Introduction to Popper Pipelines

Over the last decade software engineering and systems administration communities (also referred to as DevOps) have developed sophisticated techniques and strategies to ensure “software reproducibility”, i.e. the reproducibility of software artifacts and their behavior using versioning, dependency management, containerization, orchestration, monitoring, testing and documentation. The key idea behind the Popper protocol is to manage every experiment in computation and data exploration as a software project, using tools and services that are readily available now and enjoy wide popularity. By doing so, scientific explorations become reproducible with the same convenience, efficiency, and scalability as software repeatable while fully leveraging continuing improvements to these tools and services. Rather than mandating a particular set of tools, the convention only expects components of an experiment to be scripted. There are two main goals for Popper:

  1. It should be usable in as many research projects as possible, regardless of their domain.
  2. It should abstract underlying technologies without requiring a strict set of tools, making it possible to apply it on multiple toolchains.

Popper Pipelines

A common generic analysis/experimentation workflow involving a computational component is the one shown below. We refer to this as a pipeline in order to abstract from experiments, simulations, analysis and other types of scientific explorations. Although there are some projects that don’t fit this description, we focus on this model since it covers a large portion of pipelines out there. Typically, the implementation and documentation of a scientific exploration is commonly done in an ad-hoc way (custom bash scripts, storing in local archives, etc.).

Experimentation Workflow. The analogy of a lab notebook inexperimental sciences is to document an experiment's evolution. Thisis rarely done and, if done, usually in an ad-hoc way (an actualnotebook or a text file).

The idea behind Popper is simple: make an article self-contained by including in a code repository the manuscript along with every experiment’s scripts, inputs, parametrization, results and validation. To this end we propose leveraging state-of-the-art technologies and applying a DevOps approach to the implementation of scientific pipelines (also referred to SciOps).

DevOps approach to Implementing Scientific Explorations, alsoreferred to as SciOps.

Popper is a convention (or protocol) that maps the implementation of a pipeline to software engineering (and DevOps/SciOps) best-practices followed in open-source software projects. If a pipeline is implemented by following the Popper convention, we call it a popper-compliant pipeline or popper pipeline for short. A popper pipeline is implemented using DevOps tools (e.g., version-control systems, lightweight OS-level virtualization, automated multi-node orchestration, continuous integration and web-based data visualization), which makes it easier to re-execute and validate.

We say that an article (or a repository) is Popper-compliant if its scripts, dependencies, parameterization, results and validations are all in the same respository (i.e., the pipeline is self-contained). If resources are available, one should be able to easily re-execute a popper pipeline in its entirety. Additionally, the commit log becomes the lab notebook, which makes the history of changes made to it available to readers, an invaluable tool to learn from others and “stand on the shoulder of giants”. A “popperized” pipeline also makes it easier to advance the state-of-the-art, since it becomes easier to extend existing work by applying the same model of development in OSS (fork, make changes, publish new findings).

Repository Structure

The general repository structure is simple: a paper and pipelines folders on the root of the project with one subfolder per pipeline

$> tree mypaper/
├── pipelines
│   ├── exp1
│   │   ├──
│   │   ├── output
│   │   │   ├── exp1.csv
│   │   │   ├──
│   │   │   └── view.ipynb
│   │   ├──
│   │   ├──
│   │   ├──
│   │   └──
│   ├── analysis1
│   │   ├──
│   │   └── ...
│   └── analysis2
│       ├──
│       └── ...
└── paper
    ├── figures/
    ├── paper.tex
    └── refs.bib

Pipeline Folder Structure

A minimal pipeline folder structure for an experiment or analysis is shown below:

$> tree -a paper-repo/pipelines/myexp

Every pipeline has,,, and scripts that serve as the entrypoints to each of the stages of a pipeline. All these return non-zero exit codes if there’s a failure. In the case of, this script should print to standard output one line per validation, denoting whether a validation passed or not. In general, the form for validation results is [true|false] <statement> (see examples below).

[true]  algorithm A outperforms B
[false] network throughput is 2x the IO bandwidth

The CLI tool includes a pipeline init subcommand that can be executed to scaffold a pipeline with the above structure. The syntax of this command is:

popper pipeline init <name>

Where <name> is the name of the pipeline to initialize. More details on how pipelines are executed is presented in the next section.