Does this support Linux-based HEC-RAS execution?

Yes, through the Docker worker type, you can run HEC-RAS in containers, typically utilizing Windows for preprocessing and Linux-based container environments for the simulation engine.

Why does this skill require a session ID for PsExec execution?

HEC-RAS is a GUI-based application that requires an active desktop session to initialize properly; running it in a headless system account (Session 0) will cause the simulation to hang.

Can I mix local and remote workers in a single simulation run?

Yes, the skill supports mixed worker pools and uses queue-aware scheduling to fill high-priority slots (like local cores) before offloading to remote or cloud workers.

What are the core requirements for setting up a remote Windows worker?

Remote workers require a configured network share, specific Group Policy settings for network logon, the LocalAccountTokenFilterPolicy registry key, and an active Remote Registry service.

Distributed HEC-RAS Computing

Name: Distributed HEC-RAS Computing
Author: gpt-cmdr

bygpt-cmdr

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Data Science & ML

Scales HEC-RAS hydraulic simulations across remote Windows machines, Docker containers, and cloud environments using automated distributed execution.

This skill enables high-performance hydraulic modeling by orchestrating HEC-RAS 6.x plan executions across a network of distributed workers. It abstracts the complexity of remote process management, handling session-based Windows execution via PsExec, containerized workflows with Docker, and cloud-based scaling. By implementing queue-aware scheduling and priority-based worker allocation, it allows engineers to maximize hardware utilization and significantly reduce simulation times for large-scale watershed or flood studies while ensuring result aggregation and HDF5 integrity.

Key Features

0143 GitHub stars

02Multi-worker orchestration supporting PsExec, Docker, SSH, and Local execution

03Session-aware execution logic to satisfy HEC-RAS GUI desktop requirements

04Lazy-loading architecture for optional cloud and container dependencies

05Queue-based priority scheduling for optimized hardware and license usage

06Automated result aggregation and HDF output verification across network shares

Use Cases

01Running massive Monte Carlo simulations or climate change scenarios across an office workstation cluster

02Automating cloud-based flood modeling using Docker containers on remote servers

03Offloading heavy hydraulic computations from a local workstation to dedicated high-performance remote servers

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add gpt-cmdr/ras-commander hecras_compute_remote

For use in Claude.ai and ChatGPT

Download Skill