Modal is a serverless cloud platform for running Python on demand, including on-demand GPUs. Use when deploying or serving AI/ML models, running GPU-accelerated workloads (training, fine-tuning, inference), serving web endpoints, scheduling batch jobs, or scaling Python code to
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Modal is a serverless cloud platform for running Python on demand, including on-demand GPUs. Use when deploying or serving AI/ML models, running GPU-accelerated workloads (training, fine-tuning, inference), serving web endpoints, scheduling batch jobs, or scaling Python code to
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LicenseMIT
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</> SKILL.md
modal.SKILL.md
---name: modal
description: Modal is a serverless cloud platform for running Python on demand, including on-demand GPUs. Use when deploying or serving AI/ML models, running GPU-accelerated workloads (training, fine-tuning, inference), serving web endpoints, scheduling batch jobs, or scaling Python code to cloud containers with the Modal SDK.
license: Apache-2.0
required_environment_variables: [{"name": "MODAL_TOKEN_ID", "prompt": "Modal token id.", "required_for": "full functionality"}, {"name": "MODAL_TOKEN_SECRET", "prompt": "Modal token secret.", "required_for": "full functionality"}, {"name": "DATABASE_URL", "prompt": "Optional database URL for examples.", "required_for": "optional features"}]
metadata: {"version": "1.2", "skill-author": "K-Dense Inc.", "openclaw": {"envVars": [{"name": "MODAL_TOKEN_ID", "required": true, "description": "Modal token id."}, {"name": "MODAL_TOKEN_SECRET", "required": true, "description": "Modal token secret."}, {"name": "DATABASE_URL", "required": false, "description": "Optional database URL for examples."}]}}
---# Modal
## Overview
Modal is a cloud platform for running Python code serverlessly, with a focus on AI/ML workloads. Key capabilities:
- **GPU compute** on demand (T4, L4, A10, L40S, A100, H100, H200, B200)
- **Serverless functions** with autoscaling from zero to thousands of containers
- **Custom container images** built entirely in Python code
- **Persistent storage** via Volumes for model weights and datasets
- **Web endpoints** for serving models and APIs
fast = Model.with_options(gpu="H200", max_containers=20)
fast().generate.remote(prompt)
```
**Reference**: See `references/scaling.md` for `.map()`, `.starmap()`, `.spawn()`, and limits.
### Resource Configuration
```python
@app.function(
cpu=4.0, # Physical cores (not vCPUs)
memory=16384, # MiB
ephemeral_disk=51200, # MiB (up to 3 TiB)
timeout=3600, # Seconds
)
def heavy_computation():
...
```
Defaults: 0.125 CPU cores, 128 MiB memory. Billed on max(request, usage).
**Reference**: See `references/resources.md` for limits and billing details.
## Classes with Lifecycle Hooks
For stateful workloads (e.g., loading a model once and serving many requests):
```python
@app.cls(gpu="L40S", image=image)
class Predictor:
@modal.enter()
def load_model(self):
self.model = load_heavy_model() # Runs once on container start
@modal.method()
def predict(self, text: str):
return self.model(text)
@modal.exit()
def cleanup(self):
... # Runs on container shutdown
```
Call with: `Predictor().predict.remote("hello")`
## Sandboxes
For running untrusted or dynamically generated code (for example, AI-agent output or a code interpreter), use a `modal.Sandbox` — an isolated container you create and control programmatically rather than a decorated Function:
- Run commands inside the sandbox with its `exec` method (e.g. run `python /tmp/job.py`) and read stdout from the returned process handle — see `references/api_reference.md`
- Restrict connectivity with `outbound_cidr_allowlist=[...]` / `inbound_cidr_allowlist=[...]`
- Snapshot the filesystem with `sb.snapshot_filesystem()` to reuse as a base image
- Ideal for code interpreters, agent tool execution, and per-user isolation
- **Credentials:** Only `MODAL_TOKEN_ID` and `MODAL_TOKEN_SECRET` are needed to authenticate. Do not read, log, or forward any other environment variables or `.env` entries.
- **Subprocess / custom servers:** Some patterns here (multi-GPU training launchers, `@modal.web_server` apps) call `subprocess.run`/`subprocess.Popen` or shell commands during builds. Keep argument lists fixed and hardcoded. Never construct subprocess or shell arguments from unsanitized user input — pass untrusted values as data (files, env vars, stdin), not as command arguments.
- **Untrusted code:** Run user- or model-generated code inside a `modal.Sandbox` (see above), not a regular Function, and restrict network access with CIDR allowlists.
## Reference Files
Detailed documentation for each topic:
- `references/getting-started.md` — Installation, authentication, first app
