chore: fix CI by adding a dependency of python/sdk for the e2e-test

sdk/python/test_data/pipelines/xgboost_sample_pipeline.py

@@ -0,0 +1,95 @@
+# Copyright 2021 The Kubeflow Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from kfp import compiler
+from kfp import components
+from kfp import dsl
+
+chicago_taxi_dataset_op = components.load_component_from_url(
+    'https://raw.githubusercontent.com/kubeflow/pipelines/60a2612541ec08c6a85c237d2ec7525b12543a43/components/datasets/Chicago_Taxi_Trips/component.yaml'
+)
+convert_csv_to_apache_parquet_op = components.load_component_from_url(
+    'https://raw.githubusercontent.com/kubeflow/pipelines/0d7d6f41c92bdc05c2825232afe2b47e5cb6c4b3/components/_converters/ApacheParquet/from_CSV/component.yaml'
+)
+xgboost_train_on_csv_op = components.load_component_from_url(
+    'https://raw.githubusercontent.com/kubeflow/pipelines/567c04c51ff00a1ee525b3458425b17adbe3df61/components/XGBoost/Train/component.yaml'
+)
+xgboost_predict_on_csv_op = components.load_component_from_url(
+    'https://raw.githubusercontent.com/kubeflow/pipelines/31939086d66d633732f75300ce69eb60e9fb0269/components/XGBoost/Predict/component.yaml'
+)
+xgboost_train_on_parquet_op = components.load_component_from_url(
+    'https://raw.githubusercontent.com/kubeflow/pipelines/0ae2f30ff24beeef1c64cc7c434f1f652c065192/components/XGBoost/Train/from_ApacheParquet/component.yaml'
+)
+xgboost_predict_on_parquet_op = components.load_component_from_url(
+    'https://raw.githubusercontent.com/kubeflow/pipelines/31939086d66d633732f75300ce69eb60e9fb0269/components/XGBoost/Predict/from_ApacheParquet/component.yaml'
+)
+
+
+@dsl.pipeline(name='xgboost-sample-pipeline')
+def xgboost_pipeline():
+    training_data_csv = chicago_taxi_dataset_op(
+        where='trip_start_timestamp >= "2019-01-01" AND trip_start_timestamp < "2019-02-01"',
+        select='tips,trip_seconds,trip_miles,pickup_community_area,dropoff_community_area,fare,tolls,extras,trip_total',
+        limit=10000,
+    ).output
+
+    # Training and prediction on dataset in CSV format
+    model_trained_on_csv = xgboost_train_on_csv_op(
+        training_data=training_data_csv,
+        label_column=0,
+        objective='reg:squarederror',
+        num_iterations=200,
+    ).outputs['model']
+
+    xgboost_predict_on_csv_op(
+        data=training_data_csv,
+        model=model_trained_on_csv,
+        label_column=0,
+    )
+
+    # Training and prediction on dataset in Apache Parquet format
+    training_data_parquet = convert_csv_to_apache_parquet_op(
+        data=training_data_csv).output
+
+    model_trained_on_parquet = xgboost_train_on_parquet_op(
+        training_data=training_data_parquet,
+        label_column_name='tips',
+        objective='reg:squarederror',
+        num_iterations=200,
+    ).outputs['model']
+
+    xgboost_predict_on_parquet_op(
+        data=training_data_parquet,
+        model=model_trained_on_parquet,
+        label_column_name='tips',
+    )
+
+    # Checking cross-format predictions
+    xgboost_predict_on_parquet_op(
+        data=training_data_parquet,
+        model=model_trained_on_csv,
+        label_column_name='tips',
+    )
+
+    xgboost_predict_on_csv_op(
+        data=training_data_csv,
+        model=model_trained_on_parquet,
+        label_column=0,
+    )
+
+
+if __name__ == '__main__':
+    compiler.Compiler().compile(
+        pipeline_func=xgboost_pipeline,
+        package_path=__file__.replace('.py', '.yaml'))