from google.cloud import aiplatform
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value
def create_training_pipeline_tabular_forecasting_sample(
project: str,
display_name: str,
dataset_id: str,
model_display_name: str,
target_column: str,
time_series_identifier_column: str,
time_column: str,
time_series_attribute_columns: str,
unavailable_at_forecast: str,
available_at_forecast: str,
forecast_horizon: int,
location: str = "us-central1",
api_endpoint: str = "us-central1-aiplatform.googleapis.com",
):
# The AI Platform services require regional API endpoints.
client_options = {"api_endpoint": api_endpoint}
# Initialize client that will be used to create and send requests.
# This client only needs to be created once, and can be reused for multiple requests.
client = aiplatform.gapic.PipelineServiceClient(client_options=client_options)
# set the columns used for training and their data types
transformations = [
{"auto": {"column_name": "date"}},
{"auto": {"column_name": "state_name"}},
{"auto": {"column_name": "county_fips_code"}},
{"auto": {"column_name": "confirmed_cases"}},
{"auto": {"column_name": "deaths"}},
]
data_granularity = {"unit": "day", "quantity": 1}
# the inputs should be formatted according to the training_task_definition yaml file
training_task_inputs_dict = {
# required inputs
"targetColumn": target_column,
"timeSeriesIdentifierColumn": time_series_identifier_column,
"timeColumn": time_column,
"transformations": transformations,
"dataGranularity": data_granularity,
"optimizationObjective": "minimize-rmse",
"trainBudgetMilliNodeHours": 8000,
"timeSeriesAttributeColumns": time_series_attribute_columns,
"unavailableAtForecast": unavailable_at_forecast,
"availableAtForecast": available_at_forecast,
"forecastHorizon": forecast_horizon,
}
training_task_inputs = json_format.ParseDict(training_task_inputs_dict, Value())
training_pipeline = {
"display_name": display_name,
"training_task_definition": "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_forecasting_1.0.0.yaml",
"training_task_inputs": training_task_inputs,
"input_data_config": {
"dataset_id": dataset_id,
"fraction_split": {
"training_fraction": 0.8,
"validation_fraction": 0.1,
"test_fraction": 0.1,
},
},
"model_to_upload": {"display_name": model_display_name},
}
parent = f"projects/{project}/locations/{location}"
response = client.create_training_pipeline(
parent=parent, training_pipeline=training_pipeline
)
print("response:", response)
