Defining tasks¶

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 Task or 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.

Note

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.

Note

To see exactly what tasks in your fabfile may be executed via fab, use fab --list.

New-style tasks¶

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 os module (which would show up as valid “tasks” under the classic methodology.)

With the introduction of Task, there are two ways to set up new tasks:

Decorate a regular module level function with @task, which transparently wraps the function in a Task subclass. The function name will be used as the task name when invoking.
Subclass Task (Task itself is intended to be abstract), define a run method, and instantiate your subclass at module level. Instances’ name attributes 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 `@task` decorator¶

The quickest way to make use of new-style task features is to wrap basic task functions with @task:

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.)

Arguments¶

@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.)

task_class: The Task subclass used to wrap the decorated function. Defaults to WrappedCallableTask.
aliases: An iterable of string names which will be used as aliases for the wrapped function. See Aliases for details.
alias: Like aliases but taking a single string argument instead of an iterable. If both alias and aliases are specified, aliases will 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.)

Aliases¶

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

Calling --list on this fabfile would show both the original deploy_with_migrations and its alias dwm:

$ 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 string.

Default tasks¶

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 deploy):

$ fab --list
Available commands:

    deploy.full_deploy
    deploy.migrate
    deploy.provision
    deploy.push

Calling 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.

Using the default kwarg to @task, we can tag e.g. 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

Note that full_deploy still exists as its own explicit task – but now deploy shows up as a sort of top level alias for full_deploy.

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¶

Using @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 e.g. 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 is necessary.

`Task` subclasses¶

If you’re used to classic-style tasks, an easy way to think about Task subclasses is that their run method is directly equivalent to a classic task; its arguments are the task arguments (other than 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 `@task`¶

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.

Specifically, any 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 @task.

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.

Namespaces¶

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 @task or your own Task subclass instances) you may take advantage of namespacing:

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 standard Python __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.

Basic¶

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:

deploy
compress

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 – lb.py:

@task
def add_backend():
    ...

And we’ll add this to the top of __init__.py:

import lb

Now 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.

Going deeper¶

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, a migrations.py module:

@task
def list():
    ...

@task
def run():
    ...

We need to make sure that this module is visible to anybody importing db, so we add it to the sub-package’s __init__.py:

import migrations

As a final step, we import the sub-package into our root-level __init__.py, 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

and 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 expect.

Limiting with `all`¶

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 db/migrations.py:

__all__ = ['list']

Note the lack of 'run' there. You could, if needed, import run directly 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 this:

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 --list 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 --list-format option:

$ 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.

Classic tasks¶

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 run and sudo will not show up in your task list.

Imports¶

Python’s 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.

Note

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 fast.

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.

Defining tasks¶

New-style tasks¶

The `@task` decorator¶

Arguments¶

Aliases¶

Default tasks¶

Top-level default tasks¶

`Task` subclasses¶

Using custom subclasses with `@task`¶

Namespaces¶

Basic¶

Importing a submodule¶

Going deeper¶

Limiting with `all`¶

Switching it up¶

Nested list output¶

Classic tasks¶

Imports¶

fab-classic

Navigation

Defining tasks¶

New-style tasks¶

The @task decorator¶

Arguments¶

Aliases¶

Default tasks¶

Top-level default tasks¶

Task subclasses¶

Using custom subclasses with @task¶

Namespaces¶

Basic¶

Importing a submodule¶

Going deeper¶

Limiting with __all__¶

Switching it up¶

Nested list output¶

Classic tasks¶

Imports¶

The `@task` decorator¶

`Task` subclasses¶

Using custom subclasses with `@task`¶

Limiting with `all`¶