This page describes Libcloud development process and contains general guidelines and information on how to contribute to the project.


We welcome contributions of any kind (ideas, code, tests, documentation, examples, …).

If you need help or get stuck at any point during this process, stop by on our IRC channel (#libcloud on freenode) and we will do our best to assist you.

Getting started with contributing to Libcloud

General contribution guidelines

  • Any non-trivial change must contain tests. For more information, refer to the Testing page.
  • All the functions and methods must contain Sphinx docstrings which are used to generate the API documentation. For more information, refer to the Docstring conventions section below.
  • If you are adding a new feature, make sure to add a corresponding documentation.

Code style guide

  • We follow PEP8 Python Style Guide
  • Use 4 spaces for a tab
  • Use 79 characters in a line
  • Make sure edited file doesn’t contain any trailing whitespace
  • You can verify that your modifications don’t break any rules by running the flake8 script - e.g. flake8 libcloud/ or tox -e lint. Second command will run flake8 on all the files in the repository.

And most importantly, follow the existing style in the file you are editing and be consistent.

Git pre-commit hook

To make complying with our style guide easier, we provide a git pre-commit hook which automatically checks modified Python files for violations of our style guide.

You can install it by running following command in the root of the repository checkout:

ln -s contrib/ .git/hooks/pre-commit

After you have installed this hook it will automatically check modified Python files for violations before a commit. If a violation is found, commit will be aborted.

Code conventions

This section describes some general code conventions you should follow when writing a Libcloud code.

1. Import ordering

Organize the imports in the following order:

  1. Standard library imports
  2. Third-party library imports
  3. Local library (Libcloud) imports

Each section should be separated with a blank line. For example:

import sys
import base64

import paramiko

from libcloud.compute.base import Node, NodeDriver
from libcloud.compute.providers import Provider

2. Function and method ordering

Functions in a module and methods on a class should be organized in the following order:

  1. “Public” functions / methods
  2. “Private” functions / methods (methods prefixed with an underscore)
  3. “Internal” methods (methods prefixed and suffixed with a double underscore)

For example:

class Unicorn(object):
    def __init__(self, name='fluffy'):
        self._name = name

    def make_a_rainbow(self):

    def _get_rainbow_colors(self):

    def __eq__(self, other):
        return ==

Methods on a driver class should be organized in the following order:

  1. Methods which are part of the standard API
  2. Extension methods
  3. “Private” methods (methods prefixed with an underscore)
  4. “Internal” methods (methods prefixed and suffixed with a double underscore)

Methods which perform a similar functionality should be grouped together and defined one after another.

For example:

class MyDriver(object):
    def __init__(self):

    def list_nodes(self):

    def list_images(self):

    def create_node(self):

    def reboot_node(self):

    def ex_create_image(self):

    def _to_nodes(self):

    def _to_node(self):

    def _to_images(self):

    def _to_image(self):

Methods should be ordered this way for the consistency reasons and to make reading and following the generated API documentation easier.

3. Prefer keyword over regular arguments

For better readability and understanding of the code, prefer keyword over regular arguments.


some_method(public_ips=public_ips, private_ips=private_ips)


some_method(public_ips, private_ips)

4. Don’t abuse **kwargs

You should always explicitly declare arguments in a function or a method signature and only use **kwargs and *args respectively when there is a valid use case for it.

Using **kwargs in many contexts is against Python’s “explicit is better than implicit” mantra and makes it for a bad and a confusing API. On top of that, it makes many useful things such as programmatic API introspection hard or impossible.

A use case when it might be valid to use **kwargs is a decorator.


def my_method(self, name, description=None, public_ips=None):

Bad (please avoid):

def my_method(self, name, **kwargs):
    description = kwargs.get('description', None)
    public_ips = kwargs.get('public_ips', None)

5. When returning a dictionary, document its structure

Dynamic nature of Python can be very nice and useful, but if (ab)use it in a wrong way it can also make it hard for the API consumer to understand what is going on and what kind of values are being returned.

If you have a function or a method which returns a dictionary, make sure to explicitly document in the docstring which keys the returned dictionary contains.

6. Prefer to use “is not None” when checking if a variable is provided or defined

When checking if a variable is provided or defined, prefer to use if foo is not None instead of if foo.

If you use if foo approach, it’s easy to make a mistake when a valid value can also be falsy (e.g. a number 0).

For example:

class SomeClass(object):
    def some_method(self, domain=None):
        params = {}

        if domain is not None:
            params['Domain'] = domain

Docstring conventions

For documenting the API we we use Sphinx and reStructuredText syntax. Docstring conventions to which you should adhere to are described below.

  • Docstrings should always be used to describe the purpose of methods, functions, classes, and modules.
  • Method docstring should describe all the normal and keyword arguments. You should describe all the available arguments even if you use *args and **kwargs.
  • All parameters must be documented using :param p: or :keyword p: and :type p: annotation.
  • :param p: ... - A description of the parameter p for a function or method.
  • :keyword p: ... - A description of the keyword parameter p.
  • :type p: ... The expected type of the parameter p.
  • Return values must be documented using :return: and :rtype annotation.
  • :return: ... A description of return value for a function or method.
  • :rtype: ... The type of the return value for a function or method.
  • Required keyword arguments must contain (required) notation in description. For example: :keyword image:  OS Image to boot on node. (required)
  • Multiple types are separated with or For example: :type auth: :class:`.NodeAuthSSHKey` or :class:`.NodeAuthPassword`
  • For a description of the container types use the following notation: <container_type> of <objects_type>. For example: :rtype: `list` of :class:`Node`

For more information and examples, please refer to the following links:

Contribution workflow

1. Start a discussion on the mailing list

If you are implementing a big feature or a change, start a discussion on the mailing list first.

