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Update Modal skill
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Timothy Kassis
@@ -1,92 +1,175 @@
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# Getting Started with Modal
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# Modal Getting Started Guide
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## Sign Up
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## Installation
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Sign up for free at https://modal.com and get $30/month of credits.
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Install Modal using uv (recommended) or pip:
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```bash
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# Recommended
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uv pip install modal
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# Alternative
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pip install modal
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```
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## Authentication
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Set up authentication using the Modal CLI:
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### Interactive Setup
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```bash
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modal token new
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modal setup
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```
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This creates credentials in `~/.modal.toml`. Alternatively, set environment variables:
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- `MODAL_TOKEN_ID`
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- `MODAL_TOKEN_SECRET`
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This opens a browser for authentication and stores credentials locally.
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## Basic Concepts
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### Headless / CI/CD Setup
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### Modal is Serverless
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For environments without a browser, use token-based authentication:
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Modal is a serverless platform - only pay for resources used and spin up containers on demand in seconds.
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1. Generate tokens at https://modal.com/settings
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2. Set environment variables:
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### Core Components
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```bash
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export MODAL_TOKEN_ID=<your-token-id>
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export MODAL_TOKEN_SECRET=<your-token-secret>
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```
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**App**: Represents an application running on Modal, grouping one or more Functions for atomic deployment.
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Or use the CLI:
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**Function**: Acts as an independent unit that scales up and down independently. No containers run (and no charges) when there are no live inputs.
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```bash
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modal token set --token-id <id> --token-secret <secret>
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```
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**Image**: The environment code runs in - a container snapshot with dependencies installed.
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### Free Tier
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## First Modal App
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Modal provides $30/month in free credits. No credit card required for the free tier.
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Create a file `hello_modal.py`:
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## Your First App
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### Hello World
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Create a file `hello.py`:
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```python
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import modal
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app = modal.App(name="hello-modal")
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app = modal.App("hello-world")
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@app.function()
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def hello():
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print("Hello from Modal!")
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return "success"
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def greet(name: str) -> str:
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return f"Hello, {name}! This ran in the cloud."
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@app.local_entrypoint()
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def main():
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hello.remote()
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result = greet.remote("World")
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print(result)
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```
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Run with:
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Run it:
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```bash
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modal run hello_modal.py
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modal run hello.py
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```
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## Running Apps
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What happens:
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1. Modal packages your code
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2. Creates a container in the cloud
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3. Executes `greet()` remotely
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4. Returns the result to your local machine
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### Ephemeral Apps (Development)
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### Understanding the Flow
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Run temporarily with `modal run`:
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```bash
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modal run script.py
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- `modal.App("name")` — Creates a named application
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- `@app.function()` — Marks a function for remote execution
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- `@app.local_entrypoint()` — Defines the local entry point (runs on your machine)
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- `.remote()` — Calls the function in the cloud
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- `.local()` — Calls the function locally (for testing)
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### Running Modes
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| Command | Description |
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|---------|-------------|
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| `modal run script.py` | Run the `@app.local_entrypoint()` function |
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| `modal serve script.py` | Start a dev server with hot reload (for web endpoints) |
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| `modal deploy script.py` | Deploy to production (persistent) |
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### A Simple Web Scraper
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```python
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import modal
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app = modal.App("web-scraper")
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image = modal.Image.debian_slim().uv_pip_install("httpx", "beautifulsoup4")
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@app.function(image=image)
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def scrape(url: str) -> str:
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import httpx
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from bs4 import BeautifulSoup
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response = httpx.get(url)
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soup = BeautifulSoup(response.text, "html.parser")
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return soup.get_text()[:1000]
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@app.local_entrypoint()
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def main():
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result = scrape.remote("https://example.com")
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print(result)
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```
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The app stops when the script exits. Use `--detach` to keep running after client exits.
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### GPU-Accelerated Inference
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### Deployed Apps (Production)
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```python
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import modal
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Deploy persistently with `modal deploy`:
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```bash
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modal deploy script.py
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app = modal.App("gpu-inference")
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image = (
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modal.Image.debian_slim(python_version="3.11")
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.uv_pip_install("torch", "transformers", "accelerate")
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)
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@app.function(gpu="L40S", image=image)
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def generate(prompt: str) -> str:
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from transformers import pipeline
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pipe = pipeline("text-generation", model="gpt2", device="cuda")
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result = pipe(prompt, max_length=100)
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return result[0]["generated_text"]
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@app.local_entrypoint()
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def main():
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print(generate.remote("The future of AI is"))
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```
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View deployed apps at https://modal.com/apps or with:
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```bash
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modal app list
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## Project Structure
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Modal apps are typically single Python files, but can be organized into modules:
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```
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my-project/
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├── app.py # Main app with @app.local_entrypoint()
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├── inference.py # Inference functions
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├── training.py # Training functions
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└── common.py # Shared utilities
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```
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Stop deployed apps:
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```bash
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modal app stop app-name
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```
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Use `modal.Image.add_local_python_source()` to include local modules in the container image.
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## Key Features
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## Key Concepts Summary
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- **Fast prototyping**: Write Python, run on GPUs in seconds
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- **Serverless APIs**: Create web endpoints with a decorator
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- **Scheduled jobs**: Run cron jobs in the cloud
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- **GPU inference**: Access T4, L4, A10, A100, H100, H200, B200 GPUs
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- **Distributed volumes**: Persistent storage for ML models
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- **Sandboxes**: Secure containers for untrusted code
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| Concept | What It Does |
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|---------|-------------|
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| `App` | Groups related functions into a deployable unit |
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| `Function` | A serverless function backed by autoscaling containers |
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| `Image` | Defines the container environment (packages, files) |
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| `Volume` | Persistent distributed file storage |
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| `Secret` | Secure credential injection |
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| `Schedule` | Cron or periodic job scheduling |
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| `gpu` | GPU type/count for the function |
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## Next Steps
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- See `functions.md` for advanced function patterns
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- See `images.md` for custom container environments
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- See `gpu.md` for GPU selection and configuration
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- See `web-endpoints.md` for serving APIs
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