Popper

Popper is an experimentation protocol for organizing a academic article’s artifacts following a DevOps approach (sometimes referred to as “SciOps”). This documentation describes the experimentation protocol and the Popper CLI tool; it gives examples from multiple domains showing how to follow the protocol; and also shows how to use a CI system to continuously validate Popperized experiments.

• Getting Started
  • New pipeline
  • Popper Run
  • Adding Project to GitHub
  • Adding Project to Travis

• Concepts
  • Scientific Exploration Pipelines
  • Popper Pipelines
  • Popper vs. Other Software

• CLI feautures
  • New pipeline initialization
  • Executing a pipeline
  • Specifying environment requirements
  • Reusing existing pipelines
  • Continuously validating a pipeline
  • Popper Badges
  • Visualizing a pipeline
  • Adding metadata to a project
  • Archiving and DOI generation
  • The popper.yml configuration file

• CI features
  • Execution logic
  • Execution environments
  • Parametrizing pipelines
  • Matrix Executions
  • Popper Badges

• Examples
  • Pipeline portability
  • Dataset Management
  • Infrastructure automation
  • Domain-specific pipelines
  • Results Validation
  • Pipeline Parametrization
  • Provenance and automatic dependency resolution

• Other Resources
  • Automated Artifact Evaluation
  • Self-paced Tutorial

• FAQ
  • How can we deal with large datasets? For example I have to work on large data of hundreds GB, how would this be integrated into Popper?
  • How can Popper capture more complex workflows? For example, automatically restarting failed tasks?
  • Can I follow Popper in computational science research, as opposed to computer science?
  • How to apply the Popper protocol for applications that take large quantities of computer time?

• Contributing
  • Code of Conduct
  • Contributing CLI features
  • Contributing example pipelines

Indices and tables

• Index
• Module Index
• Search Page