Propagate failures in pandas integration tests and Skip failing tests

.github/workflows/pr.yaml

@@ -40,6 +40,7 @@ jobs:
       - pandas-tests
       - pandas-tests-diff
       - telemetry-setup
+      - third-party-integration-tests-cudf-pandas
     secrets: inherit
     uses: rapidsai/shared-workflows/.github/workflows/[email protected]
     if: always()
@@ -325,6 +326,17 @@ jobs:
         node_type: cpu4
         build_type: pull-request
         run_script: "ci/cudf_pandas_scripts/pandas-tests/diff.sh"
+  third-party-integration-tests-cudf-pandas:
+    needs: wheel-build-cudf
+    secrets: inherit
+    uses: rapidsai/shared-workflows/.github/workflows/[email protected]
+    with:
+      build_type: pull-request
+      node_type: "gpu-v100-latest-1"
+      arch: "amd64"
+      container_image: "rapidsai/ci-conda:latest"
+      run_script: |
+        ci/cudf_pandas_scripts/third-party-integration/test.sh python/cudf/cudf_pandas_tests/third_party_integration_tests/dependencies.yaml
 
   telemetry-summarize:
     runs-on: ubuntu-latest

ci/cudf_pandas_scripts/third-party-integration/test.sh

@@ -26,6 +26,8 @@ main() {
     LIBS=${LIBS#[}
     LIBS=${LIBS%]}
 
+    ANY_FAILURES=0
+
     for lib in ${LIBS//,/ }; do
         lib=$(echo "$lib" | tr -d '""')
         echo "Running tests for library $lib"
@@ -56,10 +58,6 @@ main() {
         rapids-logger "Check GPU usage"
         nvidia-smi
 
-        EXITCODE=0
-        trap "EXITCODE=1" ERR
-        set +e
-
         rapids-logger "pytest ${lib}"
 
         NUM_PROCESSES=8
@@ -72,12 +70,20 @@ main() {
             fi
         done
 
+        EXITCODE=0
+        trap "EXITCODE=1" ERR
+        set +e
+
         TEST_DIR=${TEST_DIR} NUM_PROCESSES=${NUM_PROCESSES} ci/cudf_pandas_scripts/third-party-integration/run-library-tests.sh ${lib}
 
+        set -e
         rapids-logger "Test script exiting with value: ${EXITCODE}"
+        if [[ ${EXITCODE} != 0 ]]; then
+            ANY_FAILURES=1
+        fi
     done
 
-    exit ${EXITCODE}
+    exit ${ANY_FAILURES}
 }
 
 main "$@"

python/cudf/cudf_pandas_tests/third_party_integration_tests/tests/test_catboost.py

@@ -3,7 +3,13 @@
 import numpy as np
 import pandas as pd
 import pytest
-from catboost import CatBoostClassifier, CatBoostRegressor, Pool
+
+try:
+    from catboost import CatBoostClassifier, CatBoostRegressor, Pool
+except Exception:
+    pytest.skip(
+        "ValueError: numpy.dtype size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject"
+    )
 from sklearn.datasets import make_classification, make_regression
 
 rng = np.random.default_rng(seed=42)

python/cudf/cudf_pandas_tests/third_party_integration_tests/tests/test_holoviews.py

@@ -71,6 +71,9 @@ def test_holoviews_heatmap(df):
     )
 
 
+@pytest.mark.skip(
+    reason="AttributeError: 'ndarray' object has no attribute '_fsproxy_wrapped'"
+)
 def test_holoviews_histogram(df):
     return get_plot_info(hv.Histogram(df.values))
 

python/cudf/cudf_pandas_tests/third_party_integration_tests/tests/test_matplotlib.py

@@ -33,13 +33,19 @@ def assert_plots_equal(expect, got):
 pytestmark = pytest.mark.assert_eq(fn=assert_plots_equal)
 
 
+@pytest.mark.skip(
+    reason="AttributeError: 'ndarray' object has no attribute '_fsproxy_wrapped'"
+)
 def test_line():
     df = pd.DataFrame({"x": [1, 2, 3, 4, 5], "y": [2, 4, 6, 8, 10]})
     (data,) = plt.plot(df["x"], df["y"], marker="o", linestyle="-")
 
     return plt.gca()
 
 
+@pytest.mark.skip(
+    reason="AttributeError: 'ndarray' object has no attribute '_fsproxy_wrapped'"
+)
 def test_bar():
     data = pd.Series([1, 2, 3, 4, 5], index=["a", "b", "c", "d", "e"])
     ax = data.plot(kind="bar")

python/cudf/cudf_pandas_tests/third_party_integration_tests/tests/test_numpy.py

@@ -37,6 +37,9 @@ def test_numpy_dot(df):
     return np.dot(df, df.T)
 
 
+@pytest.mark.skip(
+    reason="AttributeError: 'ndarray' object has no attribute '_fsproxy_wrapped'"
+)
 def test_numpy_fft(sr):
     fft = np.fft.fft(sr)
     return fft

python/cudf/cudf_pandas_tests/third_party_integration_tests/tests/test_pytorch.py

@@ -116,6 +116,9 @@ def test_torch_train(data):
     return model(test_x1, test_x2)
 
 
+@pytest.mark.skip(
+    reason="AssertionError: The values for attribute 'device' do not match: cpu != cuda:0."
+)
 def test_torch_tensor_ctor():
     s = pd.Series(range(5))
     return torch.tensor(s.values)

python/cudf/cudf_pandas_tests/third_party_integration_tests/tests/test_seaborn.py

@@ -54,6 +54,9 @@ def test_scatter(df):
     return ax
 
 
+@pytest.mark.skip(
+    reason="AttributeError: 'ndarray' object has no attribute '_fsproxy_wrapped'"
+)
 def test_lineplot_with_sns_data():
     df = sns.load_dataset("flights")
     ax = sns.lineplot(data=df, x="month", y="passengers")

python/cudf/cudf_pandas_tests/third_party_integration_tests/tests/test_stumpy_distributed.py

@@ -41,7 +41,7 @@ def test_multidimensional_distributed_timeseries(dask_client):
     rng = np.random.default_rng(seed=42)
     # Each row represents data from a different dimension while each column represents
     # data from the same dimension
-    your_time_series = rng.random(3, 1000)
+    your_time_series = rng.random((3, 1000))
     # Approximately, how many data points might be found in a pattern
     window_size = 50
 

python/cudf/cudf_pandas_tests/third_party_integration_tests/tests/test_tensorflow.py

@@ -271,6 +271,7 @@ def call(self, values):
         return tf.concat(values, axis=-1)
 
 
+@pytest.mark.xfail(reason="ValueError: Invalid dtype: object")
 def test_full_example_train_with_df(df, target):
     # https://www.tensorflow.org/tutorials/load_data/pandas_dataframe#full_example
     # Inputs are directly passed as dictionary of series

python/cudf/cudf_pandas_tests/third_party_integration_tests/tests/test_xgboost.py

@@ -113,6 +113,9 @@ def test_with_external_memory(
     return predt
 
 
+@pytest.mark.skip(
+    reason="TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly."
+)
 @pytest.mark.parametrize("device", ["cpu", "cuda"])
 def test_predict(device: str) -> np.ndarray:
     reg = xgb.XGBRegressor(n_estimators=2, device=device)