In this guide, you’ll learn how to:

  • Create an app with Beam
  • Deploy your app to production
  • View your logs in the dashboard
  • Call the REST API

Getting started

You’ll need two things setup before you can complete this guide:

Create an app

Let’s first create a Python file which will contain our Beam app definition. This is the environment you’ll be developing against once you start the app.

You can name your files whatever you’d like - Beam tries to limit the changes you need to make to your existing code structure, so you can organize your projects however you prefer.
import beam

app = beam.App(

Connect to the runtime

Now spin up the environment. In your shell, run:

beam start

You can run your code on this remote environment. It will feel as though you’re working locally, but all your code is actually being run on a container we’ve magically spun up for you.

Write a hello world function

Create a new file. This is the function you’ll be deploying on Beam.
def hello_world(**inputs):
    print("After deploying, you'll see these logs in the web dashboard 👀")
    return {"response": inputs["text"]}

if __name__ == "__main__":
    text = "Testing 123"

Test locally

You can run your code locally, just like you would normally. Except that this code will run on the remote environment that you defined above.


Write a REST API Trigger

Your function can be deployed as a REST API or a webhook.

Add a REST API Trigger to
import beam

app = beam.App(

    inputs={"text": beam.Types.String()},
        "response": beam.Types.String(),

Deploying to prod 🚀

Deploy your app by running:

beam deploy

A browser window will open, showing you the deployment logs.

Calling the API

Click the Call API button in the dashboard. This will generate a cURL request for you to invoke the API. Copy the code, and paste it into your shell. You should see your logs and metrics appear in the dashboard.

What’s next?

Now that you’ve created a function, deployed it to prod, and invoked the API, you can explore some of our more advanced use cases, such as deploying an app on GPU or scheduling your tasks to run asyncronously using webhooks.

Need help?

Get help from our engineers, and ask to join our Slack channel to participate in the community.