Benutzerdefinierte ML-Modelle in Vertex AI Pipelines trainieren

In dieser Anleitung wird gezeigt, wie Sie Vertex AI Pipelines verwenden, um einen End-to-End-ML-Workflow auszuführen. Dazu gehören folgende Aufgaben:

  • Daten importieren und transformieren
  • Modell mit dem ausgewählten ML-Framework trainieren.
  • Importieren Sie das trainierte Modell in Vertex AI Model Registry.
  • Optional: Stellen Sie das Modell für die Online-Bereitstellung mit Vertex AI Prediction bereit.

Hinweis

  1. Führen Sie die Aufgaben 1–3 unter Google Cloud-Projekt und Entwicklungsumgebung einrichten aus.

  2. Installieren Sie das Vertex AI SDK für Python und das Kubeflow Pipelines SDK:

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

Führen Sie die ML-Modelltrainingspipeline aus

Wählen Sie auf den folgenden Tabs das Trainingsziel und das ML-Framework aus, um Beispielcode zu erhalten, den Sie in Ihrer Umgebung ausführen können. Dieser Beispielcode tut Folgendes:

  • Lädt Komponenten aus einem Komponenten-Repository, die als Pipeline-Bausteine verwendet werden sollen.
  • Erstellt eine Pipeline; dazu werden Komponentenaufgaben erstellt und Daten zwischen ihnen über Argumente übergeben.
  • Die Pipeline wird zur Ausführung in Vertex AI Pipelines gesendet. Weitere Informationen finden Sie unter Vertex AI Pipelines: Preise.

Kopieren Sie den Code in Ihre Entwicklungsumgebung und führen Sie ihn aus.

Tabellarische Klassifizierung

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()

Tabellarische Regression

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()

Beachten Sie die Hinweise zu den Codebeispielen:

  • Eine Kubeflow-Pipeline ist als Python-Funktion definiert.
  • Die Workflowschritte der Pipeline werden mit Kubeflow-Pipeline-Komponenten erstellt. Wenn Sie die Ausgaben einer Komponente als Eingabe einer anderen Komponente verwenden, definieren Sie den Workflow der Pipeline als Grafik. Beispielsweise hängt die Aufgabe der Komponente fill_all_missing_values_using_Pandas_on_CSV_data_op von der transformed_table-Ausgabe der Aufgabe der Komponente select_columns_using_Pandas_on_CSV_data_op ab.
  • Erstellen Sie eine Pipeline in Vertex AI Pipelines mit dem Vertex AI SDK für Python.

Pipeline überwachen

Rufen Sie in der Google Cloud Console im Abschnitt „Vertex AI“ die Seite Pipelines auf und öffnen Sie den Tab Ausführungen.

Zu den Pipeline-Ausführungen

Nächste Schritte