Coba Gemini 1.5 Pro, model multimodal kami yang paling canggih di Vertex AI, dan lihat apa yang dapat Anda bangun dengan jendela konteks token 1 juta.
Coba Gemini 1.5 Pro, model multimodal kami yang paling canggih di Vertex AI, dan lihat apa yang dapat Anda bangun dengan jendela konteks token 1 juta.
Membuat pipeline pelatihan untuk perkiraan tabular
Tetap teratur dengan koleksi
Simpan dan kategorikan konten berdasarkan preferensi Anda.
Membuat pipeline pelatihan untuk perkiraan tabel menggunakan metode create_training_pipeline.
Contoh kode
Sebelum mencoba contoh ini, ikuti petunjuk penyiapan Python di
Panduan memulai Vertex AI menggunakan
library klien.
Untuk mengetahui informasi selengkapnya, lihat
Dokumentasi referensi API Python Vertex AI.
Untuk melakukan autentikasi ke Vertex AI, siapkan Kredensial Default Aplikasi.
Untuk mengetahui informasi selengkapnya, baca
Menyiapkan autentikasi untuk lingkungan pengembangan lokal.
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)
Kecuali dinyatakan lain, konten di halaman ini dilisensikan berdasarkan Lisensi Creative Commons Attribution 4.0, sedangkan contoh kode dilisensikan berdasarkan Lisensi Apache 2.0. Untuk mengetahui informasi selengkapnya, lihat Kebijakan Situs Google Developers. Java adalah merek dagang terdaftar dari Oracle dan/atau afiliasinya.
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