Environments & Deployment

Use this section to run py3plex from the command line, inside Docker containers, and at scale—while keeping runs reproducible.

This section covers:

• Docker Usage Guide — Command-line interface and containerized deployment

• Performance and Scalability Best Practices — Memory management, optimization, large networks

When to Use This Section

Use these chapters when you want to:

• Automate analyses that you currently run interactively

• Process networks too large for a single Python session

• Share reproducible environments with collaborators or CI

• Integrate py3plex into a production data pipeline

CLI gives you a scriptable interface for common operations—no Python coding required and suitable for headless automation. Start here if you already know the operations you want to run.

Docker keeps environments identical across machines, avoiding “works on my machine” issues. Use it when you need the same environment on laptops, CI, and servers.

The performance chapter covers memory management and optimization for large networks once you have a working workflow. It is most useful after you have a repeatable pipeline and want to speed it up or shrink memory use.

Most readers start with Docker Usage Guide to script or containerize workflows, then move to Performance and Scalability Best Practices to tune runtime and memory as datasets grow.

Tip

Deployment checklist:

• Pin dependency versions (requirements.txt or lock file) and set a random seed for reproducibility

• Handle missing files, empty inputs, and unexpected formats explicitly

• Validate results on a small test network before scaling up

• Enable logging for reproducibility and debugging; keep logs with your outputs

• Run a dry run on the target environment (local, Docker, or cluster) before scheduling large jobs