feat(components): PyTorch - Train fully-connected network pipeline (#5208)

components/PyTorch/_samples/Train_fully-connected_network.pipeline.py

@@ -0,0 +1,56 @@
+from kfp import components
+
+
+chicago_taxi_dataset_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/e3337b8bdcd63636934954e592d4b32c95b49129/components/datasets/Chicago%20Taxi/component.yaml')
+pandas_transform_csv_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/6162d55998b176b50267d351241100bb0ee715bc/components/pandas/Transform_DataFrame/in_CSV_format/component.yaml')
+
+create_fully_connected_pytorch_network_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/4e1facea1a270535b515a9e8cc59422d1ad76a9e/components/PyTorch/Create_fully_connected_network/component.yaml')
+train_pytorch_model_from_csv_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/603342c4b88fe2d69ff07682f702cd3601e883bb/components/PyTorch/Train_PyTorch_model/from_CSV/component.yaml')
+convert_to_onnx_from_pytorch_script_module_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/e011e4affa85542ef2b24d63fdac27f8d939bbee/components/PyTorch/Convert_to_OnnxModel_from_PyTorchScriptModule/component.yaml')
+
+
+def pytorch_pipeline():
+    feature_columns = ['trip_seconds', 'trip_miles', 'pickup_community_area', 'dropoff_community_area', 'fare', 'tolls', 'extras']  # Excluded 'trip_total'
+    label_column = 'tips'
+    network = create_fully_connected_pytorch_network_op(
+        layer_sizes=[len(feature_columns), 100, 10, 1],
+        activation_name='elu',
+    ).output
+
+    training_data = chicago_taxi_dataset_op(
+        where='trip_start_timestamp >= "2019-01-01" AND trip_start_timestamp < "2019-02-01"',
+        select=','.join([label_column] + feature_columns),
+        limit=10000,
+    ).output
+
+    training_data = pandas_transform_csv_op(
+        table=training_data,
+        transform_code='''df = df.fillna({'tolls': 0.0, 'extras': 0.0}); df = df.dropna(axis='index')''',
+    ).output
+
+    trained_model = train_pytorch_model_from_csv_op(
+        model=network,
+        training_data=training_data,
+        label_column_name=label_column,
+        loss_function_name='mse_loss',
+        # Optional:
+        batch_size=32,
+        number_of_epochs=2,
+        random_seed=0,
+        learning_rate=0.1,
+        optimizer_name='Adadelta',
+        optimizer_parameters={},
+    ).outputs['trained_model']
+
+    convert_to_onnx_from_pytorch_script_module_op(
+        model=trained_model,
+        list_of_input_shapes=[[len(feature_columns)]],
+    )
+
+if __name__ == '__main__':
+    import kfp
+    kfp_endpoint=None
+    kfp.Client(host=kfp_endpoint).create_run_from_pipeline_func(
+        pytorch_pipeline,
+        arguments={},
+    )