Radeon-Cloud

SKILL运维和基础设施
运维和基础设施 未标注权限 许可证不明确
安装这个技能

Operate the AMD Radeon Cloud GPU workstation AMD provides to you for free - one remote Ubuntu box with a 48 GB VRAM gfx1100 GPU, reached through the `radeon-cloud` SSH alias configured on this machine. Prerequisite: register for AMD Radeon Cloud (claim the free GPU time) and set up the ssh alias with the official setup guide before first use. Diagnose and self-heal SSH connection and rotated host keys, inspect ROCm and GPU status via rocm-smi, discover which Python venvs carry torch, run commands remotely, sync code and results between your machine and the box, and manage detached long-running jobs with logs. Start with `rc guide`. Triggers: radeon-cloud, Radeon cloud, Radeon 云, ROCm 远程, 远程 GPU, rocm-smi, gfx1100, 上传到 radeon, 下载结果, 跑训练, 后台任务, GPU 显存.

一句话选型结论

按资料字段整理

Radeon-Cloud是运维和基础设施类的 Agent Skill,安装方式未标注,权限级别未标注,许可证不明确,有可直接复制的安装命令。技能本质是给模型读的说明书,效果取决于模型与客户端;装完先用一个小任务验证,再放进正式流程。

分类运维和基础设施 安装方式未标注 权限级别未标注权限 许可证许可证不明确
累计安装 未标注 本周 未标注
GitHub 星标 未标注 仓库 未标注
下载量 未标注 技能市场下载数
权限级别 未标注权限 安装前请确认风险范围
许可证 许可证不明确 未说明

安装

未标注
在支持 Skills 的客户端里执行,装完问一句「你有哪些技能」验证
skillhub install user_f2b58ed4/radeon-cloud
  1. 1
    确认客户端支持

    确认你的客户端支持 Skills(Claude、Codex、Cursor 等),并升级到较新的版本。

  2. 2
    拿到技能文件

    执行上面的命令安装;也可以把仓库里的 SKILL.md 手动放进客户端的 skills 目录。

  3. 3
    刷新技能列表

    重新打开客户端,或刷新技能列表,让新放进来的技能被加载。

  4. 4
    验证是否生效

    问一句「你有哪些技能」,确认列表里出现它,再拿一个小任务试跑。

技能档案

运维和基础设施
字段来自技能仓库与公开索引,中文说明由本站标注
作者 / 维护方user_f2b58ed4仓库 未标注 · 版本 未标注
宿主客户端未标注技能可被这些客户端加载
安装方式未标注安装包 community
权限级别未标注权限安装前请确认风险范围
许可证未说明许可证不明确,商用前请自行确认条款
仓库可用性available技能仓库当前是否可访问
最近核验未标注上游最后核验时间 · 数据更新 1791621824
正文体量13.9 KBSKILL.md 原文大小,越大说明说明书写得越细

典型使用场景

3 类
按分类整理的常见用法,具体以 SKILL.md 为准
查服务与日志

按现象定位服务状态与日志线索。

写部署与巡检脚本

把重复的部署、巡检写成脚本。

故障排查

按步骤排查网络、依赖与配置。

选择建议

装之前先看这几条
按条目字段给出的判断,不替你决定用不用

值得选的地方

  • 有可直接复制的安装命令,不用自己翻文档找入口。

先想清楚的地方

  • 没有标注权限级别,实际能做什么要装好后确认。
  • 许可证不明确,商用或二次分发前先到仓库确认条款。
  • 没有做站内核验,内容以作者仓库为准。
  • 技能只是说明书,实际效果取决于模型与客户端,装完先用小任务试一遍。

更适合

  • 需要在运维和基础设施场景里给模型补一套做法的人。
  • 愿意先读一遍 SKILL.md、确认它会让模型做什么的人。

不太适合

  • 对许可证要求很严、必须明确可商用的人。
  • 只想装完就自动变强、不打算看说明书的人。

同类对比

6 个同类
同分类里挑最接近的几条,字段以各自详情页为准
技能权限级别安装方式累计安装宿主客户端安装命令
Radeon-Cloud当前 未标注权限 未标注 未标注 未标注 有
Azure 部署 调用外部服务 GitHub 仓库 664.2 万 Codex、Claude、Cursor 有
Azure diagnostics 问题管理 调用外部服务 GitHub 仓库 657.6 万 Codex、Claude、Cursor 有
Azure aigateway 部署 调用外部服务 GitHub 仓库 655.3 万 Codex、Claude、Cursor 有
Azure AI 搜索 调用外部服务 GitHub 仓库 651.4 万 Codex、Claude、Cursor 有
Azure Cloud migrate 部署 调用外部服务 GitHub 仓库 650.7 万 Codex、Claude、Cursor 有

