linting

tests/ci_build/conda_env/linux_sycl_test.yml

@@ -19,3 +19,4 @@ dependencies:
 - pytest-cov
 - dpcpp_linux-64
 - onedpl-devel
+- dask

tests/python-sycl/test_sycl_simple_dask.py

@@ -6,6 +6,7 @@
 import dask.array as da
 import dask.distributed
 
+
 def train_result(client, param, dtrain, num_rounds):
     result = dxgb.train(
         client,
@@ -17,24 +18,25 @@ def train_result(client, param, dtrain, num_rounds):
     )
     return result
 
+
 class TestSYCLDask:
-  # The simplest test verify only one node training.
-  def test_simple(self):
-    cluster = dask.distributed.LocalCluster(n_workers=1)
-    client = dask.distributed.Client(cluster)
-
-    param={}
-    param["tree_method"] = "hist"
-    param["device"] = "sycl"
-    param["verbosity"] = 0
-    param["objective"] = "reg:squarederror"
-
-    # X and y must be Dask dataframes or arrays
-    num_obs = 1e4
-    num_features = 20
-    X = da.random.random(size=(num_obs, num_features), chunks=(1000, num_features))
-    y = da.random.random(size=(num_obs, 1), chunks=(1000, 1))
-    dtrain = dxgb.DaskDMatrix(client, X, y)
-
-    result = train_result(client, param, dtrain, 10)
-    assert tm.non_increasing(result["history"]["train"]["rmse"])
+    # The simplest test verify only one node training.
+    def test_simple(self):
+        cluster = dask.distributed.LocalCluster(n_workers=1)
+        client = dask.distributed.Client(cluster)
+
+        param = {}
+        param["tree_method"] = "hist"
+        param["device"] = "sycl"
+        param["verbosity"] = 0
+        param["objective"] = "reg:squarederror"
+
+        # X and y must be Dask dataframes or arrays
+        num_obs = 1e4
+        num_features = 20
+        X = da.random.random(size=(num_obs, num_features), chunks=(1000, num_features))
+        y = da.random.random(size=(num_obs, 1), chunks=(1000, 1))
+        dtrain = dxgb.DaskDMatrix(client, X, y)
+
+        result = train_result(client, param, dtrain, 10)
+        assert tm.non_increasing(result["history"]["train"]["rmse"])