pipen 1.1.19

A pipeline framework for python

A pipeline framework for python

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Documentation | ChangeLog | Examples | API

Why pipen?

pipen is designed for data scientists, bioinformaticians, and researchers who need to create reproducible, scalable computational pipelines without the complexity of traditional workflow systems.

Target Audience

• Data Scientists: Process large datasets with automatic parallelization and caching
• Bioinformaticians: Build reproducible analysis pipelines for genomics data
• Researchers: Create transparent, reproducible workflows for computational research
• DevOps Engineers: Orchestrate batch jobs across different schedulers (SLURM, SGE, Google Cloud)

Key Benefits

1. Zero Configuration

• Get started immediately with sensible defaults
• Configure only what you need, when you need it
• Profile-based configuration for different environments

2. Reproducibility Built-In

• Automatic job caching based on input/output signatures
• Full audit trail of pipeline runs and parameters
• Dependency tracking ensures processes run in correct order

3. Flexible Scheduling

• Run locally for development
• Scale to HPC clusters (SLURM, SGE)
• Deploy to cloud (Google Cloud Batch, SSH)
• Run in containers for reproducibility

4. Developer-Friendly

• Define pipelines as Python classes
• Use familiar Python syntax and tools
• Extensible plugin system for custom functionality
• Rich, informative logging and progress tracking

5. Data Flow Management

• Automatic data passing between pipeline stages
• Support for files, directories, and in-memory data
• Built-in operations for transforming and aggregating data

Comparison with Alternatives

Feature	pipen	Snakemake	Nextflow	Airflow
Target Audience	Data Scientists, Bioinformaticians, Researchers, DevOps	Bioinformaticians	Bioinformaticians	Data Engineers
Learning Curve	Low	Medium	High	High
Python Integration	Native	Limited	Limited	Native
Scheduler Support	6+ (Local, SGE, SLURM, SSH, Container, Gbatch)	Limited	Limited	Plugin-based
Caching	Built-in, automatic	Manual	Manual	Plugin-based
Cloud Native Support	Yes (Google Cloud Batch)	Partial	Yes	Yes
Interactive Debugging	Yes	Limited	No	No
Easy to Use	Define pipelines as Python classes, familiar syntax	Workflow DSL, separate config files	DAG definition in Python, complex UI
Zero Configuration	Sensible defaults, configure only what needed	Many configuration options	Heavy configuration required	Complex setup
Nice Logging	Rich, informative, color-coded, progress bars	Text-based	Text-based	Basic logging
Highly Extensible	Simple plugin system, hook-based	Custom rules/scripts	Custom operators	Custom operators/providers
Data Flow Management	Built-in channel operations (expand_dir, collapse_files)	Manual handling	Channel system	XCom system
Reproducibility	Built-in caching, full audit trail	Manual	Versioned containers	DAG versioning
Flexible Scheduling	Switch schedulers without code changes	Config-based	Config-based	Config-based

Installation

pip install -U pipen

Quickstart

example.py

from pipen import Proc, Pipen, run

class P1(Proc):
    """Sort input file"""
    input = "infile"
    input_data = ["/tmp/data.txt"]
    output = "outfile:file:intermediate.txt"
    script = "cat {{in.infile}} | sort > {{out.outfile}}"

class P2(Proc):
    """Paste line number"""
    requires = P1
    input = "infile:file"
    output = "outfile:file:result.txt"
    script = "paste <(seq 1 3) {{in.infile}} > {{out.outfile}}"

# class MyPipeline(Pipen):
#     starts = P1

if __name__ == "__main__":
    # MyPipeline().run()
    run("MyPipeline", starts=P1)

