Rechercher

PythonReactSEOPDF
OpenClaw

rocm_vllm_deployment

Production-ready vLLM deployment on AMD ROCm GPUs. Combines environment auto-check, model parameter detection, Docker Compose deployment, health verification, and functional testing with comprehensive logging and security best practices.

alexhegit0 installations selon la source☆ 0 Étoiles du dépôtv1.0.0

Installation

Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.

SkillHub CLI

skillhub install alexhegit/rocm-vllm-deployment

Examinez la source et la compatibilité, puis suivez les étapes d’installation.

Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.

Voir la source ↗Guides

Instructions

ROCm vLLM Deployment Skill

Production-ready automation for deploying vLLM inference services on AMD ROCm GPUs using Docker Compose.

Features

  • Environment Auto-Check - Detects and repairs missing dependencies
  • Model Parameter Detection - Auto-reads config.json for optimal settings
  • VRAM Estimation - Calculates memory requirements before deployment
  • Secure Token Handling - Never writes tokens to compose files
  • Structured Output - All logs and test results saved per-model
  • Deployment Reports - Human-readable summary for each deployment
  • Health Verification - Automated health checks and functional tests
  • Troubleshooting Guide - Common issues and solutions

Environment Prerequisites

Recommended (for production): Add to ~/.bash_profile:

# HuggingFace authentication token (required for gated models)
export HF_TOKEN="hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"

# Model cache directory (optional)
export HF_HOME="$HOME/models"

# Apply changes
source ~/.bash_profile

Not required for testing: The skill will proceed without these set:

  • HF_TOKEN: Optional — public models work without it; gated models fail at download with clear error
  • HF_HOME: Optional — defaults to /root/.cache/huggingface/hub
Environment Variable Detection

Priority Order:

  1. Explicit parameter (highest) — Provided in task/request (e.g., hf_token: "xxx")
  2. Environment variable — Already set in shell or from parent process
  3. ~/.bash_profile — Source to load variables
  4. Default value (lowest) — HF_HOME defaults to /root/.cache/huggingface/hub
Variable Required If Missing
HF_TOKEN Conditional Continue without token (public models work; gated models fail at download with clear error)
HF_HOME No Warning + Default — Use /root/.cache/huggingface/hub

Philosophy: Fail fast for configuration errors, fail at download time for authentication errors.


#...

Informations sur la source

Source
github
Repository
openclaw/skills
Path
skills/alexhegit/rocm-vllm-deployment
Version
1.0.0
Commit
31efca3a88371dca3734b78ff51a64d61192f7be
Source de classification
rules
Statut
active
Visibilité
public

Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.

Contenu extrait

SKILL.md
# ROCm vLLM Deployment Skill

Production-ready automation for deploying vLLM inference services on AMD ROCm GPUs using Docker Compose.

## Features

- Environment Auto-Check - Detects and repairs missing dependencies
- Model Parameter Detection - Auto-reads config.json for optimal settings
- VRAM Estimation - Calculates memory requirements before deployment
- Secure Token Handling - Never writes tokens to compose files
- **Structured Output** - All logs and test results saved per-model
- **Deployment Reports** - Human-readable summary for each deployment
- Health Verification - Automated health checks and functional tests
- Troubleshooting Guide - Common issues and solutions

## Environment Prerequisites

**Recommended (for production):** Add to `~/.bash_profile`:

```bash
# HuggingFace authentication token (required for gated models)
export HF_TOKEN="hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"

# Model cache directory (optional)
export HF_HOME="$HOME/models"

# Apply changes
source ~/.bash_profile
```

**Not required for testing:** The skill will proceed without these set:
- **HF_TOKEN**: Optional — public models work without it; gated models fail at download with clear error
- **HF_HOME**: Optional — defaults to `/root/.cache/huggingface/hub`

### Environment Variable Detection

**Priority Order:**
1. **Explicit parameter** (highest) — Provided in task/request (e.g., `hf_token: "xxx"`)
2. **Environment variable** — Already set in shell or from parent process
3. **~/.bash_profile** — Source to load variables
4. **Default value** (lowest) — HF_HOME defaults to `/root/.cache/huggingface/hub`

| Variable | Required | If Missing |
|----------|----------|------------|
| `HF_TOKEN` | **Conditional** | Continue without token (public models work; gated models fail at download with clear error) |
| `HF_HOME` | No | **Warning + Default** — Use `/root/.cache/huggingface/hub` |

**Philosophy:** Fail fast for configuration errors, fail at download time for authentication errors.

---

#...

Skills associés

OpenClaw

agent-pack-n-go

aicodelion

🚀 Clone your OpenClaw AI Agent to a new device in ~25 minutes — configs, memory, skills, everything.

OpenClaw

bambu-studio-ai

heyixuan2

Bambu Lab 3D printer control and automation. Activate when user mentions: printer status, 3D printing, slice, analyze model, generate 3D, AMS filament, print monitor, Bambu Lab, or any 3D printing task. Full pi...

OpenClaw

cacheforge-skills

cacheforge-ai

⚡ SOTA agent skills for OpenClaw — observability, security, code quality, incident response, and more. Built by Anvil AI.

OpenClaw

restart-guard

zjianru

OpenClaw Skill: Safely restart the Gateway with context preservation, guardian watchdog, and multi-channel notification