Entrena modelos de AA personalizados en Vertex AI Pipelines

En este instructivo, se muestra cómo usar Vertex AI Pipelines para ejecutar un flujo de trabajo de AA de extremo a extremo, incluidas las siguientes tareas:

  • Importar y transformar datos
  • Entrenar un modelo con el framework de AA seleccionado
  • Importar el modelo entrenado a Vertex AI Model Registry
  • Opcional: Implementa el modelo para la entrega en línea con Vertex AI Prediction.

Antes de comenzar

  1. Asegúrate de haber completado las tareas 1 y 3 en Configura un proyecto de Google Cloud y un entorno de desarrollo.

  2. Instala el SDK de Vertex AI para Python y el SDK de Kubeflow Pipelines:

    python3 -m pip install "kfp<2.0.0" "google-cloud-aiplatform>=1.16.0" --upgrade --quiet
    

Ejecuta la canalización de entrenamiento de modelos del AA

Elige el objetivo de entrenamiento y el marco de trabajo de AA en las siguientes pestañas para obtener un código de muestra que puedas ejecutar en tu entorno. El código de muestra hace lo siguiente:

  • Carga componentes de un repositorio de componentes para usarlos como componentes básicos de la canalización.
  • Redacta una canalización mediante la creación de tareas de componentes y el paso de datos entre ellas mediante argumentos.
  • Envía la canalización para su ejecución en Vertex AI Pipelines. Consulta los precios de Vertex AI Pipelines.

Copia el código en tu entorno de desarrollo y ejecútalo.

Clasificación tabular

TensorFlow

# python3 -m pip install "kfp<2.0.0" "google-cloud-aiplatform>=1.16.0" --upgrade --quiet
from kfp import components

# %% Loading components
download_from_gcs_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/storage/download/component.yaml")
select_columns_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Select_columns/in_CSV_format/component.yaml")
fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml")
binarize_column_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Binarize_column/in_CSV_format/component.yaml")
split_rows_into_subsets_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/dataset_manipulation/Split_rows_into_subsets/in_CSV/component.yaml")
create_fully_connected_tensorflow_network_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/tensorflow/Create_fully_connected_network/component.yaml")
train_model_using_Keras_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/tensorflow/Train_model_using_Keras/on_CSV/component.yaml")
predict_with_TensorFlow_model_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/tensorflow/Predict/on_CSV/component.yaml")
upload_Tensorflow_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_Tensorflow_model/component.yaml")
deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/component.yaml")

# %% Pipeline definition
def train_tabular_classification_model_using_TensorFlow_pipeline():
    dataset_gcs_uri = "gs://ml-pipeline-dataset/Chicago_taxi_trips/chicago_taxi_trips_2019-01-01_-_2019-02-01_limit=10000.csv"
    feature_columns = ["trip_seconds", "trip_miles", "pickup_community_area", "dropoff_community_area", "fare", "tolls", "extras"]  # Excluded "trip_total"
    label_column = "tips"
    training_set_fraction = 0.8
    # Deploying the model might incur additional costs over time
    deploy_model = False

    classification_label_column = "class"
    all_columns = [label_column] + feature_columns

    dataset = download_from_gcs_op(
        gcs_path=dataset_gcs_uri
    ).outputs["Data"]

    dataset = select_columns_using_Pandas_on_CSV_data_op(
        table=dataset,
        column_names=all_columns,
    ).outputs["transformed_table"]

    dataset = fill_all_missing_values_using_Pandas_on_CSV_data_op(
        table=dataset,
        replacement_value="0",
        # # Optional:
        # column_names=None,  # =[...]
    ).outputs["transformed_table"]

    classification_dataset = binarize_column_using_Pandas_on_CSV_data_op(
        table=dataset,
        column_name=label_column,
        predicate=" > 0",
        new_column_name=classification_label_column,
    ).outputs["transformed_table"]

    split_task = split_rows_into_subsets_op(
        table=classification_dataset,
        fraction_1=training_set_fraction,
    )
    classification_training_data = split_task.outputs["split_1"]
    classification_testing_data = split_task.outputs["split_2"]

    network = create_fully_connected_tensorflow_network_op(
        input_size=len(feature_columns),
        # Optional:
        hidden_layer_sizes=[10],
        activation_name="elu",
        output_activation_name="sigmoid",
        # output_size=1,
    ).outputs["model"]

    model = train_model_using_Keras_on_CSV_op(
        training_data=classification_training_data,
        model=network,
        label_column_name=classification_label_column,
        # Optional:
        loss_function_name="binary_crossentropy",
        number_of_epochs=10,
        #learning_rate=0.1,
        #optimizer_name="Adadelta",
        #optimizer_parameters={},
        #batch_size=32,
        #metric_names=["mean_absolute_error"],
        #random_seed=0,
    ).outputs["trained_model"]

    predictions = predict_with_TensorFlow_model_on_CSV_data_op(
        dataset=classification_testing_data,
        model=model,
        # label_column_name needs to be set when doing prediction on a dataset that has labels
        label_column_name=classification_label_column,
        # Optional:
        # batch_size=1000,
    ).outputs["predictions"]

    vertex_model_name = upload_Tensorflow_model_to_Google_Cloud_Vertex_AI_op(
        model=model,
    ).outputs["model_name"]

    # Deploying the model might incur additional costs over time
    if deploy_model:
        vertex_endpoint_name = deploy_model_to_endpoint_op(
            model_name=vertex_model_name,
        ).outputs["endpoint_name"]

pipeline_func = train_tabular_classification_model_using_TensorFlow_pipeline

# %% Pipeline submission
if __name__ == '__main__':
    from google.cloud import aiplatform
    aiplatform.PipelineJob.from_pipeline_func(pipeline_func=pipeline_func).submit()

PyTorch

# python3 -m pip install "kfp<2.0.0" "google-cloud-aiplatform>=1.16.0" --upgrade --quiet
from kfp import components

# %% Loading components
download_from_gcs_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/storage/download/component.yaml")
select_columns_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Select_columns/in_CSV_format/component.yaml")
fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml")
binarize_column_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Binarize_column/in_CSV_format/component.yaml")
create_fully_connected_pytorch_network_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/PyTorch/Create_fully_connected_network/component.yaml")
train_pytorch_model_from_csv_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/PyTorch/Train_PyTorch_model/from_CSV/component.yaml")
create_pytorch_model_archive_with_base_handler_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/PyTorch/Create_PyTorch_Model_Archive/with_base_handler/component.yaml")
upload_PyTorch_model_archive_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_PyTorch_model_archive/component.yaml")
deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/component.yaml")

# %% Pipeline definition
def train_tabular_classification_model_using_PyTorch_pipeline():
    dataset_gcs_uri = "gs://ml-pipeline-dataset/Chicago_taxi_trips/chicago_taxi_trips_2019-01-01_-_2019-02-01_limit=10000.csv"
    feature_columns = ["trip_seconds", "trip_miles", "pickup_community_area", "dropoff_community_area", "fare", "tolls", "extras"]  # Excluded "trip_total"
    label_column = "tips"
    # Deploying the model might incur additional costs over time
    deploy_model = False

    classification_label_column = "class"
    all_columns = [label_column] + feature_columns

    training_data = download_from_gcs_op(
        gcs_path=dataset_gcs_uri
    ).outputs["Data"]

    training_data = select_columns_using_Pandas_on_CSV_data_op(
        table=training_data,
        column_names=all_columns,
    ).outputs["transformed_table"]

    # Cleaning the NaN values.
    training_data = fill_all_missing_values_using_Pandas_on_CSV_data_op(
        table=training_data,
        replacement_value="0",
        #replacement_type_name="float",
    ).outputs["transformed_table"]

    classification_training_data = binarize_column_using_Pandas_on_CSV_data_op(
        table=training_data,
        column_name=label_column,
        predicate=" > 0",
        new_column_name=classification_label_column,
    ).outputs["transformed_table"]

    network = create_fully_connected_pytorch_network_op(
        input_size=len(feature_columns),
        # Optional:
        hidden_layer_sizes=[10],
        activation_name="elu",
        output_activation_name="sigmoid",
        # output_size=1,
    ).outputs["model"]

    model = train_pytorch_model_from_csv_op(
        model=network,
        training_data=classification_training_data,
        label_column_name=classification_label_column,
        loss_function_name="binary_cross_entropy",
        # Optional:
        #number_of_epochs=1,
        #learning_rate=0.1,
        #optimizer_name="Adadelta",
        #optimizer_parameters={},
        #batch_size=32,
        #batch_log_interval=100,
        #random_seed=0,
    ).outputs["trained_model"]

    model_archive = create_pytorch_model_archive_with_base_handler_op(
        model=model,
        # Optional:
        # model_name="model",
        # model_version="1.0",
    ).outputs["Model archive"]

    vertex_model_name = upload_PyTorch_model_archive_to_Google_Cloud_Vertex_AI_op(
        model_archive=model_archive,
    ).outputs["model_name"]

