Core Concepts

How Beam Works

Beam is a new kind of cloud provider that makes using the cloud feel almost the same as using your local machine. You write plain Python, add a decorator, and run your file. Beam packages your code into a container, launches it in the cloud in under a second, runs it, and scales out automatically when traffic increases. It’s powered by an open-source container orchestrator that launches containers in less than 1 second. There’s no infrastructure to provision and no YAML to write. You only pay for the compute you use, billed by the millisecond.

The examples below use the Python SDK, which provides Beam’s decorator programming model. The TypeScript SDK offers programmatic access for creating sandboxes and calling deployed endpoints.

Functions

You can run functions on the cloud, either once, or on a schedule. Learn more about Functions.

Functions

One-off Python functions, like training runs, scraping, or batch jobs.

from beam import function

@function()
def handler():
    return {}

if __name__ == "__main__":
    # Runs locally
    handler.local()
    # Runs on the cloud
    handler.remote()

Scheduled Jobs

Functions that run based on a schedule you specify.

from beam import schedule

@schedule(when="every 1d")
def handler():
    return {}

if __name__ == "__main__":
    # Runs locally
    handler.local()
    # Runs on the cloud
    handler.remote()

Run Your Function

You’ll run your functions like a normal Python function: python app.py. Even though it feels like the code is running locally, it’s running on a container in the cloud.

Endpoints

You can also deploy synchronous and asynchronous web endpoints. Learn more about Endpoints and Task Queues.

Endpoints

Synchronous REST API endpoints, for tasks that run in 60s or less.

from beam import endpoint

@endpoint(name="quickstart")
def handler():
  return {}

Task Queues

Asynchronous REST API endpoints, for heavier tasks that take a long time to run.

from beam import task_queue

@task_queue(name="quickstart")
def handler():
  print(48393 * 39383)

Testing Your Code (Optional)

Beam provides a temporary cloud environment to test your code.
These environments hot-reload with your code changes. You can test your workflow end-to-end before deploying to production.

beam serve app.py:handler

Deploying to Production

When you’re ready to deploy a persistent endpoint, you’ll use beam deploy:

beam deploy app.py:handler

Web Services

You can also bring your own container and host web services, like Jupyter Notebooks, Node.js apps, and much more. Learn more about Pods.

Pods

Run any container behind an SSL-backed REST API.

from beam import Pod

pod = Pod(
  name="my-pod",
  cpu=2,
  memory="1Gi",
  ports=[8000],
  entrypoint=["python", "-m", "http.server", "--bind", "::", "8000"],
)

# Run the container as an API
pod.deploy()

Start Here

Customizing Container Images

Managing Data

Scaling & Performance

Sandboxes

Endpoints and Web Servers

Task Queues

One-Off Functions

Hosting Containers

Advanced Topics

Self-Hosting

Resources

Security

Core Concepts

How Beam Works

Functions

Functions

Scheduled Jobs

Run Your Function

Endpoints

Endpoints

Task Queues

Testing Your Code (Optional)

Deploying to Production

Web Services

Pods

​How Beam Works

​Functions

Functions

Scheduled Jobs

Run Your Function

​Endpoints

Endpoints

Task Queues

Testing Your Code (Optional)

Deploying to Production

​Web Services

Pods

How Beam Works

Functions

Endpoints

Web Services