# GPU Handoff Plan Generator

You are helping the user plan a task that requires GPU execution (training/evaluation) but the current Cursor GUI environment does NOT have GPU access.

## Your Job

1. **Understand the request** - What does the user want to run on GPU? (training, evaluation, benchmark, etc.)
2. **Make code changes** - Do all code modifications that can be done without GPU (add wrappers, configs, scripts, etc.)
3. **Generate a Handoff Summary** - Create a structured summary that can be copy-pasted to a GPU node's `cursor-agent` CLI

## Handoff Summary Format (MUST follow exactly)

```markdown
# GPU Handoff Summary

## Context
- **Goal**: [one-line description]
- **Repo root**: /home/shiyil/chengyuew/shiyi/LMFlow
- **Constraints**: [any must-follow rules]

## What I Changed (already merged)
- **Files modified**: [list]
- **New files**: [list]
- **New env vars**: [list with defaults]

## What is NOT Done Yet
- [things that require GPU]

## GPU Execution Checklist

### 0) Environment Setup
```bash
cd /home/shiyil/chengyuew/shiyi/LMFlow
source ~/.bashrc
conda activate <env_name>
```

### 1) Sanity Check (quick, no long run)
```bash
# verify imports work
python -c "import xxx"
```

### 2) Main Commands
```bash
# Command A (baseline)
<exact command>

# Command B (variant) - if A/B test
<exact command>
```

### 3) Expected Outputs
- Results at: `<path>`
- Logs at: `<path>`

## Debugging
- If import error: check PYTHONPATH
- If CUDA OOM: reduce batch size
- Logs location: `<path>`
```

## Rules
- NEVER ask user to run GPU commands in this GUI session
- ALWAYS output the Handoff Summary at the end
- Include absolute paths, not relative
- Include exact conda env name
- Include all env vars with their values
