NOTE:	This github repository is archived. The repository contents and history have moved to google-cloud-python.

Python Client for Compute Engine

Compute Engine API: Create and runs virtual machines on Google Cloud Platform.

• Client Library Documentation
• Product Documentation

Quick Start

In order to use this library, you first need to go through the following steps:

1. Select or create a Cloud Platform project.
2. Enable billing for your project.
3. Enable the Compute Engine API.
4. Setup Authentication.

Installation

Install this library in a virtualenv using pip. virtualenv is a tool to create isolated Python environments. The basic problem it addresses is one of dependencies and versions, and indirectly permissions.

With virtualenv, it's possible to install this library without needing system install permissions, and without clashing with the installed system dependencies.

Mac/Linux

pip install virtualenv
virtualenv <your-env>
source <your-env>/bin/activate
<your-env>/bin/pip install google-cloud-compute

Windows

pip install virtualenv
virtualenv <your-env>
<your-env>\Scripts\activate
<your-env>\Scripts\pip.exe install google-cloud-compute

Authentication and Authorization

This client library supports authentication via Google Application Default Credentials , or by providing a JSON key file for a Service Account. See examples below.

Google Application Default Credentials (ADC) is the recommended way to authorize and authenticate clients. Here is an example of a client using ADC to authenticate:

from google.cloud import compute_v1

networks_client = compute_v1.NetworksClient()
for network in networks_client.list(project='YOUR_PROJECT'):
    print(network)

You can use a file with credentials to authenticate and authorize, such as a JSON key file associated with a Google service account. You can create service account keys and download them using the Google Cloud Console. For more information, see Creating and managing Service Account keys.

The library used to create the credentials objects is google-auth. This example uses the Networks Client, but the same steps apply to the other clients in this package. Example:

from google.oauth2 import service_account
from google.cloud import compute_v1

credentials = service_account.Credentials.from_service_account_file(
    '/path/to/key.json')

networks_client = compute_v1.NetworksClient(credentials=credentials)
for network in networks_client.list(project='YOUR_PROJECT'):
    print(network)

When you don't want to store secrets on disk, you can create credentials from in-memory JSON and use the from_service_account_info method. You can also limit the use of your credentials only to specified scopes. For more information about OAuth 2.0 scopes for Google APIs, see Scopes documentation page. Example:

import json

from google.oauth2 import service_account
from google.cloud import compute_v1

json_acct_info = json.loads(function_to_get_json_creds())
credentials = service_account.Credentials.from_service_account_info(
    json_acct_info)

scoped_credentials = credentials.with_scopes(
    ['https://www.googleapis.com/auth/cloud-platform'])

networks_client = compute_v1.NetworksClient(credentials=scoped_credentials)
for network in networks_client.list(project='YOUR_PROJECT'):
    print(network)

Long Running Operations

Long-Running Operations (LROs), like the many insert() operations, can be handled using the ExtendedOperation object that is returned when the LRO is started.

You can wait for the completion of an operation using its result() method. This method accepts a timeout argument, specifying how long you want your process to wait for completion of the operation (in seconds). When the call to result() times out, the operation is not automatically cancelled. At any time, you can check whether the operation is complete by using its done() method.

A sample method to handle LROs featuring error and warning reporting can be found in the Python code samples repository: GoogleCloudPlatform/python-docs-samples

Code Samples

You can find useful code samples that will demonstrate the usage of this library on the Google Cloud samples page.

PyCharm/JetBrains IDEs

This library has now grown in size past the JetBrains default size limit of ~2.5Mb. As a result, code completion in JetBrains products can fail to work with the classes from our library. To fix this, you need to update the idea.max.intellisense.filesize setting in custom properties (Help -> Edit custom properties...). Just add the line idea.max.intellisense.filesize = 10000 to change this limit to ~10Mb.

Next Steps

• Read the Client Library Documentation for Compute Engine API to see other available methods on the client.
• Read the Compute Engine API Product documentation to learn more about the product and see How-to Guides.
• View this README to see the full list of Cloud APIs that we cover.