Install Vertex AI client libraries

This page is for application developers within application operator groups responsible for setting up their application and development environments to enable AI features. For more information, see Audiences for GDC air-gapped documentation.

Each Vertex AI service provides an API. While you can interact directly with these APIs through raw server requests, client libraries simplify programmatic access from supported languages on Distributed Cloud. They reduce the necessary code required, especially when working in environments like a JupyterLab notebook.

You can install a Vertex AI client library using these methods:

• Extract the library file directly from the tar file.
• Use a JupyterLab notebook to import the library.
• Import a client library from a notebook. For information, see Manage notebooks.

Vertex AI client libraries

Vertex AI offers different versions of client libraries for CentOS and Ubuntu operating systems.

The naming conventions of Vertex AI client libraries in the tar file are based on the operating system, the service name, and the version. The filenames adhere to the following format:

OS-google-cloud-SERVICE-VERSION.tar.gz

Replace the following:

• OS: the name of the operating system where you want to install the client library. Allowed values are centos and ubuntu.

• SERVICE: the name of the Vertex AI service from which you want to download the client library. The following are the allowed values:

  • aiplatform: the Vertex AI Platform client library.
  • speech: the Speech-to-Text client library.
  • translate: the Vertex AI Translation client library.
  • vision: the OCR client library.

• VERSION: the version number of the client library, such as 3.8.0.

The following table contains the Vertex AI client libraries that Distributed Cloud supports:

Before you begin

Before downloading the tar file and extracting client libraries, follow these steps:

1. Set up a project for Vertex AI.

2. Authenticate with gdcloud CLI:

   gdcloud auth login
   

   For more information about how to authenticate with your configured identity provider, see the gdcloud CLI authentication.

3. Verify that you have installed Python 3.7 to 3.10. The client library tarballs contain wheels for cp37 to cp310.

Install a client library

After completing the prerequisites, follow these steps to download the tar file, and use the tar file to install a client library:

1. Download the client library you want to install:

   wget https://GDC_URL/.well-known/static/client-libraries/CLIENT_LIBRARY
   

   Replace the following:

   • GDC_URL: the URL of GDC Console for your organization.
   • CLIENT_LIBRARY: the filename of the client library you want to download.

2. Extract the library file:

   tar -zxf CLIENT_LIBRARY
   

   Extracting OS-google-cloud-SERVICE-VERSION.tar.gz (for example, centos-google-cloud-speech-2.15.0.tar.gz) creates the LIBRARY_NAME directory (for example, google-cloud-speech, without the OS- prefix or -VERSION suffix).

3. Install the client library in Distributed Cloud:

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

   Replace LIBRARY_NAME with the extracted library directory name, such as google-cloud-speech.

4. If you installed the Speech-to-Text client library, verify the installation and version (2.15.0):

   from google.cloud import speech_v1p1beta1 as speech
   
   print(speech.__version__)
   

   The output displays the installed version:

   2.15.0
   

5. Import the client library using a Python script. The following example shows a code snippet of a Python script that imports the Vertex AI Translation client library to illustrate what importing libraries looks like:

   from google.cloud import translate
   from google.api_core.client_options import ClientOptions
   import google.auth
   from google.auth.transport import requests
   
   creds, _ = google.auth.default()
   creds = creds.with_gdch_audience("https://ENDPOINT:443")
   creds.refresh(requests.Request())
   client = translate.TranslationServiceClient(
       credentials=creds, client_options=ClientOptions(api_endpoint="ENDPOINT:443"))
   response = client.translate_text(request={
       "parent": "projects/PROJECT_ID", "target_language_code": "ru",
       "mime_type": "text/plain", "contents": ["Hello"]})
   print(response.translations[0].translated_text)
   

   Replace the following:

   • ENDPOINT: the Vertex AI Translation endpoint that you use for your organization, without the https:// prefix or :443 port. For more information, view service status and endpoints.
   • PROJECT_ID: your project ID.

   For more information about making Vertex AI Translation requests, learn about translation features or translate text.

6. Save the Python script with a name, such as translation-service.py.

7. Run the Python script:

   python SCRIPT_NAME
   

   Replace SCRIPT_NAME with the name you gave to your Python script, such as translation-service.py.