SKILL.md 全文

13.9 KB
模型实际读到的说明书原文,来自技能仓库

Radeon Cloud Connector

> Security model and credential handling are documented in
> SECURITY.md — the skill never touches your SSH credential
> file, only ever targets your configured radeon-cloud alias, and audits
> every remote command.

Overview

radeon-cloud is a free-of-charge AMD Radeon Cloud instance: an Ubuntu 24.04 container with one AMD Navi 31 GPU (gfx1100, about 48 GB VRAM) and ROCm installed. This skill wraps it behind a single CLI, scripts/rc.py, which exists because five things on this box repeatedly go wrong and each one costs real time when handled by hand: the SSH host key rotates whenever the instance is re-imaged, /workspace/env.sh puts a venv without torch first on PATH, the container overlay silently discards anything written outside /workspace, long jobs die when the local terminal goes away, and a broken endpoint otherwise looks like a working one.

Prefer the CLI over hand-rolled ssh one-liners. It sources the environment, applies the persistence guard, handles host-key rotation and tracks job state.

Before you start (prerequisite)

This skill operates an existing machine - it cannot register one for you. Before first use, complete AMD Radeon Cloud onboarding with the official setup guide:

在 Windows 或 MacBook 上连接 Radeon Cloud

The guide walks you through:

  1. 注册 AMD Radeon Cloud 并领取免费 GPU 时长(register and claim the free GPU hours)- the workstation costs nothing to use, but you must have an account with claimed time before anything here can connect;
  2. 配置 radeon-cloud ssh 别名(configure the radeon-cloud SSH alias with host, port, user and key - the guide shows the exact block)- every command in this skill targets that alias and nothing else.

If any rc command reports exit code 2 (connection problem), come back to this guide - it is almost always a missing or stale alias.

Invocation

PY is any Python 3.10+ interpreter (PEP 604 union syntax); RC is this skill's scripts/rc.py. Use forward slashes on Windows.

PY="${HOME}/.workbuddy-ai/binaries/python/versions/3.13.12/python.exe"   # managed interpreter
PY="${PY:-python3}"                                                      # fallback
RC="<skill-dir>/scripts/rc.py"

"$PY" "$RC" doctor
"$PY" "$RC" status --torch

Every subcommand accepts -y/--yes to skip confirmation prompts. Interactive use prompts once before connecting; scripted use (CI, another agent, a pipe) is refused unless you pass --yes for that command, or enable unattended execution with RC_ALLOW_UNATTENDED=1 or allow_unattended: true in config.yaml. --host <alias> is a whitelist, not a general override: it only accepts the already-configured radeon-cloud alias and rejects every other value with exit 2. That is deliberate — it stops the skill from being used as a proxy to point commands at someone else's machine.

The remote contract you must respect

/workspace is the only persistent volume. Anything written to /, /tmp, /dev/shm or /run is destroyed the next time the image is rebuilt, and this instance has already been rebuilt at least once. The CLI enforces this at three levels: push/pull check their destination, exec and run check --cwd, and exec/run also scan the command string itself for paths under write-prone ephemeral zones (/tmp, /var/tmp, /dev/shm, /run, /root, /home, /mnt, /media, /srv) — a hit is refused with the same escape hatch. The command-string scan is a denylist on purpose: reads of system paths (/etc, /usr, /proc) and toolchain locations (/opt) are not blocked. All three refusals are waived by --allow-ephemeral.

/workspace/env.sh sets PATH, HF_HOME and HSA_OVERRIDE_GFX_VERSION, and the CLI sources it before every command. It is shared with the user's other projects and they edit it, so treat it as a moving target rather than a fixed fact: as of 2026-09-01 it points PATH at /workspace/venv, which carries the standard stack (torch 2.12.0+rocm7.14.0). Earlier it pointed at a venv with no torch at all. Run rc env to see the current truth.