    # Deploying the model might incur additional costs over time
    if deploy_model:
        vertex_endpoint_name = deploy_model_to_endpoint_op(
            model_name=vertex_model_name,
        ).outputs["endpoint_name"]

pipeline_func=train_tabular_classification_model_using_PyTorch_pipeline

# %% Pipeline submission
if __name__ == '__main__':
    from google.cloud import aiplatform
    aiplatform.PipelineJob.from_pipeline_func(pipeline_func=pipeline_func).submit()

XGBoost

# python3 -m pip install "kfp<2.0.0" "google-cloud-aiplatform>=1.16.0" --upgrade --quiet
from kfp import components

# %% Loading components
download_from_gcs_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/storage/download/component.yaml")
select_columns_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Select_columns/in_CSV_format/component.yaml")
fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml")
binarize_column_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Binarize_column/in_CSV_format/component.yaml")
split_rows_into_subsets_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/dataset_manipulation/Split_rows_into_subsets/in_CSV/component.yaml")
train_XGBoost_model_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/XGBoost/Train/component.yaml")
xgboost_predict_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/XGBoost/Predict/component.yaml")
upload_XGBoost_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_XGBoost_model/component.yaml")
deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/component.yaml")

# %% Pipeline definition
def train_tabular_classification_model_using_XGBoost_pipeline():
    dataset_gcs_uri = "gs://ml-pipeline-dataset/Chicago_taxi_trips/chicago_taxi_trips_2019-01-01_-_2019-02-01_limit=10000.csv"
    feature_columns = ["trip_seconds", "trip_miles", "pickup_community_area", "dropoff_community_area", "fare", "tolls", "extras"]  # Excluded "trip_total"
    label_column = "tips"
    training_set_fraction = 0.8
    # Deploying the model might incur additional costs over time
    deploy_model = False

    classification_label_column = "class"
    all_columns = [label_column] + feature_columns

    dataset = download_from_gcs_op(
        gcs_path=dataset_gcs_uri
    ).outputs["Data"]

    dataset = select_columns_using_Pandas_on_CSV_data_op(
        table=dataset,
        column_names=all_columns,
    ).outputs["transformed_table"]

    dataset = fill_all_missing_values_using_Pandas_on_CSV_data_op(
        table=dataset,
        replacement_value="0",
        # # Optional:
        # column_names=None,  # =[...]
    ).outputs["transformed_table"]

    classification_dataset = binarize_column_using_Pandas_on_CSV_data_op(
        table=dataset,
        column_name=label_column,
        predicate="> 0",
        new_column_name=classification_label_column,
    ).outputs["transformed_table"]

    split_task = split_rows_into_subsets_op(
        table=classification_dataset,
        fraction_1=training_set_fraction,
    )
    classification_training_data = split_task.outputs["split_1"]
    classification_testing_data = split_task.outputs["split_2"]

    model = train_XGBoost_model_on_CSV_op(
        training_data=classification_training_data,
        label_column_name=classification_label_column,
        objective="binary:logistic",
        # Optional:
        #starting_model=None,
        #num_iterations=10,
        #booster_params={},
        #booster="gbtree",
        #learning_rate=0.3,
        #min_split_loss=0,
        #max_depth=6,
    ).outputs["model"]

    # Predicting on the testing data
    predictions = xgboost_predict_on_CSV_op(
        data=classification_testing_data,
        model=model,
        # label_column needs to be set when doing prediction on a dataset that has labels
        label_column_name=classification_label_column,
    ).outputs["predictions"]

    vertex_model_name = upload_XGBoost_model_to_Google_Cloud_Vertex_AI_op(
        model=model,
    ).outputs["model_name"]

    # Deploying the model might incur additional costs over time
    if deploy_model:
        vertex_endpoint_name = deploy_model_to_endpoint_op(
            model_name=vertex_model_name,
        ).outputs["endpoint_name"]

pipeline_func = train_tabular_classification_model_using_XGBoost_pipeline

# %% Pipeline submission
if __name__ == '__main__':
    from google.cloud import aiplatform
    aiplatform.PipelineJob.from_pipeline_func(pipeline_func=pipeline_func).submit()

