Modal

---
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
metadata:
  version: '1.3'
  category: data-science-and-ml
  maintainer: Kalaris Labs
  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
- **Scheduled jobs** via cron or fixed intervals
- **Sub-second cold starts** for low-latency inference

Everything in Modal is defined as code — no YAML, no Dockerfiles required (though both are supported).

## When to Use This Skill

Use this skill when:
- Deploy or serve AI/ML models in the cloud
- Run GPU-accelerated computations (training, inference, fine-tuning)
- Create serverless web APIs or endpoints
- Scale batch processing jobs in parallel
- Schedule recurring tasks (data pipelines, retraining, scraping)
- Need persistent cloud storage for model weights or datasets
- Want to run code in custom container environments
- Build job queues or async task processing systems

## Installation and Authentication

### Install

```bash
uv pip install modal
```

The Modal Python SDK supports Python 3.10–3.14. This skill targets the stable `modal>=1.0` API (current release: 1.4.x).

### Authenticate

Prefer existing credentials before creating new ones. Only the two Modal-specific
variables below are relevant — do not read, load, or expose any other environment
variables or `.env` file contents:

1. Check whether `MODAL_TOKEN_ID` and `MODAL_TOKEN_SECRET` are already set in the current environment.
2. If not, look up only those two keys in a local `.env` file (ignore all other entries) and load them if appropriate for the workflow.
3. Only fall back to interactive `modal setup` or generating fresh tokens if neither source already provides those two values.

```bash
modal setup
```

This opens a browser for authentication. For CI/CD or headless environments, use environment variables:

```bash
export MODAL_TOKEN_ID=<your-token-id>
export MODAL_TOKEN_SECRET=<your-token-secret>
```

If tokens are not already available in the environment or `.env`, generate them at https://modal.com/settings

Modal offers a free tier with $30/month in credits.

**Reference**: See `references/getting-started.md` for detailed setup and first app walkthrough.

## Core Concepts

### App and Functions

A Modal `App` groups related functions. Functions decorated with `@app.function()` run remotely in the cloud:

```python
import modal

app = modal.App("my-app")

@app.function()
def square(x):
    return x ** 2

@app.local_entrypoint()
def main():
    # .remote() runs in the cloud
    print(square.remote(42))
```

Run with `modal run script.py`. Deploy with `modal deploy script.py`.

**Reference**: See `references/functions.md` for lifecycle hooks, classes, `.map()`, `.spawn()`, and more.

### Container Images

Modal builds container images from Python code. The recommended package installer is `uv`:

```python
image = (
    modal.Image.debian_slim(python_version="3.11")
    .uv_pip_install("torch==2.12.0", "transformers==5.9.0", "accelerate==1.13.0")
    .apt_install("git")
)

@app.function(image=image)
def inference(prompt):
    from transformers import pipeline
    pipe = pipeline("text-generation", model="meta-llama/Llama-3-8B")
    return pipe(prompt)
```

Key image methods:
- `.uv_pip_install()` — Install Python packages with uv (recommended)
- `.pip_install()` — Install with pip (fallback)
- `.apt_install()` — Install system packages
- `.run_commands()` — Run shell commands during build
- `.run_function()` — Run Python during build (e.g., download model weights)
- `.add_local_python_source()` — Add local modules
- `.env()` — Set environment variables

**Reference**: See `references/images.md` for Dockerfiles, micromamba, caching, GPU build steps.

### GPU Compute

Request GPUs via the `gpu` parameter:

```python
@app.function(gpu="H100")
def train_model():
    import torch
    device = torch.device("cuda")
    # GPU training code here

# Multiple GPUs
@app.function(gpu="H100:4")
def distributed_training():
    ...

# GPU fallback chain
@app.function(gpu=["H100", "A100-80GB", "A100-40GB"])
def flexible_inference():
    ...
```

Available GPUs: T4, L4, A10, L40S, A100-40GB, A100-80GB, RTX-PRO-6000, H100, H200, B200, B200+

- GPUs are always specified as **strings** (e.g. `gpu="H100"`, `gpu="H100:4"`). The old `modal.gpu.*` objects are deprecated as of v0.73.31.
- Up to 8 GPUs per container (except A10: up to 4)
- L40S is recommended for inference (cost/performance balance, 48 GB VRAM)
- H100/A100 can be auto-upgraded to H200/A100-80GB at no extra cost
- Use `gpu="H100!"` to prevent auto-upgrade

