agent-pack-n-go
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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.
请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求,再添加到工作流。
SkillHub CLI
skillhub install alexhegit/rocm-vllm-deployment打开来源仓库,查看说明和兼容环境后,按来源提供的命令或步骤安装。
将来源链接提供给你的 Agent,让它先读取说明、核对依赖,并向你说明将执行的安装步骤。
Production-ready automation for deploying vLLM inference services on AMD ROCm GPUs using Docker Compose.
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="$HOME/models"
# Apply changes
source ~/.bash_profile
Not required for testing: The skill will proceed without these set:
/root/.cache/huggingface/hub
Priority Order:
hf_token: "xxx")/root/.cache/huggingface/hub
| Variable | Required | If Missing |
|---|---|---|
HF_TOKEN |
Conditional | Continue without token (public models work; gated models fail at download with clear error) |
HF_HOME |
No | Warning + Default — Use /root/.cache/huggingface/hub |
Philosophy: Fail fast for configuration errors, fail at download time for authentication errors.
#...
这些信息来自收录时的来源分析,可能滞后;使用前请核对最新来源文件。
# ROCm vLLM Deployment Skill Production-ready automation for deploying vLLM inference services on AMD ROCm GPUs using Docker Compose. ## Features - Environment Auto-Check - Detects and repairs missing dependencies - Model Parameter Detection - Auto-reads config.json for optimal settings - VRAM Estimation - Calculates memory requirements before deployment - Secure Token Handling - Never writes tokens to compose files - **Structured Output** - All logs and test results saved per-model - **Deployment Reports** - Human-readable summary for each deployment - Health Verification - Automated health checks and functional tests - Troubleshooting Guide - Common issues and solutions ## Environment Prerequisites **Recommended (for production):** Add to `~/.bash_profile`: ```bash # HuggingFace authentication token (required for gated models) export HF_TOKEN="hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" # Model cache directory (optional) export HF_HOME="$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. --- #...
🚀 Clone your OpenClaw AI Agent to a new device in ~25 minutes — configs, memory, skills, everything.
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