running-python

Name: running-python
Availability: InStock
Author: yale-som-hpc

Run resumable Python jobs on the Yale SOM HPC cluster via Slurm and uv environments.

5|1|Updated Apr 29, 2026

Search Tags:#logging #python #uv #hpc #resumable #multiprocessing #slurm

Ready to run

npx skills add https://github.com/yale-som-hpc/claude-code-marketplace --skill running-python

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: running-python
Source: https://github.com/yale-som-hpc/claude-code-marketplace/tree/main/plugins/hpc/skills/running-python
Command: npx skills add https://github.com/yale-som-hpc/claude-code-marketplace --skill running-python

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Running Python workloads on the Yale SOM HPC cluster often requires careful environment setup, thread control, thorough logging, and resilient task execution to avoid repeated work.

Core Features & Use Cases

uv project environments: create isolated environments under /gpfs and manage dependencies deterministically.
Safe Slurm templates: generate and run Slurm sbatch scripts with proper thread and library settings.
Resumable tasks: design tasks so partial results can be resumed without recomputation.
Observability: structured logging to trace job progress and failures.
Guidance for debugging: practical patterns for diagnosing common Slurm/Python issues in data analyses.

Quick Start

Initialize a uv project, configure an environment, and submit a resumable Slurm Python job.

Frequently Asked Questions about running-python

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I run Python workloads on an HPC cluster with Slurm safely?▼

You can run Python workloads safely on HPC by generating Slurm sbatch scripts with controlled threading, robust logging, and resumable task execution to avoid repeated work. This approach ensures reliable job submission and clear observability.

How do I configure a uv environment for Python on HPC systems?▼

Configuring a uv environment for Python on HPC involves creating isolated project environments under shared file systems like /gpfs to manage dependencies deterministically. This ensures stable and reproducible execution across cluster nodes.

How do I make Slurm Python jobs resumable after a failure?▼

Making Slurm Python jobs resumable after a failure requires designing tasks so partial results can be resumed without recomputation. This cross-task resume behavior saves compute time and avoids redundant processing during data analysis workflows.

How do I control threading and multiprocessing for Python in Slurm?▼

Controlling threading and multiprocessing for Python in Slurm requires applying safe templates with proper thread and library settings. This manages resource allocation correctly and prevents conflicts during parallel data analysis execution on the cluster.

Does this approach support structured logging for debugging HPC Python jobs?▼

Yes, this approach supports structured logging to trace job progress and failures on HPC. It provides observability and practical guidance for diagnosing common Slurm and Python issues during data analyses, ensuring clear tracking.

Why does my Python Slurm job fail to resume partial results correctly?▼

Your Python Slurm job may fail to resume partial results if tasks are not designed with proper cross-task resume behavior. Implementing resumable task patterns ensures partial computation outputs are saved and recovered without restarting.