7024a86024
- Remove all manually registered Pi tools (alpha_search, alpha_get_paper, alpha_ask_paper, alpha_annotate_paper, alpha_list_annotations, alpha_read_code, session_search, preview_file) and their wrappers (alpha.ts, preview.ts, session-search.ts, alpha-tools.test.ts) - Add Pi skill files for alpha-research, session-search, preview, modal-compute, and runpod-compute in skills/ - Sync skills to ~/.feynman/agent/skills/ on startup via syncBundledAssets - Add node_modules/.bin to Pi subprocess PATH so alpha CLI is accessible - Add /outputs extension command to browse research artifacts via dialog - Add Modal and RunPod as execution environments in /replicate and /autoresearch prompts - Remove redundant /alpha-login /alpha-logout /alpha-status REPL commands (feynman alpha CLI still works) - Update README, researcher agent, metadata, and website docs Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
57 lines
1.4 KiB
Markdown
57 lines
1.4 KiB
Markdown
---
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name: modal-compute
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description: Run GPU workloads on Modal's serverless infrastructure. Use when the user needs remote GPU compute for training, inference, benchmarks, or batch processing and Modal CLI is available.
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---
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# Modal Compute
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Use the `modal` CLI for serverless GPU workloads. No pod lifecycle to manage — write a decorated Python script and run it.
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## Setup
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```bash
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pip install modal
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modal setup
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```
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## Commands
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| Command | Description |
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|---------|-------------|
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| `modal run script.py` | Run a script on Modal (ephemeral) |
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| `modal run --detach script.py` | Run detached (background) |
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| `modal deploy script.py` | Deploy persistently |
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| `modal serve script.py` | Serve with hot-reload (dev) |
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| `modal shell --gpu a100` | Interactive shell with GPU |
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| `modal app list` | List deployed apps |
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## GPU types
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`T4`, `L4`, `A10G`, `L40S`, `A100`, `A100-80GB`, `H100`, `H200`, `B200`
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Multi-GPU: `"H100:4"` for 4x H100s.
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## Script pattern
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```python
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import modal
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app = modal.App("experiment")
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image = modal.Image.debian_slim(python_version="3.11").pip_install("torch==2.8.0")
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@app.function(gpu="A100", image=image, timeout=600)
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def train():
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import torch
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# training code here
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@app.local_entrypoint()
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def main():
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train.remote()
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```
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## When to use
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- Stateless burst GPU jobs (training, inference, benchmarks)
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- No persistent state needed between runs
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- Check availability: `command -v modal`
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