אימון מודלים של ML בהתאמה אישית ב-Vertex AI Pipelines

במדריך הזה מוסבר איך להשתמש ב-Vertex AI Pipelines כדי להריץ תהליך עבודה של למידת מכונה מקצה לקצה, כולל המשימות הבאות:

  • ייבוא והמרה של נתונים.
  • מאמנים מודל באמצעות מסגרת למידת המכונה שנבחרה.
  • מייבאים את המודל המאומן אל מרשם המודלים של Vertex AI.
  • אופציונלי: פריסת המודל לצורך מילוי בקשה באופן מיידי באמצעות Vertex AI Inference.

לפני שמתחילים

  1. מוודאים שהשלמתם את משימות 1-3 במאמר הגדרת פרויקט וסביבת פיתוח. Google Cloud

  2. מתקינים את Vertex AI SDK ל-Python ואת Kubeflow Pipelines SDK:

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

  3. ודאו שיש לכם את הרשאות ה-IAM הבאות:

    • **aiplatform.metadataStores.get**
    • **storage.buckets.get**
    • **storage.objects.create**
    • **storage.objects.get**

    כדי להשתמש ב-Vertex AI Pipelines להרצת צינורות, צריך לקבל את ההרשאות הבאות.

הפעלת צינור עיבוד נתונים לאימון מודל למידת מכונה

בכרטיסיות הבאות אפשר לבחור יעד אימון ומסגרת ML כדי לקבל קוד לדוגמה שאפשר להריץ בסביבה שלכם. קוד לדוגמה:

  • טוען רכיבים ממאגר רכיבים לשימוש כאבני בניין של צינורות.
  • יוצר צינור עיבוד נתונים על ידי יצירת משימות של רכיבים והעברת נתונים ביניהם באמצעות ארגומנטים.
  • שולחת את צינור עיבוד הנתונים להרצה ב-Vertex AI Pipelines. ראו מחירון Vertex AI Pipelines.

מעתיקים את הקוד לסביבת הפיתוח ומפעילים פתרונות חכמים.

סיווג טבלאי

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()

רגרסיה טבלאית

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()

חשוב לשים לב לנקודות הבאות לגבי דוגמאות הקוד שמופיעות כאן:

  • צינור Kubeflow מוגדר כפונקציית Python.
  • שלבי תהליך העבודה של צינור עיבוד הנתונים נוצרים באמצעות רכיבי Kubeflow pipeline. כשמשתמשים בפלטים של רכיב כקלט של רכיב אחר, מגדירים את תהליך העבודה של צינור עיבוד הנתונים כגרף. לדוגמה, משימת הרכיב fill_all_missing_values_using_Pandas_on_CSV_data_op תלויה בפלט transformed_table ממשימת הרכיב select_columns_using_Pandas_on_CSV_data_op.
  • אתם יוצרים הרצה של צינור עיבוד נתונים ב-Vertex AI Pipelines באמצעות Vertex AI SDK ל-Python.

מעקב אחרי צינור עיבוד הנתונים

במסוף Google Cloud , בקטע Vertex AI, עוברים לדף Pipelines ופותחים את הכרטיסייה Runs.

מעבר אל Pipeline runs

המאמרים הבאים