Try Translation

This quickstart guides the Application Operator (AO) through the process of using the Vertex AI Translation pre-trained API on Google Distributed Cloud (GDC) air-gapped.

Before you begin

Follow these steps before trying Translation:

  1. Set up a project using the GDC console to group the Vertex AI services. For information about creating and using projects, see Create a project.

  2. Ask your Project IAM Admin to grant you the AI Translation Developer (ai-translation-developer) role in your project namespace.

  3. Enable the Translation pre-trained API.

  4. Download the gdcloud command-line interface (CLI).

Set up your service account

Set up your service account with the name of your service account, project ID, and service key. Replace the PROJECT_ID with your project.

  ${HOME}/gdcloud init  # set URI and project

  ${HOME}/gdcloud auth login

  ${HOME}/gdcloud iam service-accounts create SERVICE_ACCOUNT  --project=PROJECT_ID

  ${HOME}/gdcloud iam service-accounts keys create "SERVICE_KEY".json --project=PROJECT_ID --iam-account=SERVICE_ACCOUNT

Grant access to project resources

Grant access to the Translation API service account by providing your project ID, name of your service account, and the role ai-translation-developer.

  ${HOME}/gdcloud iam service-accounts add-iam-policy-binding --project=PROJECT_ID --iam-account=SERVICE_ACCOUNT --role=role/ai-translation-developer

Set your environment variables

Before running the Translation pre-trained service, set your environment variable.

  export GOOGLE_APPLICATION_CREDENTIALS="SERVICE_KEY".json

Authenticate the gdcloud CLI

You must get a token to authenticate the gdcloud CLI before sending requests to the Translation pre-trained services. Follow these steps:

  1. Install the google-auth client library.

    pip install google-auth

  2. Save the following code to a Python script, and update the ENDPOINT to the Translation endpoint. For more information, see View service statuses and endpoints.

    import google.auth
from google.auth.transport import requests

api_endpoint = "https://ENDPOINT"

creds, project_id = google.auth.default()
creds = creds.with_gdch_audience(api_endpoint)

def test_get_token():
  req = requests.Request()
  creds.refresh(req)
  print(creds.token)

if __name__=="__main__":
  test_get_token()

  3. Run the script to fetch the token.

    You must add the fetched token to the header of the grpcurl and curl requests as in the following example:

    -H "Authorization: Bearer TOKEN"

  4. Make the grpcurl or curl request:

    grpcurl

    1. If you don't have grpcurl installed, download and install it from a resource outside of Distributed Cloud (https://github.com/fullstorydev/grpcurl#from-source).

    2. Make the request:

    grpcurl -plaintext -d '{"parent": "projects/PROJECT_ID", "source_language_code": "es", "target_language_code": "en", "contents": ["Hola, esto es una prueba"]}' ENDPOINT google.cloud.translation.v3.TranslationService/TranslateText

    Replace the following:

    • PROJECT_ID: your project ID number.
    • ENDPOINT: the Translation endpoint that you use for your organization.

    curl

    curl -X POST -H "Content-Type: application/json" http://ENDPOINT/v3/projects/PROJECT_ID:translateText -d '{"parent": "projects/PROJECT_ID", "source_language_code": "es", "target_language_code": "en", "contents": ["Hola, esto es una prueba"]}'

    Replace the following:

    • PROJECT_ID: your project ID number.
    • ENDPOINT: the Translation endpoint that you use for your organization.

Run the Translation pre-trained API sample script

This example shows you how to interact with a Translation pre-trained API.

  1. Check whether there is a client library installed.

      pip freeze | grep translation
  # output example: google-cloud-translation==3.8.0

    If the existing version doesn't match the client library in https://CONSOLE_ENDPOINT/.well-known/static/client-libraries, uninstall the client library using the following command.

      pip uninstall google-cloud-translation

  2. Specify the console endpoint and the client library for Translation (provided in the example).

       wget https://CONSOLE_ENDPOINT/.well-known/static/client-libraries/google-cloud-translation

  3. Extract the tar file, and install it using pip. If errors are generated because something isn't found, install any missing dependencies.

    tar -xvzf CLIENT_LIBRARY

pip install -r FOLDER/requirements.txt --no-index --find-links FOLDER

  4. Use the Translation client library script to generate the token, and make requests to the Translation service.

  5. Set up your environment variable:

    os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = ""SERVICE_KEY".json"

Translation sample

Replace the ENDPOINT with the Translation endpoint that you use for your organization.

from google.cloud import translate
import google.auth
from google.auth.transport import requests
from google.api_core.client_options import ClientOptions

audience = "https://ENDPOINT:443"
api_endpoint="ENDPOINT:443"

def translate_client(creds):
  opts = ClientOptions(api_endpoint=api_endpoint)
  return translate.TranslationServiceClient(credentials=creds, client_options=opts)

def main():
  creds = None
  try:
    creds, project_id = google.auth.default()
    creds = creds.with_gdch_audience(audience)
    req = requests.Request()
    creds.refresh(req)
    print("Got token: ")
    print(creds.token)
  except Exception as e:
    print("Caught exception" + str(e))
    raise e
  return creds

def translate_func(creds):
  tc = translate_client(creds)
  req = {"parent": "projects/PROJECT_ID", "source_language_code": "es", "target_language_code": "en", "contents": ["Hola, esto es una prueba"]}

  resp = tc.translate_text(req)
  print(resp)

if __name__=="__main__":
  creds = main()
  translate_func(creds)

For more information on the translate_text method, see the Python client library.

What's next