Conversation with Gemini List setup instructions for cluster

Gemini said To set up the cluster repository, initialize a new git repository locally, add the remote origin using git@github.com:sterl27/cluster.git, and push your local branch to the main origin.

Cluster

Node.js TypeScript License Docker Kubernetes

Vision

Cluster is a high-performance, modular backend architecture designed for seamless scalability and low-latency data orchestration. Built with a minimalist, dark-aesthetic philosophy, it prioritizes clean code, rapid deployment, and robust API integration.

✨ Features

🚀 Modular Architecture - Decoupled services for independent scaling

🔄 Real-time Orchestration - Distributed task management and coordination

📦 Multi-Protocol Support - REST, gRPC, and WebSocket ready

💾 Dual Database Strategy - PostgreSQL (persistent) + Redis (caching)

🐳 Container Native - Docker & Kubernetes manifests included

📡 Production Ready - Health checks, auto-scaling, resource limits

🔐 Enterprise Ready - JWT auth, middleware support, error handling

⚡ TypeScript First - Full type safety and IDE support

Core Architecture

Single Node (Local)

┌─────────────────────────────────────────┐

│ API Layer (Express) │

│ REST Endpoints + gRPC Services │

└────────┬────────────────────────────────┘

     │

┌────┴────┐

▼         ▼

┌────────┐ ┌──────────────┐

│ Cache │ │ Database │

│(Redis) │ │(PostgreSQL) │

└────────┘ └──────────────┘

│         │

└────┬────┘

     ▼

┌─────────────────────────────────────────┐

│ Orchestration Layer │

│ (Task Scheduling & Distribution) │

└─────────────────────────────────────────┘

│

▼

┌─────────────────────────────────────────┐

│ Service Module Layer │

│ (Decoupled, Independent Services) │

└─────────────────────────────────────────┘

3-Node Cluster (Production)

┌─────────────────────────┐

│ Node 1: Gateway (101) │

│ ├─ Express API │

│ ├─ Discovery Registry │

│ └─ Load Balancer │

└──────────┬──────────────┘

       │ gRPC

┌──────────▼──────────────┐

│ Node 2: AI Engine (102) │

│ ├─ S73RL Beat Gen │

│ ├─ Musaix Analysis │

│ └─ gRPC Services │

└──────────┬──────────────┘

       │ TCP (optimized)

┌──────────▼──────────────────┐

│ Node 3: Data Store (103) │

│ ├─ PostgreSQL (Persistent) │

│ ├─ Redis (Cache) │

│ └─ Message Queue │

└─────────────────────────────┘

Service Discovery: No hardcoded IPs. Nodes locate each other dynamically using the ClusterNodeRegistry.

Service Discovery

Cluster uses dynamic service discovery to eliminate hardcoded IP addresses:

// Automatically find PostgreSQL on Node 3

import { discovery } from './core/discovery';

const dbUrl = await discovery.getDatabaseUrl();

// Returns: "postgresql://192.168.1.103:5432"

const redisUrl = await discovery.getRedisUrl();

// Returns: "redis://192.168.1.103:6379"

// Check if service is healthy

const isAlive = await discovery.isServiceHealthy("postgresql");

Benefits:

✓ Nodes can relocate without code changes

✓ Automatic failover ready

✓ Works with Alic3X PRO, Musaix, and custom services

✓ 30-second caching for performance

See: SERVICE_DISCOVERY.md for architecture details

Core Architecture (Detailed)

Modular Engine: Decoupled services for independent scaling and maintenance.

Unified Interface: A streamlined entry point for complex data clusters.

Performance First: Optimized for high-throughput environments and real-time processing.

Service Discovery: Dynamic discovery of databases, caches, and services across nodes.

Quick Start

Local Development (Single Machine)

Clone the Repository

git clone git@github.com:sterl27/cluster.git

cd cluster

Environment Setup

cp .env.example .env

Installation

npm install

Run Locally

npm run dev

Server runs on http://localhost:3000

3-Node Ubuntu Cluster (Production)

Deploy across 3 Ubuntu servers with automatic service discovery:

bash scripts/setup-node.sh gateway 192.168.1.101

bash scripts/setup-node.sh ai-engine 192.168.1.102

bash scripts/setup-node.sh data-store 192.168.1.103

See: CLUSTER_DEPLOYMENT.md for complete guide

Tech Stack

Component Technology Purpose

Runtime Node.js 20+ JavaScript execution

Language TypeScript 5.0+ Type safety & IDE support

API Framework Express.js REST API routing

Database PostgreSQL Persistent data storage

Cache Redis In-memory caching layer

RPC gRPC High-performance service communication

Orchestration Kubernetes Container orchestration

Containerization Docker Application packaging

Testing Jest Unit & integration tests

API Documentation

Base URL

http://localhost:3000/api/v1

Health Check

GET /health

Response:

{

"status": "healthy"

}

Status Endpoint

GET /api/v1/status

Response:

{

"status": "operational",

"version": "v1",

"timestamp": "2026-03-19T00:00:00.000Z"

}

Services List

GET /api/v1/services

Response:

{

"services": []

}

Project Structure

cluster/

├── src/

│ ├── api/ # REST route handlers

│ ├── services/ # Business logic modules

│ ├── database/ # PostgreSQL connection pool

│ ├── cache/ # Redis client

│ ├── orchestration/ # Task management

│ ├── middleware/ # Express middleware

│ ├── config/ # Configuration management

│ ├── types/ # TypeScript interfaces

│ ├── utils/ # Helper functions

│ ├── server.ts # Server bootstrap

│ └── index.ts # Application entry point

├── tests/ # Unit test suite

├── docker/ # Docker build files

├── k8s/ # Kubernetes manifests

├── package.json # Dependencies

├── tsconfig.json # TypeScript config

├── .env.example # Environment template

└── README.md # This file

Development

Install Dependencies

npm install

Development Server

npm run dev

Build

npm run build

Run Tests

npm test

npm run test:watch

Lint

npm run lint

Deployment

Docker

npm run docker:build

npm run docker:run

Kubernetes

kubectl apply -f k8s/deployment.yaml

kubectl get deployments cluster

kubectl logs -l app=cluster

3-Node Ubuntu Cluster (Automated)

One-command setup for production deployment:

wget https://raw.githubusercontent.com/sterl27/cluster/n/scripts/setup-node.sh

chmod +x setup-node.sh

./setup-node.sh gateway 192.168.1.101

./setup-node.sh ai-engine 192.168.1.102

./setup-node.sh data-store 192.168.1.103

What the setup script does:

✓ Updates system packages

✓ Installs Node.js 20 + Python 3.11

✓ Configures PostgreSQL + Redis on Node 3

✓ Sets up systemd services for auto-restart

✓ Applies kernel tuning for low-latency (-30-40% latency reduction)

✓ Validates connectivity to all services

Full documentation: See CLUSTER_DEPLOYMENT.md

Kernel Tuning

For manual kernel optimization on Ubuntu servers:

sudo bash scripts/kernel-tuning.sh "$(hostname)"

Optimizations applied:

TCP buffer sizes: 128MB (zero-copy transport)

Network latency: -30-40% reduction

CPU scheduler: -20-30% jitter reduction

I/O scheduler: noop/none for consistent latency

ECN + SACK: Better congestion handling

Cloud Platforms

Cluster is ready for:

AWS ECS/EKS - Use provided Dockerfile and k8s manifests

Google Cloud GKE - Drop-in compatible with Kubernetes YAML

Azure AKS - Full support with managed PostgreSQL & Redis

On-Premises - Full 3-node Ubuntu cluster with discovery + tuning

Examples

Adding a New Service

Create service module in src/services/my-service.ts

export class MyService {

async initialize() {

console.log('MyService initialized');

}

async shutdown() {

console.log('MyService shutting down');

}

}

Register in API router (src/api/index.ts)

router.use('/my-service', myServiceRouter());

Using Cache

import { Cache } from '../cache';

const cache = new Cache(config);

await cache.connect();

// Set value with TTL (seconds)

await cache.set('user:123', JSON.stringify(userData), 3600);

// Get value

const data = await cache.get('user:123');

Database Queries

import { Database } from '../database';

const db = new Database(config);

await db.connect();

const result = await db.query(

'SELECT * FROM users WHERE id = $1',

[userId]

);

Task Orchestration

import { Orchestrator } from '../orchestration';

const orchestrator = new Orchestrator();

const taskId = orchestrator.createTask('process-data');

orchestrator.updateTask(taskId, 'running');

// ... do work ...

orchestrator.updateTask(taskId, 'completed');

Development Philosophy

"Complexity is the enemy of execution." Focus on Atomic Design, Dry Principles, and Extreme Scalability.

Contributing

Fork the Repository

git clone git@github.com:YOUR_USERNAME/cluster.git

Create Feature Branch

git checkout -b feature/your-feature

Commit Changes

git commit -m "feat: add your feature"

Push to Branch

git push origin feature/your-feature

Open Pull Request

Provide clear description

Link related issues

Include test coverage

Use Cases

Real-time Analytics Pipeline

Cluster's modular architecture supports high-throughput data ingestion with Redis caching for metrics and PostgreSQL for historical data.

Microservices Aggregator

Use the orchestration layer to coordinate multiple services and aggregate responses with gRPC for inter-service communication.

Event-Driven Processing

Task orchestration enables event queuing, processing, and state management for complex workflows.

API Gateway

REST + gRPC dual protocol support makes Cluster ideal as a unified API gateway for heterogeneous backend systems.

Performance Characteristics

Throughput: 10k+ req/sec per instance

Latency: P95 < 50ms (with co-located Redis/DB)

Memory: ~256MB baseline, 512MB recommended

CPU: 250m request, 500m limit per pod

License

MIT © 2026 Cluster Contributors

Documentation

Comprehensive guides for deployment, architecture, and operations:

CLUSTER_DEPLOYMENT.md - 6-phase production deployment guide

System setup & kernel tuning

Node 1-3 configuration (Gateway, AI Engine, Data Store)

PostgreSQL + Redis setup

Monitoring & troubleshooting

SERVICE_DISCOVERY.md - Dynamic service location architecture

How nodes find each other without hardcoded IPs

Caching strategy & health checks

Failover & recovery patterns

Extension to Consul/Kubernetes

Automation Scripts

Script Purpose Usage

scripts/setup-node.sh One-command node deployment bash setup-node.sh gateway 192.168.1.101

scripts/kernel-tuning.sh Linux kernel optimization sudo bash kernel-tuning.sh

For issues, feature requests, or questions:

Open an Issue

Subm

Gemini said To set up the cluster repository, perform the following steps for either local development or a production 3-node cluster:

Local Development (Single Machine) Clone the Repository: git clone git@github.com:sterl27/cluster.git cd cluster

Environment Setup: cp .env.example .env (then edit .env with your configuration)

Installation: npm install

Run Locally: npm run dev (Server runs on http://localhost:3000)

3-Node Ubuntu Cluster (Production) Deploy across three Ubuntu servers using the automated setup script:

Download and Prepare Script: wget https://raw.githubusercontent.com/sterl27/cluster/n/scripts/setup-node.sh chmod +x setup-node.sh

Deploy Node 1 (Gateway): ./setup-node.sh gateway 192.168.1.101

Deploy Node 2 (AI Engine): ./setup-node.sh ai-engine 192.168.1.102

Deploy Node 3 (Data Store): ./setup-node.sh data-store 192.168.1.103

Note: For manual kernel optimization, run sudo bash scripts/kernel-tuning.sh "$(hostname)" to reduce network latency.

3 node local ssh ubuntu server - 1 wsl gpu nvidia node

Gemini said To configure a 3-node local cluster where one node is a WSL2 instance with NVIDIA GPU support, follow these steps:

1. GPU Node Setup (WSL2) On your Windows host, ensure you have the NVIDIA Game Ready or Studio Driver installed. In your Ubuntu WSL2 instance:

Install NVIDIA Container Toolkit:

Bash curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
&& curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list |
sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' |
sudo tee /etc/etc/apt/sources.list.d/nvidia-container-toolkit.list sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit Verify GPU Access: Run nvidia-smi to confirm the GPU is passed through from Windows.

2. Networking & SSH Access For all three nodes (WSL2 and the two standalone Ubuntu servers) to communicate:

Internal Networking: Since WSL2 uses a NAT bridge, you may need to use WSLBridge or port forwarding to allow the other two nodes to SSH into the WSL2 instance.

SSH Key Exchange:

Generate keys: ssh-keygen -t rsa -b 4096

Copy to other nodes: ssh-copy-id user@node-ip

Static IPs: Ensure your two standalone servers have static IPs assigned via /etc/netplan/.

3. Cluster Initialization Using the cluster framework previously discussed:

Node 1 (Gateway): bash scripts/setup-node.sh gateway

Node 2 (AI Engine/WSL2): Use this for the GPU node. bash scripts/setup-node.sh ai-engine

Node 3 (Data Store): bash scripts/setup-node.sh data-store

4. Kernel Tuning (WSL2 Caution) While you can run sudo bash scripts/kernel-tuning.sh on the standalone Ubuntu servers, avoid running it inside WSL2 as it uses a proprietary Microsoft kernel that may not support all sysctl optimizations.

WSL2 GPU paravirtualization + kind + NVIDIA Container Toolkit is the exact stack that turns your fresh Ubuntu WSL instance into a full GPU-accelerated local Kubernetes cluster — perfect for running the AI Engine node from your new https://github.com/sterl27/cluster repo.

Your repo already has the modular architecture (Gateway + AI Engine with S73RL Beat Gen/Musaix Analysis + Data Store), Docker support, and k8s/deployment.yaml manifests. GPU-PV + NVIDIA Container Toolkit + kind gives the AI Engine direct access to your RTX 4070 Super for CUDA workloads (including the Nemotron model we talked about) without touching the Windows host gaming/desktop performance.

It's not a full VM or bare-metal K8s — it's still the same lightweight Hyper-V shim we covered in the report you just saved as PDF. The paravirtualized /dev/dxg + stub libraries stay exactly the same; we just layer Docker runtime + kind on top so your Kubernetes pods see nvidia.com/gpu: 1 resources.

1. GPU-PV foundation (already covered in your report)

   • Windows NVIDIA driver → /dev/dxg inside WSL → stub CUDA libs → real RTX 4070 Super.

2. Docker + NVIDIA Container Toolkit bridge

   • Install Docker CE in Ubuntu WSL.

   • NVIDIA Container Toolkit adds the nvidia runtimeClass and CDI config that automatically injects the GPU into any container (or pod) that requests it.

   • When kind spins up nodes (as Docker containers), the toolkit passes the paravirtualized GPU straight through.

   • Your AI Engine pods simply declare resources: limits: nvidia.com/gpu: 1 and get full CUDA access.

3. kind (Kubernetes IN Docker) layer

   • kind runs a full K8s control plane + worker nodes inside Docker containers in WSL.

   • With the toolkit configured as default runtime, every pod sees the GPU exactly like native Kubernetes on Linux.

Performance stays near-native (same 90-98% you saw in the PDF comparison table). Your Windows desktop and gaming stay 100% unaffected.

| Aspect | WSL2 GPU-PV alone | + Docker + NVIDIA Toolkit + kind | Traditional bare-metal K8s |

|-------------------------|------------------------------------|-------------------------------------------|----------------------------|

| GPU Ownership | Shared with Windows | Shared with Windows + all kind pods | Dedicated hardware |

| Setup Time | Driver only | ~5 commands after nvidia-smi | Hours + hardware |

| Your cluster repo fit | nvidia-smi works | AI Engine pods get GPU + kubectl apply | Full production |

| Multi-use | Yes | Yes (Windows + multiple pods) | Usually exclusive |

Step 0: Confirm paravirtualization is live

In your WSL Ubuntu terminal, run:

nvidia-smi

Paste the output (or any error) right here — this confirms the stub libraries are working before we touch Docker/kind.

Step 1: Install Docker + NVIDIA Container Toolkit + kind (~5 commands)

Once nvidia-smi looks good, copy-paste these in order:

# 1. Install Docker

sudo apt-get update && sudo apt-get install -y docker.io


# 2. NVIDIA Container Toolkit repo + install

distribution=$(. /etc/os-release;echo $ID$VERSION_ID) && \

curl -s -L https://nvidia.github.io/nvidia-container-toolkit/libnvidia-container/gpgkey | sudo apt-key add - && \

curl -s -L https://nvidia.github.io/nvidia-container-toolkit/$distribution/nvidia-container-toolkit.list | sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list && \

sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit


# 3. Configure Docker to use NVIDIA runtime

sudo nvidia-ctk runtime configure --runtime=docker && \

sudo systemctl restart docker


# 4. Test GPU in Docker (should show your RTX 4070 Super)

docker run --rm --runtime=nvidia --gpus all nvcr.io/nvidia/cuda:12.6.0-base-ubuntu22.04 nvidia-smi


# 5. Install kind + kubectl

curl -Lo ./kind https://kind.sigs.k8s.io/dl/v0.24.0/kind-linux-amd64 && chmod +x ./kind && sudo mv ./kind /usr/local/bin/ && \

curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/linux/amd64/kubectl" && chmod +x kubectl && sudo mv kubectl /usr/local/bin/

Step 2: Create the GPU-enabled kind cluster + deploy your repo

kind create cluster --name sterl-cluster --config - <<EOF

kind: Cluster

apiVersion: kind.x-k8s.io/v1alpha4

nodes:

- role: control-plane

- role: worker

EOF


# Install NVIDIA device plugin so Kubernetes sees the GPU

kubectl apply -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/main/deployments/static/nvidia-device-plugin.yml


# Clone your repo and deploy (we'll patch the AI Engine deployment for GPU in one extra line)

git clone https://github.com/sterl27/cluster.git && cd cluster

# (optional quick GPU patch if needed — I can give you the exact sed command once we see your deployment.yaml)

kubectl apply -f k8s/deployment.yaml

Your AI Engine pods will now request and get the GPU. We can then deploy the Nemotron model via NVIDIA NIM or custom inference pod right on top.

Paste the nvidia-smi result (or the output of any step above if you hit a snag) and we’ll confirm everything, tweak your k8s/ manifests for the AI Engine, and have a running GPU-accelerated local K8s cluster with your exact repo in under 10 minutes.

We’re minutes away from a fully GPU-powered sterl-cluster with Nemotron running in the AI Engine node! 🚀

Ready when you are — drop that nvidia-smi output! 🎉

Gemini said To transform your WSL2 Ubuntu instance into a GPU-accelerated node for your cluster repository, follow these refined instructions. This setup specifically ensures your AI Engine can leverage the RTX 4070 Super through the paravirtualized /dev/dxg interface.

Step 0: Confirm Paravirtualization Run nvidia-smi in your WSL2 terminal. You should see your RTX 4070 Super listed. If you see an error like "couldn't communicate with the driver," ensure you have the latest NVIDIA Windows Drivers installed on the host (WSL2 uses these directly).

Step 1: Install Docker & NVIDIA Container Toolkit The toolkit is the "bridge" that allows Docker containers (and eventually Kubernetes pods) to see the GPU.

Install Docker CE:

Bash sudo apt-get update && sudo apt-get install -y docker.io sudo usermod -aG docker $USER && newgrp docker # Run without sudo Add NVIDIA Container Toolkit Repository:

Bash curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list |
sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' |
sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit Configure Docker Runtime:

Bash sudo nvidia-ctk runtime configure --runtime=docker sudo systemctl restart docker Step 2: Initialize kind with GPU Support kind (Kubernetes IN Docker) runs nodes as containers. We must ensure the worker node is configured to use the NVIDIA runtime.

Install kind & kubectl:

Bash curl -Lo ./kind https://kind.sigs.k8s.io/dl/v0.24.0/kind-linux-amd64 && chmod +x ./kind && sudo mv ./kind /usr/local/bin/ Create Cluster Config (kind-gpu.yaml):

YAML kind: Cluster apiVersion: kind.x-k8s.io/v1alpha4 nodes:

• role: control-plane
• role: worker extraMounts:
  • hostPath: /dev/dxg containerPath: /dev/dxg Launch Cluster:

Bash kind create cluster --name cluster-gpu --config kind-gpu.yaml Step 3: Enable GPU Discovery in Kubernetes Install the NVIDIA Device Plugin so the cluster recognizes the GPU as a schedulable resource.

Bash kubectl apply -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/main/deployments/static/nvidia-device-plugin.yml Step 4: Deploy the AI Engine Navigate to your cloned cluster directory. Update your k8s/deployment.yaml for the AI Engine service to include the GPU resource request:

YAML spec: containers:

• name: ai-engine image: sterl27/cluster-ai-engine:latest resources: limits: nvidia.com/gpu: 1 # Requests 1 GPU Apply the manifests:

Bash kubectl apply -f k8s/deployment.yaml Once deployed, your pods will have near-native CUDA performance (90-98%) for tasks like the S73RL Beat Gen or Musaix Analysis while keeping your Windows host stable.

WSL2 GPU paravirtualization + kind + NVIDIA Container Toolkit is the exact stack that turns your fresh Ubuntu WSL instance into a full GPU-accelerated local Kubernetes cluster — perfect for running the AI Engine node from your new https://github.com/sterl27/cluster repo.

Your repo already has the modular architecture (Gateway + AI Engine with S73RL Beat Gen/Musaix Analysis + Data Store), Docker support, and k8s/deployment.yaml manifests. GPU-PV + NVIDIA Container Toolkit + kind gives the AI Engine direct access to your RTX 4070 Super for CUDA workloads (including the Nemotron model we talked about) without touching the Windows host gaming/desktop performance.

It's not a full VM or bare-metal K8s — it's still the same lightweight Hyper-V shim we covered in the report you just saved as PDF. The paravirtualized /dev/dxg + stub libraries stay exactly the same; we just layer Docker runtime + kind on top so your Kubernetes pods see nvidia.com/gpu: 1 resources.

1. GPU-PV foundation (already covered in your report)

   • Windows NVIDIA driver → /dev/dxg inside WSL → stub CUDA libs → real RTX 4070 Super.

2. Docker + NVIDIA Container Toolkit bridge

   • Install Docker CE in Ubuntu WSL.

   • NVIDIA Container Toolkit adds the nvidia runtimeClass and CDI config that automatically injects the GPU into any container (or pod) that requests it.

   • When kind spins up nodes (as Docker containers), the toolkit passes the paravirtualized GPU straight through.

   • Your AI Engine pods simply declare resources: limits: nvidia.com/gpu: 1 and get full CUDA access.

3. kind (Kubernetes IN Docker) layer

   • kind runs a full K8s control plane + worker nodes inside Docker containers in WSL.

   • With the toolkit configured as default runtime, every pod sees the GPU exactly like native Kubernetes on Linux.

Performance stays near-native (same 90-98% you saw in the PDF comparison table). Your Windows desktop and gaming stay 100% unaffected.

| Aspect | WSL2 GPU-PV alone | + Docker + NVIDIA Toolkit + kind | Traditional bare-metal K8s |

|-------------------------|------------------------------------|-------------------------------------------|----------------------------|

| GPU Ownership | Shared with Windows | Shared with Windows + all kind pods | Dedicated hardware |

| Setup Time | Driver only | ~5 commands after nvidia-smi | Hours + hardware |

| Your cluster repo fit | nvidia-smi works | AI Engine pods get GPU + kubectl apply | Full production |

| Multi-use | Yes | Yes (Windows + multiple pods) | Usually exclusive |

Step 0: Confirm paravirtualization is live

In your WSL Ubuntu terminal, run:

nvidia-smi

Paste the output (or any error) right here — this confirms the stub libraries are working before we touch Docker/kind.

Step 1: Install Docker + NVIDIA Container Toolkit + kind (~5 commands)

Once nvidia-smi looks good, copy-paste these in order:

# 1. Install Docker

sudo apt-get update && sudo apt-get install -y docker.io


# 2. NVIDIA Container Toolkit repo + install

distribution=$(. /etc/os-release;echo $ID$VERSION_ID) && \

curl -s -L https://nvidia.github.io/nvidia-container-toolkit/libnvidia-container/gpgkey | sudo apt-key add - && \

curl -s -L https://nvidia.github.io/nvidia-container-toolkit/$distribution/nvidia-container-toolkit.list | sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list && \

sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit


# 3. Configure Docker to use NVIDIA runtime

sudo nvidia-ctk runtime configure --runtime=docker && \

sudo systemctl restart docker


# 4. Test GPU in Docker (should show your RTX 4070 Super)

docker run --rm --runtime=nvidia --gpus all nvcr.io/nvidia/cuda:12.6.0-base-ubuntu22.04 nvidia-smi


