Getting started

This document presents a whirlwind tour of Invoke’s feature set. Please see the links throughout for detailed conceptual & API docs. For installation help, see the project’s installation page.

Defining and running task functions

The core use case for Invoke is setting up a collection of task functions and executing them. This is pretty easy – all you need is to make a file called tasks.py importing the task decorator and decorating one or more functions. You will also need to add an arbitrarily-named context argument (convention is to use c, ctx or context) as the first positional arg. Don’t worry about using this context parameter yet.

Let’s start with a dummy Sphinx docs building task:

from invoke import task

@task
def build(c):
    print("Building!")

You can then execute that new task by telling Invoke’s command line runner, invoke, that you want it to run:

$ invoke build
Building!

The function body can be any Python you want – anything at all.

Task parameters

Functions can have arguments, and thus so can tasks. By default, your task functions’ args/kwargs are mapped automatically to both long and short CLI flags, as per the CLI docs. For example, if we add a clean argument and give it a boolean default, it will show up as a set of toggle flags, --clean and -c:

@task
def build(c, clean=False):
    if clean:
        print("Cleaning!")
    print("Building!")

Invocations:

$ invoke build -c
$ invoke build --clean

Naturally, other default argument values will allow giving string or integer values. Arguments with no default values are assumed to take strings, and can also be given as positional arguments. Take this incredibly contrived snippet for example:

@task
def hi(c, name):
    print("Hi {}!".format(name))

It can be invoked in the following ways, all resulting in “Hi Name!”:

$ invoke hi Name
$ invoke hi --name Name
$ invoke hi --name=Name
$ invoke hi -n Name
$ invoke hi -nName

Adding metadata via @task

@task can be used without any arguments, as above, but it’s also a convenient vector for additional metadata about the task function it decorates. One common example is describing the task’s arguments, via the help parameter (in addition to optionally giving task-level help via the docstring):

@task(help={'name': "Name of the person to say hi to."})
def hi(c, name):
    """
    Say hi to someone.
    """
    print("Hi {}!".format(name))

This description will show up when invoking --help:

$ invoke --help hi
Usage: inv[oke] [--core-opts] hi [--options] [other tasks here ...]

Docstring:
  Say hi to someone.

Options:
  -n STRING, --name=STRING   Name of the person to say hi to.

More details on task parameterization and metadata can be found in Invoking tasks (for the command-line & parsing side of things) and in the task API documentation (for the declaration side).

Listing tasks

You’ll sometimes want to see what tasks are available in a given tasks.py – invoke can be told to list them instead of executing something:

$ invoke --list
Available tasks:

    build

This will also print the first line of each task’s docstring, if it has one. To see what else is available besides --list, say invoke --help.

Running shell commands

Many use cases for Invoke involve running local shell commands, similar to programs like Make or Rake. This is done via the run function:

from invoke import task

@task
def build(c):
    c.run("sphinx-build docs docs/_build")

You’ll see the command’s output in your terminal as it runs:

$ invoke build
Running Sphinx v1.1.3
loading pickled environment... done
...
build succeeded, 2 warnings.

run has a number of arguments controlling its behavior, such as activation of pseudo-terminals for complex programs requiring them, suppression of exit-on-error behavior, hiding of subprocess’ output (while still capturing it for later review), and more. See its API docs for details.

run always returns a useful Result object providing access to the captured output, exit code, and other information.

Aside: what exactly is this ‘context’ arg anyway?

A common problem task runners face is transmission of “global” data - values loaded from configuration files or other configuration vectors, given via CLI flags, generated in ‘setup’ tasks, etc.

Some libraries (such as Fabric 1.x) implement this via module-level attributes, which makes testing difficult and error prone, limits concurrency, and increases implementation complexity.

Invoke encapsulates state in explicit Context objects, handed to tasks when they execute . The context is the primary API endpoint, offering methods which honor the current state (such as Context.run) as well as access to that state itself.

Declaring pre-tasks

Tasks may be configured in a number of ways via the task decorator. One of these is to select one or more other tasks you wish to always run prior to execution of your task, indicated by name.

Let’s expand our docs builder with a new cleanup task that runs before every build (but which, of course, can still be executed on its own):

from invoke import task

@task
def clean(c):
    c.run("rm -rf docs/_build")

@task(clean)
def build(c):
    c.run("sphinx-build docs docs/_build")

Now when you invoke build, it will automatically run clean first.

Note

If you’re not a fan of the implicit “positional arguments are pre-run task names” API, you can instead explicitly give the pre kwarg: @task(pre=[clean]).

Details can be found in How tasks run.

Creating namespaces

Right now, our tasks.py is implicitly for documentation only, but maybe our project needs other non-doc things, like packaging/deploying, testing, etc. At that point, a single flat namespace isn’t enough, so Invoke lets you easily build a nested namespace. Here’s a quick example.

Let’s first rename our tasks.py to be docs.py; no other changes are needed there. Then we create a new tasks.py, and for the sake of brevity populate it with a new, truly top level task called deploy.

Finally, we can use a new API member, the Collection class, to bind this task and the docs module into a single explicit namespace. When Invoke loads your task module, if a Collection object bound as ns or namespace exists it will get used for the root namespace:

from invoke import Collection, task
import docs

@task
def deploy(c):
    c.run("python setup.py sdist")
    c.run("twine upload dist/*")

namespace = Collection(docs, deploy)

The result:

$ invoke --list
Available tasks:

    deploy
    docs.build
    docs.clean

For a more detailed breakdown of how namespacing works, please see the docs.