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Container Adoption

What Are Containers?​

Containers provide a modern, lightweight, and reproducible way to package software environments. At Jefferson Lab (JLab), containers play a critical role in ensuring consistency across interactive systems, batch jobs, and cloud or Open Science Grid (OSG) resources.

Unlike virtual machines, containers share the host system's kernel, making them more resource-efficient and faster to start. They provide a reproducible execution environment critical for both development and scientific analysis.

This section outlines the key reasons for adopting containers and how they're supported in the JLab ecosystem.

Why Use Containers?​

Containers are a way to bundle software, runtime environments, system libraries, and dependencies into a portable, self-contained unit. They isolate applications from the underlying host operating system, making it easier to share, reproduce, and run software anywhere.

Key benefits:

  • Reproducibility: Run the same code the same way across environments.
  • Portability: Move your analysis from local dev machine to farm nodes or cloud.
  • Isolation: Prevent environment conflicts and simplify testing/debugging.
  • Version Control: Use base images tagged with explicit versions (e.g., alma9.5) to lock environments.

1. Preservation and Reproducibility​

Scientific computing often relies on complex software stacks and curated environments. Containers allow researchers to preserve:

  • Code, including compiled binaries
  • Analysis environments with precise toolchains
  • External dependencies (ROOT, Geant4, Python libraries, etc.)

By containerizing jobs, the environment used for an analysis can be archived and re-run years later with confidence in its reproducibility.

⚠️ While containers are built with reproducibiltiy and code preservation in mind, developers and scientific users must take control by keeping code and data separate while making jobs in continers Easily Configurable. Examples for Python and CERN ROOT are provided in ROOT Scientific Projects.

2. Separation of Code and Data​

Data is typically stored in high-performance or distributed file systems (like Lustre - /volatile/ and /cache/ at JLab), while code and tools are encapsulated in containers. This clean separation allows:

  • Better maintainability
  • Reusability of tools across projects
  • Consistent runtime environments across users and locations

3. Interoperability​

Containers act as interfaces for scientific applications. This abstraction means the same container can be:

  • Submitted to a Slurm batch system on JLab clusters
  • Deployed on cloud infrastructure (OSG)
  • Run locally on a laptop for testing and debugging

This increases the portability and longevity of scientific workflows.