The CLI defends against both states: when env.sh's default venv cannot import torch, exec and run automatically prepend a torch-capable venv and say so on stdout. rc exec -- python -c "import torch" therefore works with no extra flags in either case. Pass --venv <path> to choose one explicitly, or --no-auto-venv to disable the behaviour. The probe is cached in ~/.radeon-cloud/venv-cache.json: a successful probe for six hours, a failed one for only five minutes so a transient timeout cannot disable auto-fix for a whole working session. rc doctor, rc env and rc status --torch refresh it.

Which venv is correct changes whenever the user rebuilds environments, and stale venvs get deleted outright during disk cleanups. Never copy a venv path out of this document into a command without checking rc env first; the candidate list is discovery-only and any named path may disappear after a rebuild or cleanup.

Disk is the recurring failure mode on this box. /workspace reached 97% used (3.3 GiB free) in early September 2026 and had to be cleaned back to 50% before real work could continue; it fills up again quickly. Check rc status before pushing anything large, and prefer leaving model weights in the HuggingFace cache at /root/.cache/huggingface, which is a separate persistent host mount.

Commands

Command Purpose
guide Print the zero-to-first-result sequence, checked live against your current state. Start here on a cold machine.
doctor Layered check of ssh config, credential, TCP reachability, auth, workspace, venv, load and GPU. Detects a rotated host key and offers a backed-up repair.
status [--torch] Live GPU summary (model, gfx version, temp, power, VRAM used/total), plus disk, memory, load (annotated when it indicates D-state buildup or saturation) and the torch inventory of every candidate venv. Add --raw to print the full rocm-smi dump instead of the distilled summary.
exec -- <cmd> Run a command remotely, defaulting to /workspace, sourcing env.sh, with --cwd, --timeout, --venv, --no-auto-venv, --stream, --dry-run.
push <local> <remote> Upload a directory over tar+ssh (there is no local rsync). --exclude is repeatable.
pull <remote> <local> Download a remote directory. Refuses to clobber an existing local path unless --overwrite.
run --name <slug> -- <cmd> Start a detached job via setsid+nohup, recording pid and log under /workspace/.rc-jobs.
jobs List tracked jobs with a live/exited state derived from the remote pid.
logs <job-id> [-f] [-n N] Show or follow a job log.
stop <job-id> [--force] Terminate a job: SIGTERM by default, SIGKILL with --force. Idempotent on a job that already exited.
env Validate the whole remote contract, including the env.sh PATH cross-check.
config [--show|--set K=V|--reset] Local connector configuration, stored in ~/.radeon-cloud/config.json.

Exit codes: 0 success, 1 a real failure, 2 the remote host could not be reached or authenticated. A command that returns 2 always prints one actionable next step. Two documented exceptions: exec and run return the remote command's own exit code — a script that dies with exit 3 makes rc exec return 3; only 2 stays reserved for connection problems, so test with rc exec ...; [ $? -ne 0 ] rather than -eq 1. And doctor against an unreachable or unauthenticated host exits 2 (the connection case), not 1.

Workflows

On a cold machine, run rc guide first; it tells you which step you are actually on rather than assuming a working setup. Otherwise check the box before starting anything: rc doctor, then rc status.

If ssh radeon-cloud fails outright (before rc can run at all), complete the connection setup first: 在 Windows 或 MacBook 上连接 Radeon Cloud.

Send code up and run it:

"$PY" "$RC" push ./myproject /workspace/myproject --exclude "*.log" --exclude "__pycache__"
"$PY" "$RC" run --name train --cwd /workspace/myproject -- python train.py
"$PY" "$RC" jobs
"$PY" "$RC" logs <job-id> -f

Collect results afterwards with rc pull /workspace/myproject/outputs ./outputs.

Safety rails

The CLI defaults to the persistent volume and requires --allow-ephemeral to write anywhere else; the refusal message names the path and the flag. Host-key repair backs up known_hosts first, removes only the entries for the exact target host and port (sibling containers on the same IP are untouched), prints the new fingerprints and asks before trusting them. stop always confirms unless -y is given. push and pull support --dry-run, and pull refuses to overwrite an existing local directory without --overwrite.

Every command that needs the remote host fails fast with one actionable message instead of a raw ssh error or, worse, an empty success. An unknown ssh alias, a dead endpoint and a refused key are each reported in plain language with the exact thing to run next. A failing remote command is never dressed up as a connection problem: a ModuleNotFoundError stays a ModuleNotFoundError.

