README.md

Note: this library is still work in progress, but is currently entering production (as of August 31, 2015) and is getting stabler and stabler by the hour. Adventurous early-adopters are much welcome to test it out and file issues for anything not working properly, features missing, or suggestions for improvements!

Scientific Luigi (SciLuigi for short) is a light-weight wrapper library around Spotify's Luigi workflow system that aims to make writing scientific workflows (consisting of numerous interdependent commandline applications) more fluent, flexible and modular.

Luigi is a great, flexible, and very fun-to-use library. It has turned out though, that its default way of defining dependencies by hard coding them in each task's requires() function is not optimal for some type of workflows common in scientific fields such as bioinformatics, where multiple inputs and outputs, complex dependencies, and the need to quickly try different workflow connectivity (e.g. plugging in extra filtering steps) in an explorative fashion is central to the way of working.

SciLuigi was designed to solve some these problem we were facing when trying to use luigi for defining complex workflows for data preprocessing, machine-learning and cross-validation.

To achieve that, SciLuigi provides the following "features" over vanilla Luigi:

• Separation of dependency definitions from the tasks themselves, for improved modularity and composability.
• Inputs and outputs implemented as separate fields, a.k.a. "ports", to allow specifying dependencies between specific input and output-targets rather than just between tasks. This is again to let such details of the network definition reside outside the tasks.
• The fact that inputs and outputs are object fields, also allows auto-completion support to ease the network connection work (Works great e.g. with jedi-vim.
• Inputs and outputs are connected with an intuitive "single-assignment syntax".
• Good default high-level logging of workflow tasks and execution times.
• Produces an easy to read audit-report with high level information per task.
• Integration with some HPC workload managers. (So far only SLURM though).

Because of Luigi's great easy-to-use API, these changes have been implemented as a very thin layer on top of luigi's own API, and no changes to the luigi core is needed at all, so you can continue leveraging the work already being put into maintaining and further developing luigi, by the team at Spotify and others.

Workflow code quick demo

Just to give a quick feel for how a workflow definition might look like in SciLuigi, check this code example (implementation of tasks hidden here for brevity. See Usage section further below for more details):

import sciluigi as sl

class MyWorkflow(sl.WorkflowTask):
    def workflow(self):
        # Initialize tasks:
        foowrt = self.new_task('foowriter', MyFooWriter)
        foorpl = self.new_task('fooreplacer', MyFooReplacer,
            replacement='bar')

        # Here we do the *magic*: Connecting outputs to inputs:
        foorpl.in_foo = foowrt.out_foo

        # Return the last task(s) in the workflow chain.
        return foorpl

That's it! And again, see the "usage" section just below for a more detailed description of getting to this!

Usage

Creating workflows in SciLuigi differs slightly from how it is done in vanilla Luigi. Very briefly, it is done in these main steps:

1. Create a workflow tasks clas
2. Create task classes
3. Add the workflow definition in the workflow class's worklfow() method.
4. Add a run method at the end of the script
5. Run the script

Create a Workflow task

The first thing to do when creating a workflow, is to define a workflow task.

You do this by:

1. Creating a subclass of sciluigi.WorkflowTask
2. Implementing the workflow() method.

Example:

import sciluigi

class MyWorkflow(sciluigi.WorkflowTask):
    def workflow(self):
        pass # TODO: Implement workflow here later!

Create tasks

Then, you need to define some tasks that can be done in this workflow.

This is done by:

1. Creating a subclass of sciluigi.Task (or sciluigi.SlurmTask if you want Slurm support)
2. Adding fields named in_<yournamehere> for each input, in the new task class
3. Define methods named out_<yournamehere>() for each output, that return sciluigi.TargetInfo objects. (sciluigi.TargetInfo is initialized with a reference to the task object itself - typically self - and a path name, where upstream tasks paths can be used).
4. Define luigi parameters to the task.
5. Implement the run() method of the task.

Example:

Let's define a simple task that just writes "foo" to a file named foo.txt:

class MyFooWriter(sciluigi.Task):
    # We have no inputs here
    # Define outputs:
    def out_foo(self):
        return sciluigi.TargetInfo(self, 'foo.txt')
    def run(self):
        with self.out_foo().open('w') as foofile:
            foofile.write('foo\n')

Then, let's create a task taht replaces "foo" with "bar":

class MyFooReplacer(sciluigi.Task):
    replacement = luigi.Parameter() # Here, we take as a parameter
                                  # what to replace foo with.
    # Here we have one input, a "foo file":
    in_foo = None
    # ... and an output, a "bar file":
    def out_replaced(self):
        # As the path to the returned target(info), we
        # use the path of the foo file:
        return TargetInfo(self, self.in_foo().path + '.bar.txt')
    def run(self):
        with self.in_foo().open() as in_f:
            with self.out_replaced('w') as out_f:
                # Here we see that we use the parameter self.replacement:
                out_f.write(in_f.read().replace('foo', self.replacement))

The last lines, we could have instead written using the command-line sed utility, available in linux, by calling it on the commandline, with the built-in ex() method:

    def run(self):
        # Here, we use the in-built self.ex() method, to execute commands:
        self.ex("sed 's/foo/{repl}' {in} > {out}".format(
            repl=self.replacement,
            in=self.in_foo().path,
            out=self.out_bar().path))

Write the workflow definition

Now, we can use these two tasks we created, to create a simple workflow, in our workflow class, that we also created above.

We do this by:

1. Instantiating the tasks, using the self.new_task(<unique_taskname>, <task_class>, *args, **kwargs) method, of the workflow task.
2. Connect the tasks together, by pointing the right out_* method to the right in_* field.
3. Returning the last task in the chain, from the workflow method.

Example:

import sciluigi
class MyWorkflow(sciluigi.WorkflowTask):
    def workflow(self):
        foowriter = self.new_task('foowriter', MyFooWriter)
        fooreplacer = self.new_task('fooreplacer', MyFooReplacer,
            replacement='bar')

        # Here we do the *magic*: Connecting outputs to inputs:
        fooreplacer.in_foo = foowriter.out_foo

        # Return the last task(s) in the workflow chain.
        return fooreplacer

Add a run method to the end of the script

Now, the only thing that remains, is adding a run method to the end of the script.

You can use luigi's own luigi.run(), or our own two methods:

1. sciluigi.run()
2. sciluigi.run_local()

The run_local() one, is handy if you don't want to run a central scheduler daemon, but just want to run the workflow as a script.

Both of the above take the same options as luigi.run(), so you can for example set the main class to use (our workflow task):

# End of script ....
if __name__ == '__main__':
    sciluigi.run_local(main_task_cls=MyWorkflow)

Run the workflow

Now, you should be able to run the workflow as simple as:

python myworkflow.py

... provided of course, that the workflow is saved in a file named myworkflow.py.

More Examples

See the examples folder for more detailed examples!

More links, background info etc.

The basic idea behind SciLuigi, and a preceding solution to it, was presented in workshop (e-Infra MPS 2015) talk:

• Slides
• Video

See also this collection of links, to more of our reported experiences using Luigi, which lead up to the creation of SciLuigi.

Acknowledgements

This work is funded by:

• Faculty grants of the dept. of Pharmaceutical Biosciences, Uppsala University
• Bioinformatics Infrastructure for Life Sciences, BILS

Many ideas and inspiration for the API is taken from:

• John Paul Morrison's invention and works on Flow-Based Programming