This page provides an overview of using the APIs installed with Vertex AI on Google Distributed Cloud (GDC) air-gapped and its reference documentation.
Service endpoint and discovery document
A service endpoint is required to interact programmatically with the Vertex AI APIs.
Get the Vertex AI Workbench endpoint and discovery document
The API endpoint for the Vertex AI Workbench KRM API is:
https://ENDPOINT/apis/aiplatform.gdc.goog/v1
Replace ENDPOINT with the API endpoint of the
Management API server.
To access the Vertex AI Workbench discovery document, perform one of the following actions:
Enter the endpoint URL in a browser.
Run the
kubectl proxycommand in a tool such as curl to open a proxy to the Management API server on your local machine. After that command is running, enter the following URL in your browser:http://127.0.0.1:8001/apis/aiplatform.gdc.goog/v1.
Get the pre-trained APIs endpoints
To get the endpoints for the pre-trained APIs, view service status and endpoints.
REST, gRPC, KRM, and client libraries
You can access the pre-trained APIs using gRPC or one of the provided client libraries. The client libraries are built on gRPC.
Alternatively, you access some pre-trained APIs using REST.
You can manage Vertex AI Workbench using its Kubernetes Resource Model (KRM) API.
Client libraries
Vertex AI provides Python client libraries for pre-trained APIs. The following table shows a comparison of advantages and disadvantages of using client libraries:
| Advantages | Disadvantages |
|---|---|
|
Maintained by Google.
Built-in authentication. Built-in retries. Efficient protocol buffer HTTP request body. |
Not available for all programming languages. |
REST
The OCR and Translation APIs support REST. For more information, see the REST API references for these services:
The following table shows a comparison of advantages and disadvantages of using REST:
| Advantages | Disadvantages |
|---|---|
|
Simple JSON interface.
Well supported by many Google and third-party tools and libraries. |
You must build your own client.
You must implement authentication. You must implement retries. Less efficient JSON HTTP request body. REST streaming is not supported by these APIs. |
gRPC
Pre-trained APIs support gRPC. For more information about the generic descriptions of the types, methods, and fields generated for a gRPC library, see the following gRPC reference:
The following table shows a comparison of advantages and disadvantages of using gRPC:
| Advantages | Disadvantage |
|---|---|
|
Supports many programming languages.
Efficient protocol buffer HTTP request body. |
You must generate your own client from Google-supplied protocol buffers.
You must implement authentication. You must implement retries. |
KRM
The Vertex AI Workbench API supports KRM. For more information, see the Vertex AI Workbench KRM API reference.
Type, method, and field names
Depending on whether you are using client libraries, REST, gRPC, or KRM, the type, method, and field names for the API vary in the following ways:
- REST is arranged by resource hierarchies and their methods.
- Client libraries and gRPC are arranged by services and their methods.
- KRM field names use camelCase, but the API service accepts either camelCase or snake_case.
- REST and gRPC field names use snake_case.
- Client library field names use either title case, camelCase, or snake_case, depending on which name is idiomatic for the language.
REST and protocol buffers
When calling the REST API, the default value behavior for protocol buffers might result in missing fields in a JSON response. These fields are set to the default value, so they are not included in the response.