Do not "fix" /workspace/env.sh on the remote host on your own initiative. As of 2026-09-01 its PATH entry is correct for torch work (/workspace/venv carries torch 2.12.0+rocm7.14.0), but it has pointed at a venv with no torch before, and the user edits it for their other projects. Check with rc env before relying on it; if the head of PATH lacks torch, report the mismatch and let the user decide, or use --venv per command instead.

The costliest trap: HIP init can hang forever

On this box rocminfo can wedge in uninterruptible sleep (D state) and never return. Every call that performs HIP initialisation shells out to it, so these calls hang the same way:

Call Behaviour when rocminfo is wedged
rocminfo (any form) hangs
torch.cuda.device_count() hangs
torch.cuda.is_available() hangs
rocm-smi fine, returns instantly
import torch / torch.version.hip fine

Rules that follow from this:

  • Never probe GPU state with torch.cuda.is_available() / torch.cuda.device_count(). Use import torch + torch.version.hip to check torch, and rc status (which reads rocm-smi) to check the GPU.
  • When you add a remote probe to this skill, always bound it on the remote side as well as locally (timeout -s KILL <n> <cmd>). A local subprocess timeout kills the local ssh client and orphans the remote process — that is exactly how one bug accumulated 100+ leaked processes and pushed loadavg past 90 in half a day.
  • Failed probes must not be cached like successful ones. The venv probe result is cached for six hours when it succeeds, but a failed probe expires after five minutes so one transient timeout cannot silently disable torch auto-fix for a whole working session.

If commands start hanging and load is inexplicably high, check for leaked probes: ssh radeon-cloud "ps -eo pid,etimes,stat,args | grep -E 'device_count|is_available' | grep -v grep". D-state processes survive even SIGKILL; only a container restart clears them.

On Windows/Git Bash, $(pwd) produces MSYS-style paths (/c/Users/...). rc push and rc pull translate these via _native_path(); hand them to nothing else without converting. Git Bash virtual mounts (/tmp, /usr, ...) do NOT map to Windows paths — push/pull reject them up front and say so; use the /c/... drive-letter form instead.

Self-verification

After any change to rc.py or to this file, confirm the connector still
diagnoses rather than guesses:

  1. A bare rc doctor on a machine where the alias is absent must print one
    actionable message (exactly once) and exit 2.
  2. rc doctor on a configured machine must reach the GPU check.
  3. No remote probe may rely on a local timeout alone; every probe carries a
    remote timeout -s KILL bound.
  4. A failed venv probe must read as "unknown", never as "no torch" — in
    doctor (warn, not fail), status --torch (warn, exit governed by the
    GPU probe), env (explicit warn line) and guide (distinct message).
  5. rc env must never print a green OK for a venv it did not actually
    verify; when the probe fails, the resolved-environment section says so
    instead of silently disappearing.
  6. The command-string path scan must refuse ephemeral-zone paths
    (touch /tmp/x) in exec and run before any connection is made,
    must never block reads of system paths (/etc, /usr, /proc), and
    must be waived by --allow-ephemeral. Load reporting must annotate
    (warn) rather than fail: loadavg 103 with 8 running tasks reads as
    D-state buildup, not as a hard error.

References

  • references/environment.md — full machine profile, storage persistence rules, venv inventory, ROCm version strategy.
  • references/troubleshooting.md — known failures and their fixes, including host-key rotation and the no-torch venv.

常见问题

5 问
答案由本站依据条目字段整理
Radeon-Cloud怎么安装?

在支持 Skills 的客户端里执行:skillhub install user_f2b58ed4/radeon-cloud。安装方式为未标注。装完刷新技能列表或重开客户端。

Radeon-Cloud支持哪些客户端?

资料没有标注宿主客户端,但只要客户端支持 Skills(Claude、Codex、Cursor 等),一般都能加载。

Radeon-Cloud权限高吗,安全吗?

权限级别为未标注权限。安装前先看它会读取或写入哪些内容。

Radeon-Cloud收费吗?

本站不代收费用,也没有标注具体价格。是否收费取决于技能本身和所用的模型或客户端,正式使用前按作者说明确认。

Radeon-Cloud用什么许可证?

许可证标注为 未说明(许可证不明确)。商用或二次分发前请到仓库确认条款原文。

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技能内容以作者仓库为准;分类、权限级别与许可证状态的中文说明为中文整理,最近核验 未标注。