sciluigi

Helper library for writing dynamic, flexible workflows in luigi

Project description

Note

For the latest source, issues and discussion, etc, please visit the GitHub repository

SciLuigi Logo

*UPDATE, Nov, 2016: A paper with the motivation and design decisions behind SciLuigi `now available <http://dx.doi.org/10.1186/s13321-016-0179-6>`__*
If you use SciLuigi in your research, please cite it like this: Lampa S, Alvarsson J, Spjuth O. Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles. J Cheminform. 2016. doi:10.1186/s13321-016-0179-6.
*A Virtual Machine with a realistic, runnable, example workflow in a Jupyter Notebook, is available `here <https://github.com/pharmbio/bioimg-sciluigi-casestudy>`__*
*Watch a 10 minute screencast going through the basics of using SciLuigi `here <https://www.youtube.com/watch?v=gkKUWskRbjw>`__*
*See a poster describing the motivations behind SciLuigi `here <http://dx.doi.org/10.13140/RG.2.1.1143.6246>`__*

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

Luigi is a flexile and 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 e.g. in bioinformatics where multiple inputs and outputs, complex dependencies, and the need to quickly try different workflow connectivity in an explorative fashion is central to the way of working.

SciLuigi was designed to solve some of these problems, by providing 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 easy-to-use API these changes have been implemented as a very thin layer on top of luigi’s own API with no changes at all to the luigi core, which means that 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

*For a brief 10 minute screencast going through the basics below, see `this link <https://www.youtube.com/watch?v=gkKUWskRbjw>`__*

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!

Support: Getting help

Please use the issue queue for any support questions, rather than mailing the author(s) directly, as the solutions can then help others who face similar issues (we are a very small team with very limited time, so this is important).

Prerequisites

Python 2.7 - 3.4
Luigi 1.3.x - 2.0.1

Install

Install SciLuigi, including its dependencies (luigi etc), through PyPI:
```
pip install sciluigi
```
Now you can use the library by just importing it in your python script, like so:
```
import sciluigi
```
Note that you can aliase it to a shorter name, for brevity, and to save keystrokes:
```
import sciluigi as sl
```

Usage

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

Create a workflow tasks class
Create task classes
Add the workflow definition in the workflow class’s workflow() method.
Add a run method at the end of the script
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:

Creating a subclass of sciluigi.WorkflowTask
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:

Creating a subclass of sciluigi.Task (or sciluigi.SlurmTask if you want Slurm support)
Adding fields named in_<yournamehere> for each input, in the new task class
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).
Define luigi parameters to the task.
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 that replaces “foo” with “bar”:

class MyFooReplacer(sciluigi.Task):
    replacement = sciluigi.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 sciluigi.TargetInfo(self, self.in_foo().path + '.bar.txt')
    def run(self):
        with self.in_foo().open() as in_f:
            with self.out_replaced().open('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}/g' {inpath} > {outpath}".format(
        repl=self.replacement,
        inpath=self.in_foo().path,
        outpath=self.out_replaced().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:

Instantiating the tasks, using the self.new_task(<unique_taskname>, <task_class>, *args, **kwargs) method, of the workflow task.
Connect the tasks together, by pointing the right out_* method to the right in_* field.
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:

sciluigi.run()
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!

Known Limitations

Changing the workflow scheduling based on data sent as parameters, is not possible.
If you have an unknown number of outputs from a task, for which you want to start a full branch of the workflow, this is not possible either.

Both of the limitations are due to the fact that Luigi does scheduling and execution separately (with the exception of Luigi’s dynamic dependencies, but they work only for upstream tasks, not downstream tasks, which we would need).

If you run into any of these problems, you might be interested in an experimental workflow engine we develop to overcome these limitations: SciPipe.