Scikit-learn

# python3 -m pip install "kfp<2.0.0" "google-cloud-aiplatform>=1.16.0" --upgrade --quiet
from kfp import components

# %% Loading components
download_from_gcs_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/storage/download/component.yaml")
select_columns_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Select_columns/in_CSV_format/component.yaml")
fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml")
binarize_column_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Binarize_column/in_CSV_format/component.yaml")
train_logistic_regression_model_using_scikit_learn_from_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/1f5cf6e06409b704064b2086c0a705e4e6b4fcde/community-content/pipeline_components/ML_frameworks/Scikit_learn/Train_logistic_regression_model/from_CSV/component.yaml")
upload_Scikit_learn_pickle_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_Scikit-learn_pickle_model/component.yaml")
deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/component.yaml")

# %% Pipeline definition
def train_tabular_classification_logistic_regression_model_using_Scikit_learn_pipeline():
    dataset_gcs_uri = "gs://ml-pipeline-dataset/Chicago_taxi_trips/chicago_taxi_trips_2019-01-01_-_2019-02-01_limit=10000.csv"
    feature_columns = ["trip_seconds", "trip_miles", "pickup_community_area", "dropoff_community_area", "fare", "tolls", "extras"]  # Excluded "trip_total"
    label_column = "tips"
    # Deploying the model might incur additional costs over time
    deploy_model = False

    classification_label_column = "class"
    all_columns = [label_column] + feature_columns

    training_data = download_from_gcs_op(
        gcs_path=dataset_gcs_uri
    ).outputs["Data"]

    training_data = select_columns_using_Pandas_on_CSV_data_op(
        table=training_data,
        column_names=all_columns,
    ).outputs["transformed_table"]

    # Cleaning the NaN values.
    training_data = fill_all_missing_values_using_Pandas_on_CSV_data_op(
        table=training_data,
        replacement_value="0",
        #replacement_type_name="float",
    ).outputs["transformed_table"]

    classification_training_data = binarize_column_using_Pandas_on_CSV_data_op(
        table=training_data,
        column_name=label_column,
        predicate="> 0",
        new_column_name=classification_label_column,
    ).outputs["transformed_table"]

    model = train_logistic_regression_model_using_scikit_learn_from_CSV_op(
        dataset=classification_training_data,
        label_column_name=classification_label_column,
        # Optional:
        #penalty="l2",
        #solver="lbfgs",
        #max_iterations=100,
        #multi_class_mode="auto",
        #random_seed=0,
    ).outputs["model"]

    vertex_model_name = upload_Scikit_learn_pickle_model_to_Google_Cloud_Vertex_AI_op(
        model=model,
    ).outputs["model_name"]

    # Deploying the model might incur additional costs over time
    if deploy_model:
        sklearn_vertex_endpoint_name = deploy_model_to_endpoint_op(
            model_name=vertex_model_name,
        ).outputs["endpoint_name"]

pipeline_func = train_tabular_classification_logistic_regression_model_using_Scikit_learn_pipeline

# %% Pipeline submission
if __name__ == '__main__':
    from google.cloud import aiplatform
    aiplatform.PipelineJob.from_pipeline_func(pipeline_func=pipeline_func).submit()

Regresión tabular

TensorFlow

# python3 -m pip install "kfp<2.0.0" "google-cloud-aiplatform>=1.16.0" --upgrade --quiet
from kfp import components

# %% Loading components
download_from_gcs_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/storage/download/component.yaml")
select_columns_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Select_columns/in_CSV_format/component.yaml")
fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml")
split_rows_into_subsets_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/dataset_manipulation/Split_rows_into_subsets/in_CSV/component.yaml")
create_fully_connected_tensorflow_network_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/tensorflow/Create_fully_connected_network/component.yaml")
train_model_using_Keras_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/tensorflow/Train_model_using_Keras/on_CSV/component.yaml")
predict_with_TensorFlow_model_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/tensorflow/Predict/on_CSV/component.yaml")
upload_Tensorflow_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_Tensorflow_model/component.yaml")
deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/component.yaml")

