Questo tutorial mostra come utilizzare Vertex AI Pipelines per eseguire un flusso di lavoro ML end-to-end, incluse le seguenti attività:
- Importare e trasformare i dati.
- Addestrare un modello utilizzando il framework ML selezionato.
- Importare il modello addestrato in Vertex AI Model Registry.
- Facoltativo: eseguire il deployment del modello per l'erogazione online con Vertex AI Inference.
Prima di iniziare
Assicurati di aver completato le attività 1-3 in Configurare un Google Cloud progetto e un ambiente di sviluppo.
Installa l' SDK Agent Platform per Python e l'SDK Kubeflow Pipelines:
python3 -m pip install "kfp<2.0.0" "google-cloud-aiplatform>=1.16.0" --upgrade --quietAssicurati di disporre delle seguenti autorizzazioni IAM:
**aiplatform.metadataStores.get****storage.buckets.get****storage.objects.create****storage.objects.get**
Devi disporre di queste autorizzazioni per utilizzare Vertex AI Pipelines per eseguire le pipeline.
Eseguire la pipeline di addestramento del modello ML
Scegli l'obiettivo di addestramento e il framework ML nelle seguenti schede per ottenere un codice campione che puoi eseguire nel tuo ambiente. Il codice campione esegue queste operazioni:
- Carica i componenti da un repository di componenti da utilizzare come blocchi di creazione della pipeline.
- Compone una pipeline creando attività di componenti e passando i dati tra di essi utilizzando gli 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("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()
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("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()
Tieni presente quanto segue in merito agli 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 di Kubeflow. Utilizzando gli output di un componente come input di un altro componente, il flusso di lavoro della pipeline viene definito come grafo. 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. - L'esecuzione di una pipeline deve essere eseguita su Vertex AI Pipelines utilizzando l'SDK Agent Platform per Python.
Monitorare la pipeline
Nella Google Cloud console, nella sezione Agent Platform, vai alla pagina Pipeline e apri la scheda Esecuzioni.
Vai alle esecuzioni della pipeline
Passaggi successivi
- Per saperne di più su Vertex AI Pipelines, consulta Introduzione a Vertex AI Pipelines.