Introduction

Cerebrium is a cloud GPU infrastructure platform that transforms how teams deploy and scale machine learning applications. By handling the complex infrastructure – GPUs, Kubernetes, queues, monitoring, and scaling – Cerebrium lets teams focus on building applications that deliver value.

The platform achieves this through rapid cold starts (Less than 5 seconds), flexible GPU options, automated scaling (1 to 10,000+ requests), and simple deployment processes. With 99.9% uptime and continuous weekly improvements based on user feedback, Cerebrium provides the reliability needed for production ML apps.

​

Getting Started

Setting up and deploying an app on Cerebrium takes just a few steps:

​

1. Install the CLI

pip install cerebrium
cerebrium login  # Redirects to your browser for authentication

​

2. Initialize a Project

cerebrium init my-first-app
cd my-first-app

This creates a basic project with main.py for app code and cerebrium.toml for configuration.

​

3. Deploy an App

cerebrium deploy

The app now builds and deploys, typically within a few seconds. Check out a detailed description of the deployment process for more information. Once deployed, an application becomes callable through an endpoint https://api.cortex.cerebrium.ai/v4/{project-id}/{app-name}/{function-name}.

​

How It Works

Cerebrium uses containerization to ensure consistent environments and reliable scaling for apps. When code is deployed, Cerebrium packages it with all necessary dependencies into a container image. This image serves as a blueprint for creating instances that handle incoming requests. The system automatically manages scaling, creating new instances when traffic increases and removing them during quiet periods.

For a detailed explanation of how Cerebrium builds and manages container images, see our Defining Container Images Guide.

Join our Discord community for support and updates, or reach out to support@cerebrium.ai with any questions.