chasqui

Chasqui

Developer Guide

If you are new to using nbdev here are some useful pointers to get you started.

Install in Development mode

# make sure  package is installed in development mode
$ pip install -e .

# make changes under nbs/ directory
# ...

# compile to have changes apply to 
$ nbdev_prepare

Usage

Installation

Install latest from the GitHub repository:

$ pip install git+https://github.com//.git

or from conda

$ conda install -c  

or from pypi

$ pip install 

About Chasqui

The Need for Simple Workflow Systems

Running computational materials calculations on HPC clusters often involves: - Submitting hundreds of jobs with varying parameters - Monitoring queue status across multiple runs - Managing data transfer between local and remote systems - Respecting queue policies (maximum jobs, walltime limits) - Dealing with authentication barriers (2FA, SSH keys)

Existing workflow systems like AiiDA, FireWorks, or Snakemake are powerful but can be heavy—requiring databases, web servers, or complex configuration. For individual researchers or small groups, there’s a gap: we need something lightweight, transparent, and easy to understand.

chasqui fills this gap by embracing simplicity:

• Single SQLite file - no database server
• Plain bash scripts - no daemons or background processes
• SSH and PBS only - standard HPC tools
• Literate programming - readable notebooks, not opaque frameworks

Why “Chasqui”?

The name comes from the chasqui (also chaski), the relay messengers of the Inca Empire. These runners formed a sophisticated communication network spanning thousands of kilometers across the Andes, carrying messages and small goods between administrative centers.

Chasquis often carried khipus (quipus)—intricate systems of knotted strings that encoded numerical data, records, and possibly narratives. These weren’t just messages; they were structured data that could be interpreted by trained khipucamayuq (khipu keepers) at each station.

Design Philosophy

1. Transparency — You should understand what’s happening
2. Simplicity — Fewer moving parts means less to break
3. Flexibility — Work with your HPC environment, not against it
4. Literate — Code as documentation, documentation as code

chasqui isn’t trying to be the most feature-rich workflow system. It’s trying to be the one you can debug at 2 AM when your calculations are due, understand six months later when you return to a project, and modify when your HPC center changes queue policies.

How to use

Fill me in please! Don’t forget code examples:

1+1

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Documentation

Documentation can be found hosted on this GitHub repository’s pages. Additionally you can find package manager specific guidelines on conda and pypi respectively.

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