AugmentClaude
MLRendering & MaterialsDevOps

GPU Rental

By davidondrej· davidondrej/cloudroom· 1

Rent a GPU box for training models, running inference, or 3D rendering work.

Installation

  1. Make sure Claude is on your device and in your terminal.

    Skills load from ~/.claude/skills/ when Claude Code starts up — so you need it on your machine first. If you don't have it yet, install it once with the command below, then run claude in any terminal to verify.

    One-time setup
    npm i -g @anthropic-ai/claude-code

    Already have it? Skip ahead.

  2. Paste into Claude Code or into your terminal.
    Install
    Sign up to copy
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    This copies the whole skill folder into ~/.claude/skills/gpu-davidondrej/ — the SKILL.md plus any scripts, reference docs, or templates the skill ships with. Safe default: works for every skill.

    Faster alternative (instruction-only skills)

    Skips the clone and grabs only the SKILL.md file. Don't use this if the skill ships Python scripts, reference markdowns, or asset templates — they won't be downloaded and the skill will fail when it tries to load them.

    Quick install (SKILL.md only)
    Sign up to copy
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  3. Restart Claude Code.

    Quit and reopen Claude Code (or any other agent that loads from ~/.claude/skills/). New skills are picked up on startup.

  4. Just ask Claude.

    Skills auto-activate when your request matches the skill's description — no slash command needed. Trigger phrases live in the skill's own frontmatter; you can read them in the “What this skill does” section above.

Prefer to read the source first? Open on GitHub.

When Claude uses it

Rent a GPU box with `room-cli gpu` for heavy GPU work, such as training or fine-tuning models, running large models, CUDA code, or 3D rendering. Each session needs the user's approval and uses their Cloud Credits. Beta, paid plans (Pro and up), Local and Cloud threads.

What this skill does

GPUs

room-cli gpu rents a separate Linux box with NVIDIA GPUs. Your own machine stays where it is; you run commands on the GPU box and copy files to and from it.

When to use it

  • Only for work that truly needs a GPU, or would take far too long on the CPU. GPUs are expensive.
  • Suggest it to the user first, with what you want to run and roughly how long. Then start it: the user approves in a card that shows the Cloud Credits.

Workflow

  1. room-cli gpu list shows GPU types, Cloud Credits per hour, and how many are free now.
  2. room-cli gpu start --gpu H100 --hours 1 --purpose "Fine-tune the classifier on data/" asks the user and waits. If it says it's still waiting, tell the user and run room-cli gpu wait ID. Don't end your turn while waiting.
  3. room-cli gpu push ID ./data copies a file or folder to work/ in the box's working folder (/workspace).
  4. room-cli gpu run ID --cwd work -- python train.py runs a command and prints its output. Each command starts fresh in /workspace, so use --cwd or cd DIR && …. Install what you need with pip or apt-get; the default image has PyTorch with CUDA.
  5. For long jobs, add --detach, then check with room-cli gpu logs ID COMMAND_ID --follow.
  6. room-cli gpu pull ID work/out ./results copies results back.
  7. room-cli gpu stop ID as soon as the GPU work is done. It's charged by the minute until then.

Limits

  • One session per thread. It stops by itself at its hours or spending limit, or after 15 minutes with no command running. Its files are deleted when it stops, so pull results first.
  • --count up to 4 GPUs, --hours up to 8. --cpu, --memory, --disk, and --image change the box.
  • The box may only reach package registries (PyPI, apt, GitHub), not the whole internet.
  • room-cli gpu help lists every command.

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