README.rst

README

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doit - automation tool

doit comes from the idea of bringing the power of build-tools to execute any kind of task

doit can be uses as a simple Task Runner allowing you to easily define ad hoc tasks, helping you to organize all your project related tasks in an unified easy-to-use & discoverable way.

doit scales-up with an efficient execution model like a build-tool. doit creates a DAG (direct acyclic graph) and is able to cache task results. It ensures that only required tasks will be executed and in the correct order (aka incremental-builds).

The up-to-date check to cache task results is not restricted to looking for file modification on dependencies. Nor it requires "target" files. So it is also suitable to handle workflows not handled by traditional build-tools.

Tasks' dependencies and creation can be done dynamically during it is execution making it suitable to drive complex workflows and pipelines.

doit is build with a plugin architecture allowing extensible commands, custom output, storage backend and "task loader". It also provides an API allowing users to create new applications/tools leveraging doit functionality like a framework.

doit is a mature project being actively developed for more than 10 years. It includes several extras like: parallel execution, auto execution (watch for file changes), shell tab-completion, DAG visualisation, IPython integration, and more.

Sample Code

Define functions returning python dict with task's meta-data.

Snippet from tutorial:

def task_imports():
    """find imports from a python module"""
    for name, module in PKG_MODULES.by_name.items():
        yield {
            'name': name,
            'file_dep': [module.path],
            'actions': [(get_imports, (PKG_MODULES, module.path))],
        }

def task_dot():
    """generate a graphviz's dot graph from module imports"""
    return {
        'targets': ['requests.dot'],
        'actions': [module_to_dot],
        'getargs': {'imports': ('imports', 'modules')},
        'clean': True,
    }

def task_draw():
    """generate image from a dot file"""
    return {
        'file_dep': ['requests.dot'],
        'targets': ['requests.png'],
        'actions': ['dot -Tpng %(dependencies)s -o %(targets)s'],
        'clean': True,
    }

Run from terminal:

$ doit list
dot       generate a graphviz's dot graph from module imports
draw      generate image from a dot file
imports   find imports from a python module
$ doit
.  imports:requests.models
.  imports:requests.__init__
.  imports:requests.help
(...)
.  dot
.  draw

Project Details

• Website & docs - http://pydoit.org
• Project management on github - https://github.com/pydoit/doit
• Discussion group - https://groups.google.com/forum/#!forum/python-doit
• News/twitter - https://twitter.com/pydoit
• Plugins, extensions and projects based on doit - https://github.com/pydoit/doit/wiki/powered-by-doit

license

The MIT License Copyright (c) 2008-2021 Eduardo Naufel Schettino

see LICENSE file

developers / contributors

see AUTHORS file

install

doit is tested on python 3.6 to 3.10.

The last version supporting python 2 is version 0.29.

$ pip install doit

dependencies

• cloudpickle
• pyinotify (linux)
• macfsevents (mac)

Tools required for development:

• git * VCS
• py.test * unit-tests
• coverage * code coverage
• sphinx * doc tool
• pyflakes * syntax checker
• doit-py * helper to run dev tasks

development setup

The best way to setup an environment to develop doit itself is to create a virtualenv...

doit$ virtualenv dev
doit$ source dev/bin/activate

install doit as "editable", and add development dependencies from dev_requirements.txt:

(dev) doit$ pip install --editable .
(dev) doit$ pip install --requirement dev_requirements.txt

tests

Use py.test - http://pytest.org

$ py.test

documentation

doc folder contains ReST documentation based on Sphinx.

doc$ make html

They are the base for creating the website. The only difference is that the website includes analytics tracking. To create it (after installing doit):

$ doit website

spell checking

All documentation is spell checked using the task spell:

$ doit spell

It is a bit annoying that code snippets and names always fails the check, these words must be added into the file doc/dictionary.txt.

The spell checker currently uses hunspell, to install it on debian based systems install the hunspell package: apt-get install hunspell.

profiling

python -m cProfile -o output.pstats `which doit` list

gprof2dot -f pstats output.pstats | dot -Tpng -o output.png

releases

Update version number at:

• doit/version.py
• setup.py
• doc/conf.py
• doc/index.html

python setup.py sdist
python setup.py bdist_wheel
twine upload dist/doit-X.Y.Z.tar.gz
twine upload dist/doit-X.Y.Z-py3-none-any.whl

Remember to push GIT tags:

git push --tags

contributing

On github create pull requests using a named feature branch.

Financial contribution to support maintenance welcome.