2. Open a new issue on our issue tracker

Go to our issue tracker and open a new issue for your changes there. This issue will be used as an umbrella place for your changes. As such, it will be used to track progress and discuss implementation details.

3. Fork our Github repository

Fork our Github git repository. Your fork will be used to hold your changes.

4. Create a new branch for your changes

For example:

git checkout -b <jira_issue_id>_<change_name>

5. Make your changes

6. Write tests for your changes and make sure all the tests pass

Make sure that all the code you have added or modified has appropriate test coverage. Also make sure all the tests including the existing ones still pass.

Use libcloud.test.unittest as the unit testing package to ensure that your tests work with older versions of Python.

For more information on how to write and run tests, please see Testing page.

7. Commit your changes

Make a single commit for your changes. If a corresponding JIRA ticket exists, make sure the commit message contains the ticket number.

For example:

git commit -a -m "[LIBCLOUD-123] Add a new compute driver for CloudStack based providers."

8. Open a pull request with your changes

Go to and open a new pull request with your changes. Your pull request will appear at

Make sure the pull request name is prefixed with a JIRA ticket number, e.g. [LIBCLOUD-436] Improvements to DigitalOcean compute driver and that the pull request description contains link to the JIRA ticket.

9. Wait for the review

Wait for your changes to be reviewed and address any outstanding comments.

10. Squash the commits and generate the patch

Once the changes has been reviewed, all the outstanding issues have been addressed and the pull request has been +1’ed, close the pull request, squash the commits (if necessary) and generate a patch.

git format-patch --stdout trunk > patch_name.patch

Make sure to use git format-patch and not git diff so we can preserve the commit authorship.

Note #1: Before you generate the patch and squash the commits, make sure to synchronize your branch with the latest trunk (run git pull upstream trunk in your branch), otherwise we might have problems applying it cleanly.

Note #2: If you have never used rebase and squashed the commits before, you can find instructions on how to do that in the following guide: squashing commits with rebase.

11. Attach a final patch with your changes to the corresponding JIRA ticket

Attach the generated patch to the JIRA issue you have created earlier.

Note about Github

Github repository is a read-only mirror of the official Apache git repository ( This mirror script runs only a couple of times per day which means this mirror can be slightly out of date.

You are advised to add a separate remote for the official upstream repository:

git remote add upstream

Github read-only mirror is used only for pull requests and code review. Once a pull request has been reviewed, all the comments have been addresses and it’s ready to be merged, user who submitted the pull request must close the pull request, create a patch and attach it to the original JIRA ticket.

Syncing your git(hub) repository with an official upstream git repository

This section describes how to synchronize your git clone / Github fork with an official upstream repository.

It’s important that your repository is in-sync with the upstream one when you start working on a new branch and before you generate a final patch. If the repository is not in-sync, generated patch will be out of sync and we won’t be able to cleanly merge it into trunk.

To synchronize it, follow the steps below in your git clone:

  1. Add upstream remote if you haven’t added it yet
git remote add upstream
  1. Synchronize your trunk branch with an upstream one
git checkout trunk
git pull upstream trunk
  1. Create a branch for your changes and start working on it
git checkout -b my_new_branch
  1. Before generating a final patch which is to be attached to the JIRA ticket, make sure your repository and branch is still in-sync
git pull upstream trunk
  1. Generate a patch which can be attached to the JIRA ticket
git format-patch --stdout remotes/upstream/trunk > patch_name.patch

Contributing Bigger Changes

If you are contributing a bigger change (e.g. large new feature or a new provider driver) you need to have signed Apache Individual Contributor License Agreement (ICLA) in order to have your patch accepted.

You can find more information on how to sign and file an ICLA on the Apache website.

When filling the form, leave field preferred Apache id(s) empty and in the notify project field, enter Libcloud.

Supporting Multiple Python Versions

Libcloud supports a variety of Python versions so your code also needs to work with all the supported versions. This means that in some cases you will need to include extra code to make sure it works in all the supported versions.

Some examples which show how to handle those cases are described below.

Context Managers

Context managers aren’t available in Python 2.5 by default. If you want to use them make sure to put from __future__ import with_statement on top of the file where you use them.

Exception Handling

There is no unified way to handle exceptions and extract the exception object in Python 2.5 and Python 3.x. This means you need to use a sys.exc_info()[1] approach to extract the raised exception object.

For example:

    some code
except Exception:
    e = sys.exc_info()[1]
    print e

Utility functions for cross-version compatibility

You can find a lot of utility functions which make code easier to work with Python 2.x and 3.x in libcloud.utils.py3 module.

You can find some more information on changes which are involved in making the code work with multiple versions on the following link - Lessons learned while porting Libcloud to Python 3