Install Required Packages
- Use Python's package manager, pip, to install the Google Cloud AutoML library and any other required dependencies.
pip install google-cloud-automl
Set Up Authentication
- Authenticate using a service account. Set the `GOOGLE_APPLICATION_CREDENTIALS` environment variable to point to your service account key file.
import os
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "path/to/your/service-account-file.json"
Import Necessary Libraries
- Python code will leverage the Google Cloud client library for accessing AutoML services.
from google.cloud import automl_v1
Initialize the Client
- Initiate the AutoML client, which will allow you to interact with your models and datasets.
client = automl_v1.AutoMlClient()
Select and Display Project Details
- Setup project details including project ID, compute region, and dataset ID.
project_id = "your_project_id"
compute_region = "us-central1"
dataset_id = "your_dataset_id"
Execute a Request
- Fetch the list of tables from the dataset using AutoML Tables Client.
tables_client = automl_v1.TablesClient(project=project_id, region=compute_region)
tables = tables_client.list_tables(dataset=dataset_id)
for table in tables:
print("Table name: {}".format(table.display_name))
Train a Model
- To train a model, define the configuration for the model you want to create.
model_display_name = "your_model_display_name"
model = tables_client.create_model(
model_display_name=model_display_name,
dataset_id=dataset_id,
train_budget_milli_node_hours=1000,
)
print("Model name: {}".format(model.name))
Predict with the Model
- Use the model to make predictions. Ensure you have loaded the model before making predictions.
payload = {"row": {"values": ["value1", "value2", "value3"]}}
response = tables_client.predict(
model=model,
inputs=payload,
)
for prediction in response.payload:
print("Predicted Class: {}".format(prediction.display_name))
Close Resources
- After operations, remember to clean up resources if needed and close any connections.
client.transport._channel.close()
