As of Fabric 1.1, there are two distinct methods you may use in order to define which objects in your fabfile show up as tasks:
- The “new” method starting in 1.1 considers instances of
Taskor its subclasses, and also descends into imported modules to allow building nested namespaces.
- The “classic” method from 1.0 and earlier considers all public callable objects (functions, classes etc) and only considers the objects in the fabfile itself with no recursing into imported module.
These two methods are mutually exclusive: if Fabric finds any
new-style task objects in your fabfile or in modules it imports, it will
assume you’ve committed to this method of task declaration and won’t
consider any non-
Task callables. If no new-style tasks
are found, it reverts to the classic behavior.
The rest of this document explores these two methods in detail.
To see exactly what tasks in your fabfile may be executed via
Fabric 1.1 introduced the
Task class to facilitate new features
and enable some programming best practices, specifically:
- Object-oriented tasks. Inheritance and all that comes with it can make for much more sensible code reuse than passing around simple function objects. The classic style of task declaration didn’t entirely rule this out, but it also didn’t make it terribly easy.
- Namespaces. Having an explicit method of declaring tasks makes it easier
to set up recursive namespaces without e.g. polluting your task list with the
contents of Python’s
osmodule (which would show up as valid “tasks” under the classic methodology.)
With the introduction of
Task, there are two ways to set up new
- Decorate a regular module level function with
@task, which transparently wraps the function in a
Tasksubclass. The function name will be used as the task name when invoking.
Taskitself is intended to be abstract), define a
runmethod, and instantiate your subclass at module level. Instances’
nameattributes are used as the task name; if omitted the instance’s variable name will be used instead.
Use of new-style tasks also allows you to set up namespaces.
The quickest way to make use of new-style task features is to wrap basic task functions with
from fabric.api import task, run @task def mytask(): run("a command")
When this decorator is used, it signals to Fabric that only functions wrapped in the decorator are to be loaded up as valid tasks. (When not present, classic-style task behavior kicks in.)
@task may also be called with arguments to
customize its behavior. Any arguments not documented below are passed into the
constructor of the
task_class being used, with the function itself as the
first argument (see Using custom subclasses with @task for details.)
Tasksubclass used to wrap the decorated function. Defaults to
aliases: An iterable of string names which will be used as aliases for the wrapped function. See Aliases for details.
aliasesbut taking a single string argument instead of an iterable. If both
aliaseswill take precedence.
default: A boolean value determining whether the decorated task also stands in for its containing module as a task name. See Default tasks.
name: A string setting the name this task appears as to the command-line interface. Useful for task names that would otherwise shadow Python builtins (which is technically legal but frowned upon and bug-prone.)
Here’s a quick example of using the
alias keyword argument to facilitate
use of both a longer human-readable task name, and a shorter name which is
quicker to type:
from fabric.api import task @task(alias='dwm') def deploy_with_migrations(): pass
--list on this fabfile would show both the original
deploy_with_migrations and its alias
$ fab --list Available commands: deploy_with_migrations dwm
When more than one alias for the same function is needed, simply swap in the
aliases kwarg, which takes an iterable of strings instead of a single
In a similar manner to aliases, it’s sometimes useful to designate a given task within a module as the “default” task, which may be called by referencing just the module name. This can save typing and/or allow for neater organization when there’s a single “main” task and a number of related tasks or subroutines.
For example, a
deploy submodule might contain tasks for provisioning new
servers, pushing code, migrating databases, and so forth – but it’d be very
convenient to highlight a task as the default “just deploy” action. Such a
deploy.py module might look like this:
from fabric.api import task @task def migrate(): pass @task def push(): pass @task def provision(): pass @task def full_deploy(): if not provisioned: provision() push() migrate()
With the following task list (assuming a simple top level
fabfile.py that just imports
$ fab --list Available commands: deploy.full_deploy deploy.migrate deploy.provision deploy.push
deploy.full_deploy on every deploy could get kind of old, or somebody new to the team might not be sure if that’s really the right task to run.
default kwarg to
@task, we can tag
full_deploy as the default task:
@task(default=True) def full_deploy(): pass
Doing so updates the task list like so:
$ fab --list Available commands: deploy deploy.full_deploy deploy.migrate deploy.provision deploy.push
full_deploy still exists as its own explicit task – but now
deploy shows up as a sort of top level alias for
If multiple tasks within a module have
default=True set, the last one to
be loaded (typically the one lowest down in the file) will take precedence.
