# vast-gpu

Evaluates training tasks, rents cost-effective GPU instances on vast.ai, and manages their lifecycle. Handy when you need cloud compute without local hardware.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 453 installs · ★ 18090
- Author: wanshuiyin
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/HEAD/skills/vast-gpu/SKILL.md
- Updated: 2026-10-24
- HTML: https://affpapa.org/en/nevibecode/skills/vast-gpu

## What it does

The skill assesses machine learning tasks so you do not have to configure hardware by hand. It inspects `refine-logs/EXPERIMENT_PLAN.md` or training scripts. Based on model parameter counts and batch sizes, the tool computes required VRAM with a 20% safety margin and sets disk allocation to a 50 GB default. Then it queries vast.ai offers and shows three options ranked by total estimated cost instead of hourly rates alone.

Instances are provisioned via `vastai create instance` using a PyTorch Docker image, with active machines logged in `vast-instances.json`. You must upload your SSH public key to vast.ai before provisioning: keys get baked in during creation, so missing keys force an immediate teardown and restart. The setup requires Python 3.10 or newer and an authenticated API key in the `vastai` CLI tool.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill vast-gpu -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/vast-gpu/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/vast-gpu/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill vast-gpu -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/vast-gpu/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill vast-gpu -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/vast-gpu/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install wanshuiyin/Auto-claude-code-research-in-sleep vast-gpu
```

2. **Manually: put the files in place**
- File: `.github/skills/vast-gpu/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill vast-gpu -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep --path skills/vast-gpu
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/vast-gpu/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill vast-gpu -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/HEAD/skills/vast-gpu/SKILL.md

## When to use it
- calculate required GPU VRAM and disk space from training scripts
- search and rank vast.ai instances by total estimated cost
- provision GPU instances with preconfigured PyTorch environments
- track running server details in `vast-instances.json`

## Example prompts
- Rent a GPU on vast.ai to fine-tune LLaMA-7B for about 2 hours
- Find the cheapest server with 24 GB VRAM for running the baseline experiment
- Pick an instance based on refine-logs/EXPERIMENT_PLAN.md

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: f008d16a6bd28b23.

BEGIN THIRD-PARTY FILE f008d16a6bd28b23
````text
---
name: vast-gpu
description: "Rent, manage, and destroy GPU instances on vast.ai. Use when user says \"rent gpu\", \"vast.ai\", \"rent a server\", \"cloud gpu\", or needs on-demand GPU without owning hardware."
argument-hint: "[task-description or action]"
allowed-tools: Bash(*), Read, Write, Edit, Grep, Glob
---

# Vast.ai GPU Management

Manage vast.ai GPU instance: $ARGUMENTS

## Overview

Rent cheap, capable GPUs from vast.ai on demand. This skill **analyzes the training task** to determine GPU requirements, searches for the best-value offers, presents options with estimated total cost, and handles the full lifecycle: rent → setup → run → destroy.

Users do NOT specify GPU models or hardware. They describe the task — the skill figures out what to rent.

**Prerequisites:** The `vastai` CLI must be installed (requires **Python ≥ 3.10**) and authenticated:
```bash
pip install vastai
vastai set api-key YOUR_API_KEY
```

> If your system Python is < 3.10, create a virtual environment with Python ≥ 3.10 (e.g., `conda create`, `pyenv`, `uv venv`, etc.) and install `vastai` there.

SSH public key **must be uploaded at https://cloud.vast.ai/manage-keys/ BEFORE creating any instance**. Keys are baked into instances at creation time — if you add a key after renting, you must destroy and re-create the instance.

## State File

All active vast.ai instances are tracked in `vast-instances.json` at the project root:
```json
[
  {
    "instance_id": 33799165,
    "offer_id": 25831376,
    "gpu_name": "RTX_3060",
    "num_gpus": 1,
    "dph": 0.0414,
    "ssh_url": "ssh://root@1.208.108.242:58955",
    "ssh_host": "1.208.108.242",
    "ssh_port": 58955,
    "created_at": "2026-03-29T21:12:00Z",
    "status": "running",
    "experiment": "exp01_baseline",
    "estimated_hours": 4.0,
    "estimated_cost": 0.17
  }
]
```

This file is the source of truth for `/run-experiment` and `/monitor-experiment` to connect to vast.ai instances.

## Workflow

### Action: Provision (default)

Analyze the task, find the best GPU, and present cost-optimized options. This is the main entry point — called directly or automatically by `/run-experiment` when `gpu: vast` is set.

**Step 1: Analyze Task Requirements**

Read available context to determine what the task needs:

1. **From the experiment plan** (`refine-logs/EXPERIMENT_PLAN.md`):
   - Compute budget (total GPU-hours)
   - Hardware hints (e.g., "4x RTX 3090")
   - Model architecture and dataset size
   - Run order and per-milestone cost estimates

2. **From experiment scripts** (if already written):
   - Model size — scan for model class, `num_parameters`, config files
   - Batch size, sequence length — estimate VRAM from these
   - Dataset — estimate training time from dataset size + epochs
````
END THIRD-PARTY FILE f008d16a6bd28b23

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