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Modal: Running Python in the Cloud
This document provides a comprehensive guide to using Modal, a cloud computing platform that simplifies running Python code remotely.

Key Features:

Infrastructure-less: No need to manage servers, containers, or cloud accounts.
Fast and Scalable: Deploy code within seconds and scale horizontally to thousands of containers.
Customizable Environments: Define container environments in code, including Python libraries, system packages, and data preloading.
GPU Acceleration: Attach GPUs with a single line of code for computationally intensive tasks.
Persistent Cron Jobs: Schedule code execution at regular intervals.
Web Endpoints: Expose your functions as web services.
Distributed Objects: Leverage powerful primitives like distributed dictionaries and queues for inter-process communication.
Getting Started:

Create an account: Sign up at modal.com.
Install the Modal Python package: pip install modal
Set up a token: Use the modal token new command to generate a token. Store it securely (e.g., as an environment variable or in a configuration file).
Running your first Modal function:

from modal import App, Image

app = App("my-first-modal-app")  # Note: prior to April 2024, "app" was called "stub"

@app.function()
def hello_world():
  print("Hello from Modal!")

if __name__ == "__main__":
  app.run() 
Create a new Modal app (app = App(...)).
Define a function with the @app.function decorator.
Run the app locally with app.run().
Deploying your app:

Once you've tested your code, deploy it to Modal for persistent execution.

modal deploy my_app.py 
This command will create a deployment and make your functions accessible from anywhere.
Customizing Environments:

Use the Image class to define container environments for your functions.

Add Python packages:
image = (
    Image.debian_slim(python_version="3.10")
    .pip_install("pandas", "numpy")
)

@app.function(image=image)
def my_function():
  import pandas as pd
  import numpy as np
  # Your code using pandas and numpy here
Run shell commands:
image = (
    Image.debian_slim()
    .run_commands(
        "curl -O https://raw.githubusercontent.com/opencv/opencv/master/data/haarcascades/haarcascade_frontalcatface.xml",
    )
)
Attach GPUs:
@app.function(gpu="A100")
def run_ml_model():
  # Your GPU-accelerated code here
Distributed Objects:

Modal provides built-in distributed objects for data sharing and communication.

Dicts: Key-value store, shared across all containers of an app.
my_dict = modal.Dict.from_name("my-persisted-dict", create_if_missing=True)
my_dict["key"] = "value"
value = my_dict["key"]
Queues: First-in, first-out queue for asynchronous tasks.
my_queue = modal.Queue.from_name("my-persisted-queue", create_if_missing=True)
my_queue.put("some object")
value = my_queue.get()
Scaling and Reliability:

Modal automatically scales your apps based on the workload, ensuring efficient resource utilization. Features like retries, timeouts, and concurrency limits enhance reliability.

Continuous Deployment:

Integrate Modal into your CI/CD pipeline for automated deployments using tools like GitHub Actions.

Scheduling Cron Jobs:

Schedule your functions to run regularly using the schedule parameter.

@app.function(schedule=modal.Period(days=1))
def daily_task():
  # Code to run daily
Invoking Deployed Functions:

Call deployed functions from other applications using the Modal client library or via HTTPS endpoints.

Examples:

Web App with OCR: Document OCR Web App
Job Queue: Job Queue
Further Exploration:

Modal Documentation: https://docs.modal.com
Modal Blog: https://blog.modal.com
Modal Examples: https://github.com/modal-labs/modal-examples
Modal Community Forum: https://community.modal.com
With its powerful features and ease of use, Modal empowers developers to run their Python code in the cloud effortlessly, focusing on building applications instead of managing infrastructure.