "rbc":
         if datatype == np.float64 or out_dtype in ["int32", np.int32]:
             pytest.skip("RBC does not support float64 dtype or int32 labels")
-    if nrows == 500000 and pytest.max_gpu_memory < 32:
+    if nrows == 500000 and max_gpu_memory < 32:
         if pytest.adapt_stress_test:
-            nrows = nrows * pytest.max_gpu_memory // 32
+            nrows = nrows * max_gpu_memory // 32
         else:
             pytest.skip(
                 "Insufficient GPU memory for this test. "
@@ -213,9 +216,12 @@ def test_dbscan_cosine(nrows, max_mbytes_per_batch, out_dtype):
 # Vary the eps to get a range of core point counts
 @pytest.mark.parametrize("eps", [0.05, 0.1, 0.5])
 def test_dbscan_sklearn_comparison(name, nrows, eps):
-    if nrows == 500000 and name == "blobs" and pytest.max_gpu_memory < 32:
+    # Assume at least 4GB memory
+    max_gpu_memory = pytest.max_gpu_memory if pytest.max_gpu_memory else 4
+
+    if nrows == 500000 and name == "blobs" and max_gpu_memory < 32:
         if pytest.adapt_stress_test:
-            nrows = nrows * pytest.max_gpu_memory // 32
+            nrows = nrows * max_gpu_memory // 32
         else:
             pytest.skip(
                 "Insufficient GPU memory for this test."

python/cuml/tests/test_lars.py

@@ -119,10 +119,13 @@ def test_lars_model(datatype, nrows, column_info, precompute):
 )
 @pytest.mark.parametrize("precompute", [True, False])
 def test_lars_collinear(datatype, nrows, column_info, precompute):
+    # Assume at least 4GB memory
+    max_gpu_memory = pytest.max_gpu_memory if pytest.max_gpu_memory else 4
+
     ncols, n_info = column_info
-    if nrows == 500000 and ncols == 1000 and pytest.max_gpu_memory < 32:
+    if nrows == 500000 and ncols == 1000 and max_gpu_memory < 32:
         if pytest.adapt_stress_test:
-            nrows = nrows * pytest.max_gpu_memory // 32
+            nrows = nrows * max_gpu_memory // 32
         else:
             pytest.skip(
                 "Insufficient GPU memory for this test."

python/cuml/tests/test_mbsgd_regressor.py

@@ -45,13 +45,12 @@
 )
 def make_dataset(request):
     nrows, ncols, n_info, datatype = request.param
-    if (
-        nrows == 500000
-        and datatype == np.float64
-        and pytest.max_gpu_memory < 32
-    ):
+    # Assume at least 4GB memory
+    max_gpu_memory = pytest.max_gpu_memory if pytest.max_gpu_memory else 4
+
+    if nrows == 500000 and datatype == np.float64 and max_gpu_memory < 32:
         if pytest.adapt_stress_test:
-            nrows = nrows * pytest.max_gpu_memory // 32
+            nrows = nrows * max_gpu_memory // 32
         else:
             pytest.skip(
                 "Insufficient GPU memory for this test."

python/cuml/tests/test_pca.py

@@ -140,10 +140,13 @@ def test_pca_defaults(n_samples, n_features, sparse):
     "name", [unit_param(None), quality_param("iris"), stress_param("blobs")]
 )
 def test_pca_fit_then_transform(datatype, input_type, name, use_handle):
+    # Assume at least 4GB memory
+    max_gpu_memory = pytest.max_gpu_memory if pytest.max_gpu_memory else 4
+
     blobs_n_samples = 500000
-    if name == "blobs" and pytest.max_gpu_memory < 32:
+    if name == "blobs" and max_gpu_memory < 32:
         if pytest.adapt_stress_test:
-            blobs_n_samples = int(blobs_n_samples * pytest.max_gpu_memory / 32)
+            blobs_n_samples = int(blobs_n_samples * max_gpu_memory / 32)
         else:
             pytest.skip(
                 "Insufficient GPU memory for this test."
@@ -193,11 +196,14 @@ def test_pca_fit_then_transform(datatype, input_type, name, use_handle):
     "name", [unit_param(None), quality_param("iris"), stress_param("blobs")]
 )
 def test_pca_fit_transform(datatype, input_type, name, use_handle):
+    # Assume at least 4GB memory
+    max_gpu_memory = pytest.max_gpu_memory if pytest.max_gpu_memory else 4
+
     blobs_n_samples = 500000
 