Top-level default tasks¶
@task(default=True) in the top level fabfile will cause the denoted
task to execute when a user invokes
fab without any task names (similar to
make.) When using this shortcut, it is not possible to specify
arguments to the task itself – use a regular invocation of the task if this
If you’re used to classic-style tasks, an easy way to
Task subclasses is that their
run method is
directly equivalent to a classic task; its arguments are the task arguments
self) and its body is what gets executed.
For example, this new-style task:
class MyTask(Task): name = "deploy" def run(self, environment, domain="whatever.com"): run("git clone foo") sudo("service apache2 restart") instance = MyTask()
is exactly equivalent to this function-based task:
@task def deploy(environment, domain="whatever.com"): run("git clone foo") sudo("service apache2 restart")
Note how we had to instantiate an instance of our class; that’s simply normal
Python object-oriented programming at work. While it’s a small bit of
boilerplate right now – for example, Fabric doesn’t care about the name you
give the instantiation, only the instance’s
name attribute – it’s well
worth the benefit of having the power of classes available.
We plan to extend the API in the future to make this experience a bit smoother.
Using custom subclasses with
It’s possible to marry custom
Task subclasses with
@task. This may be useful in cases where your core
execution logic doesn’t do anything class/object-specific, but you want to
take advantage of class metaprogramming or similar techniques.
Task subclass which is designed to take in a
callable as its first constructor argument (as the built-in
WrappedCallableTask does) may be specified as the
task_class argument to
Fabric will automatically instantiate a copy of the given class, passing in the wrapped function as the first argument. All other args/kwargs given to the decorator (besides the “special” arguments documented in Arguments) are added afterwards.
Here’s a brief and somewhat contrived example to make this obvious:
from fabric.api import task from fabric.tasks import Task class CustomTask(Task): def __init__(self, func, myarg, *args, **kwargs): super(CustomTask, self).__init__(*args, **kwargs) self.func = func self.myarg = myarg def run(self, *args, **kwargs): return self.func(*args, **kwargs) @task(task_class=CustomTask, myarg='value', alias='at') def actual_task(): pass
When this fabfile is loaded, a copy of
CustomTask is instantiated, effectively calling:
task_obj = CustomTask(actual_task, myarg='value')
Note how the
alias kwarg is stripped out by the decorator itself and never
reaches the class instantiation; this is identical in function to how
command-line task arguments work.
With classic tasks, fabfiles were limited to a single,
flat set of task names with no real way to organize them. In Fabric 1.1 and
newer, if you declare tasks the new way (via
or your own
Task subclass instances) you may take advantage
- Any module objects imported into your fabfile will be recursed into, looking for additional task objects.
- Within submodules, you may control which objects are “exported” by using the
__all__module-level variable name (thought they should still be valid new-style task objects.)
- These tasks will be given new dotted-notation names based on the modules they came from, similar to Python’s own import syntax.
Let’s build up a fabfile package from simple to complex and see how this works.
We start with a single
__init__.py containing a few tasks (the Fabric API
import omitted for brevity):
@task def deploy(): ... @task def compress(): ...
The output of
fab --list would look something like this:
There’s just one namespace here: the “root” or global namespace. Looks simple now, but in a real-world fabfile with dozens of tasks, it can get difficult to manage.
Importing a submodule¶
As mentioned above, Fabric will examine any imported module objects for tasks,
regardless of where that module exists on your Python import path. For now we
just want to include our own, “nearby” tasks, so we’ll make a new submodule in
our package for dealing with, say, load balancers –
@task def add_backend(): ...
