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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 source installs☆ 0 Repository starsv1.0.0

Installation

Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.

SkillHub CLI

skillhub install alexhegit/rocm-vllm-deployment

Inspect the source and compatibility requirements, then follow its installation steps.

Give your agent the source link and ask it to inspect instructions and dependencies before explaining the installation steps.

View source ↗Guides

Skill 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.


#...

Source information

Source
github
Repository
openclaw/skills
Path
skills/alexhegit/rocm-vllm-deployment
Version
1.0.0
Commit
31efca3a88371dca3734b78ff51a64d61192f7be
Classification source
rules
Status
active
Visibility
public

These signals come from indexed source analysis and may lag behind changes. Check the current source files.

Extracted content

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.

---

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