Diagnose Ray distributed training hang issues by analyzing cluster status and blocking patterns.
npx skills add https://github.com/EnkeZ/tgsopd --skill debug-hang-enkez
Please help me install this Agent Skill. Skill: debug-hang Source: https://github.com/EnkeZ/tgsopd/tree/main/skills/debug-hang Command: npx skills add https://github.com/EnkeZ/tgsopd --skill debug-hang-enkez
This Skill automates the diagnosis of Ray distributed training hang issues, providing a streamlined process to identify and resolve problems with Ray scheduling tasks.
Run the diagnosis script to automatically troubleshoot Ray distributed training hang issues.
High-intent search queries and answers about installing and using this skill.
Diagnosing Ray distributed training hang issues involves analyzing cluster status, task blocking, call stacks, and actor dependencies. An automated diagnosis script matches blocking patterns to determine the root cause of the hang.
Tasks block and hang during Ray distributed training often due to actor dependency deadlocks or resource exhaustion. Analyzing call stacks and matching blocking patterns pinpoints the exact root cause.
Yes, you need Ray and Python scripts installed to troubleshoot cluster hang problems. The diagnosis process relies on these dependencies to collect call stacks and analyze actor dependencies.
Yes, you can analyze actor dependencies to resolve Ray scheduling hangs. The diagnosis process includes actor dependency analysis and blocking pattern matching to locate the tasks causing the hang.
The best way to troubleshoot Ray hangs in large-scale distributed training environments is using an automated diagnosis script that collects call stacks, evaluates cluster health, and matches blocking patterns.
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