And we’ll add this to the top of
fab --list shows us:
deploy compress lb.add_backend
Again, with only one task in its own submodule, it looks kind of silly, but the benefits should be pretty obvious.
Namespacing isn’t limited to just one level. Let’s say we had a larger setup
and wanted a namespace for database related tasks, with additional
differentiation inside that. We make a sub-package named
db/ and inside it,
@task def list(): ... @task def run(): ...
We need to make sure that this module is visible to anybody importing
so we add it to the sub-package’s
As a final step, we import the sub-package into our root-level
so now its first few lines look like this:
import lb import db
After all that, our file tree looks like this:
. ├── __init__.py ├── db │ ├── __init__.py │ └── migrations.py └── lb.py
fab --list shows:
deploy compress lb.add_backend db.migrations.list db.migrations.run
We could also have specified (or imported) tasks directly into
db/__init__.py, and they would show up as
db.<whatever> as you might
You may limit what Fabric “sees” when it examines imported modules, by using
the Python convention of a module level
__all__ variable (a list of
variable names.) If we didn’t want the
db.migrations.run task to show up by
default for some reason, we could add this to the top of
__all__ = ['list']
Note the lack of
'run' there. You could, if needed, import
into some other part of the hierarchy, but otherwise it’ll remain hidden.
Switching it up¶
We’ve been keeping our fabfile package neatly organized and importing it in a straightforward manner, but the filesystem layout doesn’t actually matter here. All Fabric’s loader cares about is the names the modules are given when they’re imported.
For example, if we changed the top of our root
__init__.py to look like
import db as database
Our task list would change thusly:
deploy compress lb.add_backend database.migrations.list database.migrations.run
This applies to any other import – you could import third party modules into your own task hierarchy, or grab a deeply nested module and make it appear near the top level.
Nested list output¶
As a final note, we’ve been using the default Fabric
output during this section – it makes it more obvious what the actual task
names are. However, you can get a more nested or tree-like view by passing
nested to the
$ fab --list-format=nested --list Available commands (remember to call as module.[...].task): deploy compress lb: add_backend database: migrations: list run
While it slightly obfuscates the “real” task names, this view provides a handy way of noting the organization of tasks in large namespaces.
When no new-style
Task-based tasks are found, Fabric will
consider any callable object found in your fabfile, except the following:
- Callables whose name starts with an underscore (
_). In other words, Python’s usual “private” convention holds true here.
- Callables defined within Fabric itself. Fabric’s own functions such as
sudowill not show up in your task list.
import statement effectively includes the imported objects in your
module’s namespace. Since Fabric’s fabfiles are just Python modules, this means
that imports are also considered as possible classic-style tasks, alongside
anything defined in the fabfile itself.
This only applies to imported callable objects – not modules. Imported modules only come into play if they contain new-style tasks, at which point this section no longer applies.
Because of this, we strongly recommend that you use the
import module form
of importing, followed by
module.callable(), which will result in a cleaner
fabfile API than doing
from module import callable.
For example, here’s a sample fabfile which uses
urllib.urlopen to get some
data out of a webservice:
from urllib import urlopen from fabric.api import run def webservice_read(): objects = urlopen('http://my/web/service/?foo=bar').read().split() print(objects)
This looks simple enough, and will run without error. However, look what
happens if we run
fab --list on this fabfile:
$ fab --list Available commands: webservice_read List some directories. urlopen urlopen(url [, data]) -> open file-like object
Our fabfile of only one task is showing two “tasks”, which is bad enough, and
an unsuspecting user might accidentally try to call
fab urlopen, which
probably won’t work very well. Imagine any real-world fabfile, which is likely
to be much more complex, and hopefully you can see how this could get messy
For reference, here’s the recommended way to do it:
import urllib from fabric.api import run def webservice_read(): objects = urllib.urlopen('http://my/web/service/?foo=bar').read().split() print(objects)
It’s a simple change, but it’ll make anyone using your fabfile a bit happier.