# %% Pipeline definition
def train_tabular_regression_model_using_Tensorflow_pipeline():
    dataset_gcs_uri = "gs://ml-pipeline-dataset/Chicago_taxi_trips/chicago_taxi_trips_2019-01-01_-_2019-02-01_limit=10000.csv"
    feature_columns = ["trip_seconds", "trip_miles", "pickup_community_area", "dropoff_community_area", "fare", "tolls", "extras"]  # Excluded "trip_total"
    label_column = "tips"
    training_set_fraction = 0.8
    # Deploying the model might incur additional costs over time
    deploy_model = False

    all_columns = [label_column] + feature_columns

    dataset = download_from_gcs_op(
        gcs_path=dataset_gcs_uri
    ).outputs["Data"]

    dataset = select_columns_using_Pandas_on_CSV_data_op(
        table=dataset,
        column_names=all_columns,
    ).outputs["transformed_table"]

    dataset = fill_all_missing_values_using_Pandas_on_CSV_data_op(
        table=dataset,
        replacement_value="0",
        # # Optional:
        # column_names=None,  # =[...]
    ).outputs["transformed_table"]

    split_task = split_rows_into_subsets_op(
        table=dataset,
        fraction_1=training_set_fraction,
    )
    training_data = split_task.outputs["split_1"]
    testing_data = split_task.outputs["split_2"]

    network = create_fully_connected_tensorflow_network_op(
        input_size=len(feature_columns),
        # Optional:
        hidden_layer_sizes=[10],
        activation_name="elu",
        # output_activation_name=None,
        # output_size=1,
    ).outputs["model"]

    model = train_model_using_Keras_on_CSV_op(
        training_data=training_data,
        model=network,
        label_column_name=label_column,
        # Optional:
        #loss_function_name="mean_squared_error",
        number_of_epochs=10,
        #learning_rate=0.1,
        #optimizer_name="Adadelta",
        #optimizer_parameters={},
        #batch_size=32,
        metric_names=["mean_absolute_error"],
        #random_seed=0,
    ).outputs["trained_model"]

    predictions = predict_with_TensorFlow_model_on_CSV_data_op(
        dataset=testing_data,
        model=model,
        # label_column_name needs to be set when doing prediction on a dataset that has labels
        label_column_name=label_column,
        # Optional:
        # batch_size=1000,
    ).outputs["predictions"]

    vertex_model_name = upload_Tensorflow_model_to_Google_Cloud_Vertex_AI_op(
        model=model,
    ).outputs["model_name"]

    # Deploying the model might incur additional costs over time
    if deploy_model:
        vertex_endpoint_name = deploy_model_to_endpoint_op(
            model_name=vertex_model_name,
        ).outputs["endpoint_name"]

pipeline_func=train_tabular_regression_model_using_Tensorflow_pipeline

# %% Pipeline submission
if __name__ == '__main__':
    from google.cloud import aiplatform
    aiplatform.PipelineJob.from_pipeline_func(pipeline_func=pipeline_func).submit()

PyTorch

# python3 -m pip install "kfp<2.0.0" "google-cloud-aiplatform>=1.16.0" --upgrade --quiet
from kfp import components

# %% Loading components
download_from_gcs_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/storage/download/component.yaml")
select_columns_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Select_columns/in_CSV_format/component.yaml")
fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml")
create_fully_connected_pytorch_network_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/PyTorch/Create_fully_connected_network/component.yaml")
train_pytorch_model_from_csv_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/PyTorch/Train_PyTorch_model/from_CSV/component.yaml")
create_pytorch_model_archive_with_base_handler_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/PyTorch/Create_PyTorch_Model_Archive/with_base_handler/component.yaml")
upload_PyTorch_model_archive_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_PyTorch_model_archive/component.yaml")
deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/component.yaml")

# %% Pipeline definition
def train_tabular_regression_model_using_PyTorch_pipeline():
    dataset_gcs_uri = "gs://ml-pipeline-dataset/Chicago_taxi_trips/chicago_taxi_trips_2019-01-01_-_2019-02-01_limit=10000.csv"
    feature_columns = ["trip_seconds", "trip_miles", "pickup_community_area", "dropoff_community_area", "fare", "tolls", "extras"]  # Excluded "trip_total"
    label_column = "tips"
    all_columns = [label_column] + feature_columns
    # Deploying the model might incur additional costs over time
    deploy_model = False

    training_data = download_from_gcs_op(
        gcs_path=dataset_gcs_uri
    ).outputs["Data"]

    training_data = select_columns_using_Pandas_on_CSV_data_op(
        table=training_data,
        column_names=all_columns,
    ).outputs["transformed_table"]

