Hello, world!

This is a trivial example of a Modal function, but it illustrates a few features:

  • You can print things to stdout and stderr.
  • You can return data.
  • You can map over a function.

Import Modal and define the app

Let’s start with the top level imports. You need to import Modal and define the app. A stub is an object that defines everything that will be run.

import sys

import modal

stub = modal.Stub("example-hello-world")

Defining a function

Here we define a Modal function using the modal.function decorator. The body of the function will automatically be run remotely. This particular function is pretty silly: it just prints “hello” and “world” alternatingly to standard out and standard error.

def f(i):
    if i % 2 == 0:
        print("hello", i)
        print("world", i, file=sys.stderr)

    return i * i

Running it

Finally, let’s actually invoke it. We put this invocation code inside a @stub.local_entrypoint(). This is because this module will be imported in the cloud, and we don’t want this code to be executed a second time in the cloud.

Run modal run hello_world.py and the @stub.local_entrypoint() decorator will handle starting the Modal app and then executing the wrapped function body.

Inside the main() function body, we are calling the function f in three ways:

1 As a simple local call, f(1000) 2. As a simple remote call f.call(1000) 3. By mapping over the integers 0..19

def main():
    # Call the function locally.

    # Call the function remotely.

    # Parallel map.
    total = 0
    for ret in f.map(range(20)):
        total += ret


What happens?

When you do .call on function f, Modal will execute f in the cloud, not locally on your computer. It will take the code, put it inside a container, run it, and stream all the output back to your local computer.

Try doing one of these things next.

Change the code and run again

For instance, change the print statement in the function f. You can see that the latest code is always run.

Modal’s goal is to make running code in the cloud feel like you’re running code locally. You don’t need to run any commands to rebuild, push containers, or go to a web UI to download logs.

Map over a larger dataset

Change the map range from 20 to some large number. You can see that Modal will create and run more containers in parallel.

The function f is obviously silly and doesn’t do much, but you could imagine something more significant, like:

  • Training a machine learning model
  • Transcoding media
  • Backtesting a trading algorithm.

Modal lets you parallelize that operation trivially by running hundreds or thousands of containers in the cloud.