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:
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:
popper run -f ci.yml
Running a single step:
popper run -f ci.yml build
Starting a shell inside the
build step container:
popper run -f ci.yml build
Running on another engine (Podman):
popper run -f ci.yml -e podman build
examples/ folder for examples for tests for
other languages, as well as other types of tests (integration,
To install or upgrade Popper, run the following in your terminal:
curl -sSfL https://raw.githubusercontent.com/getpopper/popper/master/install.sh | sh
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
Once installed, you can get an overview and list of available commands:
Lightweight workflow and task automation syntax. Defining a list of steps is as simple as writing file in a lightweight YAML syntax and invoking
popper run(see demo above). If you're familiar with Docker 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, Singularity and 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/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.
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 paradigm, we end up
interactively executing multiple Docker commands (
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.
Popper adheres to the code of conduct posted in this repository. By participating or contributing to Popper, you're expected to uphold this code. If you encounter unacceptable behavior, please immediately email us.
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)