Questo tutorial mostra come utilizzare Vertex AI Pipelines per eseguire un flusso di lavoro ML end-to-end, che include le attività seguenti:
- Importa e trasforma i dati.
- Addestra un modello utilizzando il framework ML selezionato.
- Importa il modello addestrato in Vertex AI Model Registry.
- Facoltativo: esegui il deployment del modello per la pubblicazione online con Vertex AI Prediction.
Prima di iniziare
Assicurati di aver completato le attività 1-3 descritte in Configurare un progetto Google Cloud e un ambiente di sviluppo.
Installa l'SDK Vertex AI per Python e l'SDK Kubeflow Pipelines:
python3 -m pip install "kfp<2.0.0" "google-cloud-aiplatform>=1.16.0" --upgrade --quiet
Esegui la pipeline di addestramento del modello ML
Scegli l'obiettivo dell'addestramento e il framework ML nelle seguenti schede per ottenere codice campione che puoi eseguire nel tuo ambiente. Il codice di esempio esegue queste operazioni:
- Carica i componenti da un repository di componenti da utilizzare come componenti di base della pipeline.
- Crea una pipeline creando attività del componente e passando dati tra loro utilizzando argomenti.
- Invia la pipeline per l'esecuzione su Vertex AI Pipelines. Consulta i prezzi di Vertex AI Pipelines.
Copia il codice nel tuo ambiente di sviluppo ed eseguilo.
Classificazione tabulare
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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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()
Regressione tabulare
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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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("http://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()
Tieni presente quanto segue sugli esempi di codice forniti:
- Una pipeline Kubeflow è definita come funzione Python.
- I passaggi del flusso di lavoro della pipeline vengono creati utilizzando i componenti della pipeline Kubeflow. Utilizzando gli output di un componente come input di un altro componente, definisci il flusso di lavoro della pipeline come grafico. Ad esempio, l'attività del componente
fill_all_missing_values_using_Pandas_on_CSV_data_opdipende dall'outputtransformed_tabledell'attività del componenteselect_columns_using_Pandas_on_CSV_data_op. - Puoi creare un'esecuzione della pipeline su Vertex AI Pipelines utilizzando l'SDK Vertex AI per Python.
Monitora la pipeline
Nella sezione Vertex AI della console Google Cloud, vai alla pagina Pipeline e apri la scheda Esecuzioni.
Passaggi successivi
- Per scoprire di più su Vertex AI Pipelines, consulta Introduzione a Vertex AI Pipelines.