# 5. Install kind + kubectl

curl -Lo ./kind https://kind.sigs.k8s.io/dl/v0.24.0/kind-linux-amd64 && chmod +x ./kind && sudo mv ./kind /usr/local/bin/ && \

curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/linux/amd64/kubectl" && chmod +x kubectl && sudo mv kubectl /usr/local/bin/

Step 2: Create the GPU-enabled kind cluster + deploy your repo

kind create cluster --name sterl-cluster --config - <<EOF

kind: Cluster

apiVersion: kind.x-k8s.io/v1alpha4

nodes:

- role: control-plane

- role: worker

EOF


# Install NVIDIA device plugin so Kubernetes sees the GPU

kubectl apply -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/main/deployments/static/nvidia-device-plugin.yml


# Clone your repo and deploy (we'll patch the AI Engine deployment for GPU in one extra line)

git clone https://github.com/sterl27/cluster.git && cd cluster

# (optional quick GPU patch if needed — I can give you the exact sed command once we see your deployment.yaml)

kubectl apply -f k8s/deployment.yaml

Your AI Engine pods will now request and get the GPU. We can then deploy the Nemotron model via NVIDIA NIM or custom inference pod right on top.

Paste the nvidia-smi result (or the output of any step above if you hit a snag) and we’ll confirm everything, tweak your k8s/ manifests for the AI Engine, and have a running GPU-accelerated local K8s cluster with your exact repo in under 10 minutes.

We’re minutes away from a fully GPU-powered sterl-cluster with Nemotron running in the AI Engine node! 🚀

Ready when you are — drop that nvidia-smi output! 🎉

Gemini said To continue with your cluster setup, run nvidia-smi in your WSL2 terminal and paste the output here to verify that the GPU is being passed through from your Windows host. Once paravirtualization is confirmed, we can proceed with the five-command installation to bridge the NVIDIA Container Toolkit into kind.

For the other two nodes in your 3-node architecture, make sure they are accessible via SSH so the gateway can route traffic correctly once the AI Engine is live on your GPU node.

fucking gangsta list steps

Gemini said To set up your 3-node cluster with one GPU-accelerated WSL2 node and two Ubuntu servers, follow these steps:

Phase 1: Configure the GPU Node (WSL2) This node will act as your AI Engine using your RTX 4070 Super.

Verify GPU Passthrough: In your WSL2 Ubuntu terminal, run nvidia-smi. You should see your GPU details. If not, install the latest Windows NVIDIA drivers.

Install Docker & NVIDIA Container Toolkit:

Bash sudo apt-get update && sudo apt-get install -y docker.io curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit sudo nvidia-ctk runtime configure --runtime=docker && sudo systemctl restart docker Setup Kubernetes (kind): Install kind and create a cluster with the /dev/dxg device mounted for GPU access.

Bash kind create cluster --name cluster-gpu --config - <<EOF kind: Cluster apiVersion: kind.x-k8s.io/v1alpha4 nodes:

• role: worker extraMounts:
  • hostPath: /dev/dxg containerPath: /dev/dxg EOF kubectl apply -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/main/deployments/static/nvidia-device-plugin.yml Phase 2: Configure the Ubuntu Servers (Remote Nodes) These nodes will handle the Gateway and Data Store.

Assign Static IPs: Edit /etc/netplan/01-netcfg.yaml on each server to ensure they have persistent IPs (e.g., 192.168.1.101 and 103).

Enable SSH Access: On your WSL2 node, generate a key and copy it to the other nodes:

Bash ssh-keygen -t rsa -b 4096 ssh-copy-id user@192.168.1.101 ssh-copy-id user@192.168.1.103 Phase 3: Deploy Cluster Services Use the automated setup scripts from your repository to provision each node.

Node 1 (Gateway): Run on the first Ubuntu server. bash scripts/setup-node.sh gateway 192.168.1.101

Node 2 (AI Engine): Run on your WSL2 node. bash scripts/setup-node.sh ai-engine

Node 3 (Data Store): Run on the second Ubuntu server. bash scripts/setup-node.sh data-store 192.168.1.103

Phase 4: Optimization & Verification Kernel Tuning: Run sudo bash scripts/kernel-tuning.sh on the two standalone Ubuntu servers to reduce latency.

Health Check: Access http://:3000/health to verify that all nodes are communicating via the ClusterNodeRegistry.

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