Introduction
Getting started
- Create an account
- Download the CLI
- Start developing and deploying apps - that’s it!
Beam is currently in private beta. If you’re reading this, you’re one of the first people to try it.
📦 Setup remote development environments in code
Configure your runtime in Python - tell us how many GPUs you need and which libraries you want installed, and Beam will spawn a remote environment for you.
🛰 Develop locally on remote hardware
You can write and run your code locally - except when you enter your shell, your code will run on Beam instead of your local machine.
🚀 Deploy apps as serverless functions
Deploy your apps as serverless REST APIs, scheduled cron jobs, or webhooks - all in just four lines of Python.
What can you do with Beam?
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Deploy ML models on serverless runtimes. Your app will scale automatically with traffic and spin down when idle.
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Develop on your local IDE, while running on remote GPUs
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Instantly jump from hacking on a script to serving an API in production
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Save money on your cloud bill. Beam is serverless and charges simple usage-based, per-second pricing
Tutorial: Deploying Stable Diffusion on GPU
Get started with an example
Stable Diffusion on GPU
Deploy Stable Diffusion using a GPU
Web Scraping
Scraping a website and running the results through an ML model
Huggingface REST API
Deploying a pre-trained ML model as a REST API
Core Concepts
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Apps. Each project in Beam is called an app. When you first start Beam, you’ll be prompted to define your environment through the
Beam.App()method in the SDK. -
Triggers. Triggers are actions that can invoke your Beam apps. For example, a REST API trigger allows your Beam app to be invoked via a REST API. A webhook trigger will allow your Beam app to be invoked asyncronously using a webhook, and so on.
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Outputs. Outputs are file paths that can be used to save files created when your functions are run.