popper:容器本机任务自动化引擎

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  • 2022-05-29 11:11
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波普尔 Popper是用于在Docker中定义和执行容器本机测试工作流程的工具。 使用Popper,您可以在YAML文件中定义工作流程,然后使用单个命令执行该工作流程。 工作流程文件如下所示: 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:
popper-master.zip
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# <img src="https://raw.githubusercontent.com/getpopper/website/bcba4c8/assets/images/popper_logo_just_jug.png" width="64" valign="middle" alt="Popper"/> Popper [![Downloads](https://pepy.tech/badge/popper)](https://pepy.tech/project/popper) [![Build Status](https://travis-ci.org/getpopper/popper.svg?branch=master)](https://travis-ci.org/getpopper/popper) [![codecov](https://codecov.io/gh/getpopper/popper/branch/master/graph/badge.svg)](https://codecov.io/gh/getpopper/popper) [![Join the chat at https://gitter.im/systemslab/popper](https://badges.gitter.im/systemslab/popper.svg)](https://gitter.im/falsifiable-us/popper?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) [![slack](https://img.shields.io/badge/chat-on_slack-C03C20.svg?logo=slack)](https://join.slack.com/t/getpopper/shared_invite/zt-dtn0se2s-c50myMHNpeoikQXDeNbPew) [![CROSS](https://img.shields.io/badge/supported%20by-CROSS-green)](https://cross.ucsc.edu) 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:ivotron@ucsc.edu). ## 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
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