**Reference**: See `references/gpu.md` for GPU selection guidance and multi-GPU training.

### Volumes (Persistent Storage)

Volumes provide distributed, persistent file storage:

```python
vol = modal.Volume.from_name("model-weights", create_if_missing=True)

@app.function(volumes={"/data": vol})
def save_model():
    # Write to the mounted path
    with open("/data/model.pt", "wb") as f:
        torch.save(model.state_dict(), f)

@app.function(volumes={"/data": vol})
def load_model():
    model.load_state_dict(torch.load("/data/model.pt"))
```

- Optimized for write-once, read-many workloads (model weights, datasets)
- CLI access: `modal volume ls`, `modal volume put`, `modal volume get`
- Background auto-commits every few seconds
- Mount read-only or limit to a subdirectory with `vol.with_mount_options(read_only=True, sub_path="subset")`

**Reference**: See `references/volumes.md` for v2 volumes, concurrent writes, and best practices.

### Secrets

Securely pass credentials to functions:

```python
@app.function(secrets=[modal.Secret.from_name("my-api-keys")])
def call_api():
    import os
    api_key = os.environ["API_KEY"]
    # Use the key
```

Create secrets via CLI: `modal secret create my-api-keys API_KEY=sk-xxx`

Or from a `.env` file: `modal.Secret.from_dotenv()`

**Reference**: See `references/secrets.md` for dashboard setup, multiple secrets, and templates.

### Web Endpoints

Serve models and APIs as web endpoints:

```python
@app.function()
@modal.fastapi_endpoint()
def predict(text: str):
    return {"result": model.predict(text)}
```

- `modal serve script.py` — Development with hot reload and temporary URL
- `modal deploy script.py` — Production deployment with permanent URL
- Supports FastAPI, ASGI (Starlette, FastHTML), WSGI (Flask, Django), WebSockets
- Request bodies up to 4 GiB, unlimited response size

**Reference**: See `references/web-endpoints.md` for ASGI/WSGI apps, streaming, auth, and WebSockets.

### Scheduled Jobs

Run functions on a schedule:

```python
@app.function(schedule=modal.Cron("0 9 * * *"))  # Daily at 9 AM UTC
def daily_pipeline():
    # ETL, retraining, scraping, etc.
    ...

@app.function(schedule=modal.Period(hours=6))
def periodic_check():
    ...
```

Deploy with `modal deploy script.py` to activate the schedule.

- `modal.Cron("...")` — Standard cron syntax, stable across deploys
- `modal.Period(hours=N)` — Fixed interval, resets on redeploy
- Monitor runs in the Modal dashboard

**Reference**: See `references/scheduled-jobs.md` for cron syntax and management.

### Scaling and Concurrency

Modal autoscales containers automatically. Configure limits:

```python
@app.function(
    max_containers=100,    # Upper limit
    min_containers=2,      # Keep warm for low latency
    buffer_containers=5,   # Reserve capacity
    scaledown_window=300,  # Idle seconds before shutdown
)
def process(data):
    ...
```

Process inputs in parallel with `.map()`:

```python
results = list(process.map([item1, item2, item3, ...]))
```

Enable concurrent request handling per container with `@modal.concurrent`. Set
`target_inputs` (the autoscaler's per-container target) below `max_inputs` (the hard
cap) to keep headroom while scaling up:

```python
@app.function()
@modal.concurrent(max_inputs=10, target_inputs=8)
async def handle_request(req):
    ...
```

Reconfigure a deployed Function or Cls at invocation time without redeploying using
`Function.with_options()` / `Function.with_concurrency()` / `Function.with_batching()`
(and `Cls.with_options()`):

```python
Model = modal.Cls.from_name("my-app", "Model")
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:

```python
app = modal.App.lookup("sandbox-demo", create_if_missing=True)

# Isolated container; restrict egress for untrusted workloads
sb = modal.Sandbox.create(
    app=app,
    image=modal.Image.debian_slim(),
    outbound_cidr_allowlist=["10.0.0.0/8"],
)

# Stream files in/out via the filesystem API (beta)
sb.filesystem.write_text("print(2 ** 10)\n", "/tmp/job.py")
contents = sb.filesystem.read_text("/tmp/job.py")

sb.terminate()
```

- 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

## Common Workflow Patterns

### GPU Model Inference Service