-    if name == "blobs" and pytest.max_gpu_memory < 32:
+    if name == "blobs" and max_gpu_memory < 32:
         if pytest.adapt_stress_test:
-            blobs_n_samples = int(blobs_n_samples * pytest.max_gpu_memory / 32)
+            blobs_n_samples = int(blobs_n_samples * max_gpu_memory / 32)
         else:
             pytest.skip(
                 "Insufficient GPU memory for this test."
@@ -273,9 +279,12 @@ def test_pca_inverse_transform(datatype, input_type, name, use_handle, nrows):
 @pytest.mark.parametrize("return_sparse", [True, False])
 @pytest.mark.parametrize("cupy_input", [True, False])
 def test_sparse_pca_inputs(nrows, ncols, whiten, return_sparse, cupy_input):
-    if ncols == 20000 and pytest.max_gpu_memory < 48:
+    # Assume at least 4GB memory
+    max_gpu_memory = pytest.max_gpu_memory if pytest.max_gpu_memory else 4
+
+    if ncols == 20000 and max_gpu_memory < 48:
         if pytest.adapt_stress_test:
-            ncols = int(ncols * pytest.max_gpu_memory / 48)
+            ncols = int(ncols * max_gpu_memory / 48)
         else:
             pytest.skip(
                 "Insufficient GPU memory for this test."

python/cuml/tests/test_pickle.py

@@ -224,16 +224,19 @@ def assert_model(pickled_model, X_test):
 )
 @pytest.mark.parametrize("fit_intercept", [True, False])
 def test_regressor_pickle(tmpdir, datatype, keys, data_size, fit_intercept):
+    # Assume at least 4GB memory
+    max_gpu_memory = pytest.max_gpu_memory if pytest.max_gpu_memory else 4
+
     if (
         data_size[0] == 500000
         and datatype == np.float64
         and ("LogisticRegression" in keys or "Ridge" in keys)
-        and pytest.max_gpu_memory < 32
+        and max_gpu_memory < 32
     ):
         if pytest.adapt_stress_test:
-            data_size[0] = data_size[0] * pytest.max_gpu_memory // 640
-            data_size[1] = data_size[1] * pytest.max_gpu_memory // 640
-            data_size[2] = data_size[2] * pytest.max_gpu_memory // 640
+            data_size[0] = data_size[0] * max_gpu_memory // 640
+            data_size[1] = data_size[1] * max_gpu_memory // 640
+            data_size[2] = data_size[2] * max_gpu_memory // 640
         else:
             pytest.skip(
                 "Insufficient GPU memory for this test."
@@ -437,13 +440,16 @@ def test_unfit_clone(model_name):
     [unit_param([500, 20, 10, 5]), stress_param([500000, 1000, 500, 50])],
 )
 def test_neighbors_pickle(tmpdir, datatype, keys, data_info):
+    # Assume at least 4GB memory
+    max_gpu_memory = pytest.max_gpu_memory if pytest.max_gpu_memory else 4
+
     if (
         data_info[0] == 500000
-        and pytest.max_gpu_memory < 32
+        and max_gpu_memory < 32
         and ("KNeighborsClassifier" in keys or "KNeighborsRegressor" in keys)
     ):
         if pytest.adapt_stress_test:
-            data_info[0] = data_info[0] * pytest.max_gpu_memory // 32
+            data_info[0] = data_info[0] * max_gpu_memory // 32
         else:
             pytest.skip(
                 "Insufficient GPU memory for this test."
@@ -486,13 +492,16 @@ def assert_model(pickled_model, X_test):
 )
 @pytest.mark.parametrize("keys", k_neighbors_models.keys())
 def test_k_neighbors_classifier_pickle(tmpdir, datatype, data_info, keys):
+    # Assume at least 4GB memory
+    max_gpu_memory = pytest.max_gpu_memory if pytest.max_gpu_memory else 4
+
     if (
         data_info[0] == 500000
         and "NearestNeighbors" in keys
-        and pytest.max_gpu_memory < 32
+        and max_gpu_memory < 32
     ):
         if pytest.adapt_stress_test:
-            data_info[0] = data_info[0] * pytest.max_gpu_memory // 32
+            data_info[0] = data_info[0] * max_gpu_memory // 32
         else:
             pytest.skip(
                 "Insufficient GPU memory for this test."
@@ -561,9 +570,12 @@ def assert_model(loaded_model, X):
     "data_size", [unit_param([500, 20, 10]), stress_param([500000, 1000, 500])]
 )
 def test_dbscan_pickle(tmpdir, datatype, keys, data_size):
-    if data_size[0] == 500000 and pytest.max_gpu_memory < 32:
+    # Assume at least 4GB memory
+    max_gpu_memory = pytest.max_gpu_memory if pytest.max_gpu_memory else 4
+
+    if data_size[0] == 500000 and max_gpu_memory < 32:
         if pytest.adapt_stress_test:
-            data_size[0] = data_size[0] * pytest.max_gpu_memory // 32
+            data_size[0] = data_size[0] * max_gpu_memory // 32
         else:
             pytest.skip(
                 "Insufficient GPU memory for this test."