Tutorial ini menunjukkan cara menggunakan Pipeline Platform Agen Gemini Enterprise untuk menjalankan alur kerja ML secara menyeluruh, termasuk tugas-tugas berikut:
- Mengimpor dan mengubah data.
- Melatih model menggunakan framework ML yang dipilih.
- Impor model terlatih ke Gemini Enterprise Agent Platform Model Registry.
- Opsional: Men-deploy model untuk penyaluran online dengan Inferensi Vertex AI.
Sebelum memulai
Pastikan Anda telah menyelesaikan tugas 1-3 di bagian Menyiapkan project dan lingkungan pengembangan. Google Cloud
Instal Agent Platform SDK untuk Python dan Kubeflow Pipelines SDK:
python3 -m pip install "kfp<2.0.0" "google-cloud-aiplatform>=1.16.0" --upgrade --quietPastikan Anda memiliki izin IAM berikut:
**aiplatform.metadataStores.get****storage.buckets.get****storage.objects.create****storage.objects.get**
Anda harus memiliki izin ini untuk menggunakan Pipeline Gemini Enterprise Agent Platform guna menjalankan pipeline.
Menjalankan pipeline pelatihan model ML
Pilih tujuan pelatihan dan framework ML di dalam tab berikut ini untuk mendapatkan kode contoh yang dapat dijalankan di dalam lingkungan Anda. Kode contoh melakukan hal berikut ini:
- Memuat komponen dari repositori komponen untuk digunakan sebagai elemen penyusun pipeline.
- Menyusun pipeline dengan membuat tugas komponen, dan meneruskan data di antara tugas-tugas komponen menggunakan argumen.
- Mengirimkan pipeline untuk eksekusi di Gemini Enterprise Agent Platform Pipelines. Lihat Harga Pipeline Gemini Enterprise Agent Platform.
Salin kode tersebut ke dalam lingkungan pengembangan Anda, lalu jalankan.
Klasifikasi berbentuk tabel
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()
Regresi berbentuk tabulasi
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()
Perhatikan hal-hal berikut tentang contoh kode yang diberikan:
- Pipeline Kubeflow didefinisikan sebagai fungsi Python.
- Langkah-langkah alur kerja pipeline dibuat menggunakan komponen pipeline
Kubeflow. Dengan menggunakan output komponen sebagai input komponen
lain, Anda menentukan alur kerja pipeline sebagai grafik. Misalnya, tugas komponen
fill_all_missing_values_using_Pandas_on_CSV_data_opbergantung pada outputtransformed_tabledari tugas komponenselect_columns_using_Pandas_on_CSV_data_op. - Anda membuat proses pipeline di dalam Gemini Enterprise Agent Platform Pipelines menggunakan Agent Platform SDK untuk Python.
Memantau pipeline
Di konsol Google Cloud , di bagian Agent Platform, buka halaman Pipelines, lalu buka tab Runs.
Langkah berikutnya
- Untuk mempelajari lebih lanjut Pipeline Platform Agen Gemini Enterprise, lihat Pengantar Pipeline Platform Agen Gemini Enterprise.