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# Overview

The `task` module is a lightweight way to add support for running computational pipelines.

## Usage

Each stage or task in a computational pipeline is represented by a python function Each python function can be called in parallel to run multiple jobs.

1. Annotate functions with python decorators

   > Decorator | Purpose | Example
   > --- | --- | ---
   > @follows | Indicate task dependency mkdir prerequisite shorthand | `@follows(task1, "task2")` `@follows(task1, mkdir("my/directory/for/results"))`
   > @parallel | Parameters for parallel jobs | `@parallel(parameter_list)` `@parallel(parameter_generating_function)`
   > @files | I/O parameters skips up-to-date jobs | `@files(parameter_list)` `@files(parameter_generating_function)` `@files(input, output, other_params_for_a_single_job)`
   > @files_re | I/O file names via regular expressions start from lists of file names or `glob` results skips up-to-date jobs | `@files_re(glob_str, matching_regex, pattern_for_output_filenames)` `@files_re(file_names, matching_regex, pattern_for_output_filenames)` `@files_re(glob_str, matching_regex, pattern_for_input_filenames, pattern_for_output_filenames)` `@files_re(file_names, matching_regex, pattern_for_input_filenames, pattern_for_output_filenames)`
   > @check_if_uptodate | Checks if task needs to be run | `@check_if_uptodate(is_task_up_to_date_function)`
   > @posttask | Call function after task touch file shorthand | `@posttask(signal_task_completion_function)` `@posttask(touch_file("task1.completed")`

2. Print dependency graph if you necessary

   > - For a graphical flowchart in `jpg`, `svg`, `dot`, `png`, `ps`, `gif` formats:

   >       graph_printout ( open("flowchart.svg", "w"),
   >                        "svg",
   >                        list_of_target_tasks)

   > This requires `dot` to be installed

   > - For a text printout of all jobs

   >       pipeline_printout(sys.stdout, list_of_target_tasks)

3. Run the pipeline:

       pipeline_run(list_of_target_tasks, [list_of_tasks_forced_to_rerun, multiprocess = N_PARALLEL_JOBS])

See the Tutorial for a more complete introduction on how to add support for ruffus.

## Background

The purpose of a pipeline is to determine automatically which parts of a multistage process needs to be run and in what order in order to reach an objective (“targets”)

Computational pipelines, especially for analysing large scientific datasets are in widespread use. However, even a conceptually simple series of steps can be difficult to set up and to maintain, perhaps because the right tools are not available.

## Design

The ruffus module has the following design goals:

> - Simplicity. Can be picked up in 10 minutes
> - Elegance
> - Lightweight
> - Unintrusive
> - Flexible/Powerful

## Features

Automatic support for

> - Managing dependencies
> - Parallel jobs
> - Re-starting from arbitrary points, especially after errors
> - Display of the pipeline as a flowchart
> - Reporting

## Alternatives

Often, tools used to build executables can be used to manage computational pipelines. These include

> - GNU make
> - scons
> - ant

It is often necessary to learn a specialised (domain-specific) language. GNU make syntax, for example, is much critised because of limited support for abstraction compared with modern programming languages like C, Perl, python etc. GNU makefiles can quickly become unmaintainable

Pipeline specifications are usually written in a “declarative” rather than “imperative” manner. You write a specification that describes the dependencies, and the tool figures out how to perform the computations in the correct order. However, because GNU make and its kin depend entirely on file dependencies, the links between pipeline stages can be difficult to trace, and nigh impossible to debug when there are problems.

There are also complete workload managements systems such as Condor. Various bioinformatics pipelines are also available, including that used by the leading genome annotation website Ensembl, Pegasys, GPIPE, Taverna, Wildfire, MOWserv, Triana, Cyrille2 etc. These all are either hardwired to specific databases, and tasks, or have steep learning curves for both the scientist/developer and the IT system administrators

See also

Make like tools

GNU Make:
  http://www.gnu.org/software/make/
SCONS:
  http://www.scons.org/
Apache Ant:
  http://ant.apache.org/

###

See also

Bioinformatics pipelines

> Condor:
>   http://www.cs.wisc.edu/condor/description.html
> Ensembl Analysis pipeline:
>   http://www.ncbi.nlm.nih.gov/pubmed/15123589
> Pegasys:
>   http://www.ncbi.nlm.nih.gov/pubmed/15096276
> GPIPE:
>   http://www.biomedcentral.com/pubmed/15096276
> Taverna:
>   http://www.ncbi.nlm.nih.gov/pubmed/15201187
> Wildfire:
>   http://www.biomedcentral.com/pubmed/15788106
> MOWserv:
>   http://www.biomedcentral.com/pubmed/16257987
> Triana:
>   http://dx.doi.org/10.1007/s10723-005-9007-3
> Cyrille2:
>   http://www.biomedcentral.com/1471-2105/9/96

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