```python
import modal

app = modal.App("llm-service")

image = (
    modal.Image.debian_slim(python_version="3.11")
    .uv_pip_install("vllm")
)

@app.cls(gpu="H100", image=image, min_containers=1)
class LLMService:
    @modal.enter()
    def load(self):
        from vllm import LLM
        self.llm = LLM(model="meta-llama/Llama-3-70B")

    @modal.method()
    @modal.fastapi_endpoint(method="POST")
    def generate(self, prompt: str, max_tokens: int = 256):
        outputs = self.llm.generate([prompt], max_tokens=max_tokens)
        return {"text": outputs[0].outputs[0].text}
```

### Batch Processing Pipeline

```python
app = modal.App("batch-pipeline")
vol = modal.Volume.from_name("pipeline-data", create_if_missing=True)

@app.function(volumes={"/data": vol}, cpu=4.0, memory=8192)
def process_chunk(chunk_id: int):
    import pandas as pd
    df = pd.read_parquet(f"/data/input/chunk_{chunk_id}.parquet")
    result = heavy_transform(df)
    result.to_parquet(f"/data/output/chunk_{chunk_id}.parquet")
    return len(result)

@app.local_entrypoint()
def main():
    chunk_ids = list(range(100))
    results = list(process_chunk.map(chunk_ids))
    print(f"Processed {sum(results)} total rows")
```

### Scheduled Data Pipeline

```python
app = modal.App("etl-pipeline")

@app.function(
    schedule=modal.Cron("0 */6 * * *"),  # Every 6 hours
    secrets=[modal.Secret.from_name("db-credentials")],
)
def etl_job():
    import os
    db_url = os.environ["DATABASE_URL"]
    # Extract, transform, load
    ...
```

## CLI Reference

| Command | Description |
|---------|-------------|
| `modal setup` | Authenticate with Modal |
| `modal run script.py` | Run a script's local entrypoint |
| `modal serve script.py` | Dev server with hot reload |
| `modal deploy script.py` | Deploy to production |
| `modal volume ls <name>` | List files in a volume |
| `modal volume put <name> <file>` | Upload file to volume |
| `modal volume get <name> <file>` | Download file from volume |
| `modal secret create <name> K=V` | Create a secret |
| `modal secret list` | List secrets |
| `modal app list` | List deployed apps |
| `modal app stop <name>` | Stop a deployed app |

## Security Notes

- **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
- `references/functions.md` — Functions, classes, lifecycle hooks, remote execution
- `references/images.md` — Container images, package installation, caching
- `references/gpu.md` — GPU types, selection, multi-GPU, training
- `references/volumes.md` — Persistent storage, file management, v2 volumes
- `references/secrets.md` — Credentials, environment variables, dotenv
- `references/web-endpoints.md` — FastAPI, ASGI/WSGI, streaming, auth, WebSockets
- `references/scheduled-jobs.md` — Cron, periodic schedules, management
- `references/scaling.md` — Autoscaling, concurrency, .map(), limits
- `references/resources.md` — CPU, memory, disk, timeout configuration
- `references/examples.md` — Common use cases and patterns
- `references/api_reference.md` — Key API classes and methods

Read these files when detailed information is needed beyond this overview.

## Agent operating procedure

1. **Check the environment.** Confirm the Python environment and library versions (`python -c "import pkg; print(pkg.__version__)"`) and inspect the data's shape, types and missing values.
2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.
3. **Run a small version first.** Run on a sample or a single fold first and check runtime and memory.
4. **Execute the full task** using the instructions and references above.
5. **Validate the result.** Use held-out data, fixed random seeds and appropriate metrics; check for leakage; report uncertainty (CIs, std over seeds).
6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.

| If this happens | Do this |
|---|---|
| Out-of-memory or very slow execution | Subsample, use chunked or lazy computation, or reduce model size, and tell the user what changed. |
| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |
| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |

**Integrity rules**

- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.
- Never report a metric you did not compute in this session; show the code path that produced every number.
- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.
- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.

## Related skills

- `modal-serverless-gpu`: Serverless GPU cloud platform for running ML workloads.
- `tensorrt-llm`: Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency.
- `serving-llms-vllm`: Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching.