<aside> ➡️

Get started with Tetra

</aside>

Tetra is a Python SDK that streamlines the development and deployment of AI workflows on RunPod's Serverless infrastructure. It provides an abstraction layer that lets you define, execute, and monitor sophisticated AI pipelines using nothing but Python code and your local terminal, eliminating the need to interact with the RunPod console GUI.

Get started

Learn how to code Tetra workflows in serial and parallel by following this step-by-step tutorial:

You can also start by cloning the Tetra repository and running the examples inside:

git clone <https://github.com/runpod/tetra-examples.git>

Why use Tetra?

Tetra provides several advantages over vanilla Serverless:

• Simplified workflow development: Define AI pipelines in pure Python with minimal configuration, focusing on your logic rather than infrastructure details.
• Optimized resource utilization: Specify hardware requirements at the function level for precise control over GPU and CPU allocation.
• Seamless deployment: Tetra automatically handles RunPod Serverless infrastructure setup, worker communication, and data transfer.
• Reduced development overhead: Skip the tedious process of writing application code, building Docker containers, and managing endpoints for each worker.
• Intuitive programming model: Use Python decorators to mark functions for remote execution.

Key concepts

Resource configuration

Tetra lets you specify hardware requirements at the function level through the LiveServerless object. This provides granular control over GPU allocation and worker scaling.limits.

For example: