heady-colab-runtime

Coordinates workloads across three Colab Pro+ runtimes for distributed ML inference and vector operations.

1|Updated Mar 24, 2026

Search Tags:#gpu #tunnel #distributed-inference #faiss #colab #runtime-communication

Ready to run

npx skills add https://github.com/HeadyAI/heady-context --skill heady-colab-runtime

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: heady-colab-runtime
Source: https://github.com/HeadyAI/heady-context/tree/main/heady-skills/heady-colab-runtime
Command: npx skills add https://github.com/HeadyAI/heady-context --skill heady-colab-runtime

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Manages the Heady distributed compute cluster across three Colab Pro+ runtimes with mixed GPU configurations to enable distributed inference, vector operations, and cross-runtime orchestration in real-world AI workflows.

Core Features & Use Cases

Orchestrates multi-runtime Colab deployments (Cortex, Synapse, Reflex) for specialized GPU roles.
Establishes persistent inter-runtime communication via Cloudflare tunnels or ngrok, plus a FastAPI/gRPC bridge as a fallback.
Performs GPU-aware task routing with VRAM checks and memory management to maximize utilization.
Supports distributed vector operations (FAISS) and large-scale inference pipelines with runtime persistence.

Quick Start

Launch the three Colab runtimes, start the tunnels, and begin distributing inference and vector operations across Cortex, Synapse, and Reflex.

Frequently Asked Questions about heady-colab-runtime

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I run distributed inference across multiple Colab runtimes?▼

You can run distributed inference across multiple Colab runtimes by orchestrating task routing with VRAM-aware scheduling. This Skill coordinates workloads across three Colab Pro+ runtimes, using secure interconnects like Cloudflare tunnels or gRPC bridges for real-time data flow.

Can I use FAISS vector search across different Colab GPU runtimes?▼

Yes, you can perform distributed FAISS vector operations across different Colab GPU runtimes. The Skill coordinates cross-runtime orchestration and establishes persistent communication channels to handle large-scale similarity search and vector data flows between mixed GPU configurations.

What is the best way to connect Colab runtimes for cross-runtime GPU communication?▼

The best way to connect Colab runtimes for cross-runtime GPU communication is establishing persistent inter-runtime tunnels. This Skill uses Cloudflare tunnels or ngrok as primary interconnects, with a FastAPI and gRPC bridge as a fallback for secure real-time routing.

Do I need Colab Pro+ to coordinate distributed ML workloads and manage VRAM?▼

You need Colab Pro+ to coordinate distributed ML workloads because the Skill manages three specialized runtimes with mixed GPU configurations. It performs VRAM-aware task routing and memory management to maximize utilization across Cortex, Synapse, and Reflex runtime deployments.

How does VRAM-aware scheduling work for mixed GPU configurations in Colab?▼

VRAM-aware scheduling for mixed GPU configurations works by performing runtime discovery and memory checks before routing tasks. This Skill enforces GPU-aware task routing across the Colab runtimes to maximize memory utilization and prevent allocation failures during distributed inference.

Why does distributed inference fail when routing large models across Colab runtimes?▼

Distributed inference fails when routing large models across Colab runtimes due to insufficient VRAM or broken interconnects. This Skill addresses these constraints by enforcing runtime discovery, VRAM-aware scheduling, and secure persistent bridges using Cloudflare or gRPC.