> echo -e "3\n2\n1" > /tmp/data.txt
> python example.py

04-17 16:19:35 I core                   _____________________________________   __
04-17 16:19:35 I core                   ___  __ \___  _/__  __ \__  ____/__  | / /
04-17 16:19:35 I core                   __  /_/ /__  / __  /_/ /_  __/  __   |/ /
04-17 16:19:35 I core                   _  ____/__/ /  _  ____/_  /___  _  /|  /
04-17 16:19:35 I core                   /_/     /___/  /_/     /_____/  /_/ |_/
04-17 16:19:35 I core
04-17 16:19:35 I core                               version: 1.1.16
04-17 16:19:35 I core
04-17 16:19:35 I core    ╔═══════════════════════════ MYPIPELINE ════════════════════════════╗
04-17 16:19:35 I core    ║ My pipeline                                                       ║
04-17 16:19:35 I core    ╚═══════════════════════════════════════════════════════════════════╝
04-17 16:19:35 I core    plugins         : verbose v1.1.1
04-17 16:19:35 I core    # procs         : 2
04-17 16:19:35 I core    profile         : default
04-17 16:19:35 I core    outdir          : /path/to/cwd/MyPipeline-output
04-17 16:19:35 I core    cache           : True
04-17 16:19:35 I core    dirsig          : 1
04-17 16:19:35 I core    error_strategy  : ignore
04-17 16:19:35 I core    forks           : 1
04-17 16:19:35 I core    lang            : bash
04-17 16:19:35 I core    loglevel        : info
04-17 16:19:35 I core    num_retries     : 3
04-17 16:19:35 I core    scheduler       : local
04-17 16:19:35 I core    submission_batch: 8
04-17 16:19:35 I core    template        : liquid
04-17 16:19:35 I core    workdir         : /path/to/cwd/.pipen/MyPipeline
04-17 16:19:35 I core    plugin_opts     :
04-17 16:19:35 I core    template_opts   : filters={'realpath': <function realpath at 0x7fc3eba12...
04-17 16:19:35 I core                    : globals={'realpath': <function realpath at 0x7fc3eba12...
04-17 16:19:35 I core    Initializing plugins ...
04-17 16:19:36 I core
04-17 16:19:36 I core    ╭─────────────────────────────── P1 ────────────────────────────────╮
04-17 16:19:36 I core    │ Sort input file                                                   │
04-17 16:19:36 I core    ╰───────────────────────────────────────────────────────────────────╯
04-17 16:19:36 I core    P1: Workdir: '/path/to/cwd/.pipen/MyPipeline/P1'
04-17 16:19:36 I core    P1: <<< [START]
04-17 16:19:36 I core    P1: >>> ['P2']
04-17 16:19:36 I verbose P1: in.infile: /tmp/data.txt
04-17 16:19:36 I verbose P1: out.outfile: /path/to/cwd/.pipen/MyPipeline/P1/0/output/intermediate.txt
04-17 16:19:38 I verbose P1: Time elapsed: 00:00:02.051s
04-17 16:19:38 I core
04-17 16:19:38 I core    ╭═══════════════════════════════ P2 ════════════════════════════════╮
04-17 16:19:38 I core    ║ Paste line number                                                 ║
04-17 16:19:38 I core    ╰═══════════════════════════════════════════════════════════════════╯
04-17 16:19:38 I core    P2: Workdir: '/path/to/cwd/.pipen/MyPipeline/P2'
04-17 16:19:38 I core    P2: <<< ['P1']
04-17 16:19:38 I core    P2: >>> [END]
04-17 16:19:38 I verbose P2: in.infile: /path/to/cwd/.pipen/MyPipeline/P1/0/output/intermediate.txt
04-17 16:19:38 I verbose P2: out.outfile: /path/to/cwd/MyPipeline-output/P2/result.txt
04-17 16:19:41 I verbose P2: Time elapsed: 00:00:02.051s
04-17 16:19:41 I core

             MYPIPELINE: 100%|██████████████████████████████| 2/2 [00:06<00:00, 0.35 procs/s]

> cat ./MyPipeline-output/P2/result.txt
1       1
2       2
3       3

Examples

See more examples at examples/ and a more realcase example at:

https://github.com/pwwang/pipen-report/tree/master/example

Plugin gallery

Plugins make pipen even better.

• pipen-annotate: Use docstring to annotate pipen processes
• pipen-args: Command line argument parser for pipen
• pipen-board: Visualize configuration and running of pipen pipelines on the web
• pipen-diagram: Draw pipeline diagrams for pipen
• pipen-dry: Dry runner for pipen pipelines
• pipen-filters: Add a set of useful filters for pipen templates.
• pipen-lock: Process lock for pipen to prevent multiple runs at the same time.
• pipen-log2file: Save running logs to file for pipen
• pipen-poplog: Populate logs from jobs to running log of the pipeline
• pipen-report: Generate report for pipen
• pipen-runinfo: Save running information to file for pipen
• pipen-verbose: Add verbosal information in logs for pipen.
• pipen-gcs: A plugin for pipen to handle files in Google Cloud Storage.
• pipen-deprecated: A pipen plugin to mark processes as deprecated.
• pipen-mcp: A pipen plugin to turn pipen processes into MCP (model context protocol) processes.
• pipen-cli-init: A pipen CLI plugin to create a pipen project (pipeline)
• pipen-cli-ref: Make reference documentation for processes
• pipen-cli-require: A pipen cli plugin check the requirements of a pipeline
• pipen-cli-run: A pipen cli plugin to run a process or a pipeline
• pipen-cli-gbatch: A pipen cli plugin to submit pipeline to Google Batch Jobs