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https://github.com/getpopper/popper

Container-native task automation engine.
https://github.com/getpopper/popper

automation-engine cli containers devops docker podman reproducibility sciops singularity workflows

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Container-native task automation engine.

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README 

        
# Popper Popper



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Popper is a tool for defining and executing container-native testing 

workflows in Docker. With Popper, you define a workflow in a YAML 

file, and then execute it with a single command. A workflow file looks 

like this:



```yaml

steps:

- id: dev-init

  uses: docker://rikorose/gcc-cmake:gcc-9

  runs: [sh, -uexc]

  args:

  - |

    rm -rf build/

    cmake -DCMAKE_BUILD_TYPE=Release -S . -B build



- id: build

  uses: docker://rikorose/gcc-cmake:gcc-9

  runs: [cmake, --build, build/, --parallel, '4']



- id: test

  uses: docker://rikorose/gcc-cmake:gcc-9

  dir: /workspace/build/

  runs: [ctest]

```



Assuming the above is stored in a `ci.yml` file in the root of your 

project folder, this entire workflow gets executed by running:



```bash

popper run -f ci.yml

```



Running a single step:



```bash

popper run -f ci.yml build

```



Starting a shell inside the `build` step container:



```bash

popper run -f ci.yml build

```



Running on another engine (Podman):



```bash

popper run -f ci.yml -e podman build

```



See the [`examples/`](./examples) folder for examples for tests for 

other languages, as well as other types of tests (integration, 

regresssion, etc.).



## Installation



To install or upgrade Popper, run the following in your terminal:



```bash

curl -sSfL https://raw.githubusercontent.com/getpopper/popper/master/install.sh | sh

```



[Docker][docker] is required to run Popper and the installer will 

abort if the `docker` command cannot be invoked from your shell. For 

other installation options, including installing for use with the 

other supported engines (Singularity and Podman), or for setting up a 

developing environment for Popper, [read the complete installation 

instructions][installation].



Once installed, you can get an overview and list of available 

commands:



```bash

popper help

```



Read the [Quickstart Guide][getting_started] to learn the basics of 

how to use Popper. Or browse the [Official documentation][docs].



## Features



  * **Lightweight workflow and task automation syntax.** Defining a list of 

    steps is as simple as writing file in a [lightweight YAML syntax][cnwf] and 

    invoking `popper run` (see demo above). If you're familiar with 

    [Docker Compose][compose], you can think of Popper as Compose but 

    for end-to-end tasks instead of services.



  * **An abstraction over container runtimes**. In addition to Docker, 

    Popper can seamlessly execute workflows in other runtimes by 

    interacting with distinct container engines. Popper currently 

    supports [Docker][docker], [Singularity][sylabs] and 

    [Podman][podman].



  * **An abstraction over CI services**. Define a pipeline once and 

    then instruct Popper to generate configuration files for distinct 

    CI services, allowing users to run the exact same workflows they 

    run locally on Travis, Jenkins, Gitlab, Circle and others. See the 

    [`examples/`](./examples/) folder for examples on how to automate 

    CI tasks for multiple projects (Go, C++, Node, etc.).



  * **An abstraction over resource managers**. Popper can also execute 

    workflows on a variety of resource managers and schedulers such as 

    Kubernetes and SLURM, without requiring any modifications to a 

    workflow YAML file. We currently support SLURM and are working on 

    adding support for Kubernetes.



  * **Aid in workflow development**. Aid in the implementation and 

    [debugging][pp-sh] of workflows, and provide with an extensive 

    list of [example 

    workflows](https://github.com/getpopper/popper-examples) that can 

    serve as a starting point.



## What Problem Does Popper Solve?



Popper is a container-native workflow execution and task automation 

engine. In practice, when we work following the 

[container-native](docs/sections/concepts.md) paradigm, we end up 

interactively executing multiple Docker commands (`docker pull`, 

`docker build`, `docker run`, etc.) so that we can build containers, 

compile code, test applications, deploy software, among others. 

Keeping track of which `docker` commands we have executed, in which 

order, and which flags were passed to each, can quickly become 

unmanageable, difficult to document (think of outdated README 

instructions) and error prone.



On top of this, having the same workflow work in other environments 

(CI, K8S, etc.) is time-consuming and defeats the purpose of using 

containers (portability). The goal of Popper is to bring order to this 

chaotic scenario by providing a framework for clearly and explicitly 

defining container-native tasks. You can think of Popper as tool for 

wrapping all these manual tasks in a lightweight, machine-readable, 

self-documented format (YAML).



While this sounds simple at first, it has significant implications: 

results in time-savings, improves communication and in general unifies 

development, testing and deployment workflows. As a developer or user 

of "Popperized" container-native projects, you only need to learn one 

tool, and leave the execution details to Popper, whether is to build 

and tests applications locally, on a remote CI server or a Kubernetes 

cluster.



## Contributing



Anyone is welcome to contribute to Popper! To get started, take a look 

at our [contributing guidelines](CONTRIBUTING.md), then dive in with 

our [list of good first issues][gfi].



## Participation Guidelines



Popper adheres to the code of conduct [posted in this 

repository](CODE_OF_CONDUCT.md). By participating or contributing to 

Popper, you're expected to uphold this code. If you encounter unacceptable 

behavior, please immediately [email us](mailto:[email protected]).



## How to Cite Popper



> Ivo Jimenez, Michael Sevilla, Noah Watkins, Carlos Maltzahn, Jay 

> Lofstead, Kathryn Mohror, Andrea Arpaci-Dusseau and Remzi 

> Arpaci-Dusseau. _The Popper Convention: Making Reproducible Systems 

> Evaluation Practical_. In 2017 IEEE International Parallel and 

> Distributed Processing Symposium Workshops (IPDPSW), 1561–70, 2017. 

> (https://doi.org/10.1109/IPDPSW.2017.157)



PDF for a pre-print version [available here](https://raw.githubusercontent.com/systemslab/popper-paper/master/paper/paper.pdf). 

For BibTeX, [click here](https://raw.githubusercontent.com/systemslab/popper-paper/master/popper.bib).



[gfi]: https://github.com/getpopper/popper/issues?utf8=%E2%9C%93&q=is%3Aissue+label%3A%22good+first+issue%22+is%3Aopen

[docker]: https://docs.docker.com/get-docker/

[getting_started]: https://popper.readthedocs.io/en/latest/sections/getting_started.html

[docs]: https://popper.readthedocs.io/en/latest/

[sylabs]: https://sylabs.io/

[compose]: https://docs.docker.com/compose/

[podman]: https://podman.io

[pp-sh]: docs/sections/cli_features.md#executing-a-step-interactively

[installation]: docs/installation.md

[cnwf]: ./docs/sections/cn_workflows.md#syntax