    # Cleaning the NaN values.
    training_data = fill_all_missing_values_using_Pandas_on_CSV_data_op(
        table=training_data,
        replacement_value="0",
        #replacement_type_name="float",
    ).outputs["transformed_table"]

    network = create_fully_connected_pytorch_network_op(
        input_size=len(feature_columns),
        # Optional:
        hidden_layer_sizes=[10],
        activation_name="elu",
        # output_activation_name=None,
        # output_size=1,
    ).outputs["model"]

    model = train_pytorch_model_from_csv_op(
        model=network,
        training_data=training_data,
        label_column_name=label_column,
        # Optional:
        #loss_function_name="mse_loss",
        #number_of_epochs=1,
        #learning_rate=0.1,
        #optimizer_name="Adadelta",
        #optimizer_parameters={},
        #batch_size=32,
        #batch_log_interval=100,
        #random_seed=0,
    ).outputs["trained_model"]

    model_archive = create_pytorch_model_archive_with_base_handler_op(
        model=model,
        # Optional:
        # model_name="model",
        # model_version="1.0",
    ).outputs["Model archive"]

    vertex_model_name = upload_PyTorch_model_archive_to_Google_Cloud_Vertex_AI_op(
        model_archive=model_archive,
    ).outputs["model_name"]

    # Deploying the model might incur additional costs over time
    if deploy_model:
        vertex_endpoint_name = deploy_model_to_endpoint_op(
            model_name=vertex_model_name,
        ).outputs["endpoint_name"]

pipeline_func=train_tabular_regression_model_using_PyTorch_pipeline

# %% Pipeline submission
if __name__ == '__main__':
    from google.cloud import aiplatform
    aiplatform.PipelineJob.from_pipeline_func(pipeline_func=pipeline_func).submit()

XGBoost

# python3 -m pip install "kfp<2.0.0" "google-cloud-aiplatform>=1.16.0" --upgrade --quiet
from kfp import components

# %% Loading components
download_from_gcs_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/storage/download/component.yaml")
select_columns_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Select_columns/in_CSV_format/component.yaml")
fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml")
split_rows_into_subsets_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/dataset_manipulation/Split_rows_into_subsets/in_CSV/component.yaml")
train_XGBoost_model_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/XGBoost/Train/component.yaml")
xgboost_predict_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/XGBoost/Predict/component.yaml")
upload_XGBoost_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_XGBoost_model/component.yaml")
deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/component.yaml")

# %% Pipeline definition
def train_tabular_regression_model_using_XGBoost_pipeline():
    dataset_gcs_uri = "gs://ml-pipeline-dataset/Chicago_taxi_trips/chicago_taxi_trips_2019-01-01_-_2019-02-01_limit=10000.csv"
    feature_columns = ["trip_seconds", "trip_miles", "pickup_community_area", "dropoff_community_area", "fare", "tolls", "extras"]  # Excluded "trip_total"
    label_column = "tips"
    training_set_fraction = 0.8
    # Deploying the model might incur additional costs over time
    deploy_model = False

    all_columns = [label_column] + feature_columns

    dataset = download_from_gcs_op(
        gcs_path=dataset_gcs_uri
    ).outputs["Data"]

    dataset = select_columns_using_Pandas_on_CSV_data_op(
        table=dataset,
        column_names=all_columns,
    ).outputs["transformed_table"]

    dataset = fill_all_missing_values_using_Pandas_on_CSV_data_op(
        table=dataset,
        replacement_value="0",
        # # Optional:
        # column_names=None,  # =[...]
    ).outputs["transformed_table"]

    split_task = split_rows_into_subsets_op(
        table=dataset,
        fraction_1=training_set_fraction,
    )
    training_data = split_task.outputs["split_1"]
    testing_data = split_task.outputs["split_2"]

    model = train_XGBoost_model_on_CSV_op(
        training_data=training_data,
        label_column_name=label_column,
        # Optional:
        #starting_model=None,
        #num_iterations=10,
        #booster_params={},
        #objective="reg:squarederror",
        #booster="gbtree",
        #learning_rate=0.3,
        #min_split_loss=0,
        #max_depth=6,
    ).outputs["model"]

    # Predicting on the testing data
    predictions = xgboost_predict_on_CSV_op(
        data=testing_data,
        model=model,
        # label_column needs to be set when doing prediction on a dataset that has labels
        label_column_name=label_column,
    ).outputs["predictions"]

    vertex_model_name = upload_XGBoost_model_to_Google_Cloud_Vertex_AI_op(
        model=model,
    ).outputs["model_name"]

    # Deploying the model might incur additional costs over time
    if deploy_model:
        vertex_endpoint_name = deploy_model_to_endpoint_op(
            model_name=vertex_model_name,
        ).outputs["endpoint_name"]

pipeline_func = train_tabular_regression_model_using_XGBoost_pipeline

# %% Pipeline submission
if __name__ == '__main__':
    from google.cloud import aiplatform
    aiplatform.PipelineJob.from_pipeline_func(pipeline_func=pipeline_func).submit()

Scikit-learn

# python3 -m pip install "kfp<2.0.0" "google-cloud-aiplatform>=1.16.0" --upgrade --quiet
from kfp import components

# %% Loading components
download_from_gcs_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/storage/download/component.yaml")
select_columns_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Select_columns/in_CSV_format/component.yaml")
fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml")
train_linear_regression_model_using_scikit_learn_from_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/1f5cf6e06409b704064b2086c0a705e4e6b4fcde/community-content/pipeline_components/ML_frameworks/Scikit_learn/Train_linear_regression_model/from_CSV/component.yaml")
upload_Scikit_learn_pickle_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_Scikit-learn_pickle_model/component.yaml")
deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/component.yaml")

# %% Pipeline definition
def train_tabular_regression_linear_model_using_Scikit_learn_pipeline():
    dataset_gcs_uri = "gs://ml-pipeline-dataset/Chicago_taxi_trips/chicago_taxi_trips_2019-01-01_-_2019-02-01_limit=10000.csv"
    feature_columns = ["trip_seconds", "trip_miles", "pickup_community_area", "dropoff_community_area", "fare", "tolls", "extras"]  # Excluded "trip_total"
    label_column = "tips"
    all_columns = [label_column] + feature_columns
    # Deploying the model might incur additional costs over time
    deploy_model = False

    training_data = download_from_gcs_op(
        gcs_path=dataset_gcs_uri
    ).outputs["Data"]

    training_data = select_columns_using_Pandas_on_CSV_data_op(
        table=training_data,
        column_names=all_columns,
    ).outputs["transformed_table"]

    # Cleaning the NaN values.
    training_data = fill_all_missing_values_using_Pandas_on_CSV_data_op(
        table=training_data,
        replacement_value="0",
        #replacement_type_name="float",
    ).outputs["transformed_table"]

    model = train_linear_regression_model_using_scikit_learn_from_CSV_op(
        dataset=training_data,
        label_column_name=label_column,
    ).outputs["model"]

    vertex_model_name = upload_Scikit_learn_pickle_model_to_Google_Cloud_Vertex_AI_op(
        model=model,
    ).outputs["model_name"]

    # Deploying the model might incur additional costs over time
    if deploy_model:
        sklearn_vertex_endpoint_name = deploy_model_to_endpoint_op(
            model_name=vertex_model_name,
        ).outputs["endpoint_name"]

pipeline_func = train_tabular_regression_linear_model_using_Scikit_learn_pipeline

# %% Pipeline submission
if __name__ == '__main__':
    from google.cloud import aiplatform
    aiplatform.PipelineJob.from_pipeline_func(pipeline_func=pipeline_func).submit()

Ten en cuenta lo siguiente sobre las muestras de código proporcionadas:

  • Una canalización de Kubeflow se define como una función de Python.
  • Los pasos del flujo de trabajo de la canalización se crean mediante los componentes de la canalización de Kubeflow. Cuando usas los resultados de un componente como entrada de otro componente, defines el flujo de trabajo de la canalización como un grafo. Por ejemplo, la tarea de componente fill_all_missing_values_using_Pandas_on_CSV_data_op depende de la salida transformed_table de la tarea de componente select_columns_using_Pandas_on_CSV_data_op.
  • Crea una canalización que se ejecute en Vertex AI Pipelines mediante el SDK de Vertex AI para Python.

Supervisa la canalización

En la consola de Google Cloud, en la sección Vertex AI, ve a la página Canalizaciones y abre la pestaña Ejecuciones.

Ir a Ejecuciones de canalización

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