KhulnaSoft AI Compute Grid - Deploy

.github/workflows/deploy.yml

@@ -0,0 +1,52 @@
+name: KhulnaSoft AI Compute Grid - Deploy
+
+on:
+  push:
+    branches: [ main ]
+    paths:
+      - 'platform/**'
+      - 'runtime/**'
+      - 'infra/k8s/**'
+  pull_request:
+    branches: [ main ]
+
+jobs:
+  build-and-push:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v2
+
+      - name: Login to DockerHub
+        uses: docker/login-action@v2
+        with:
+          username: ${{ secrets.DOCKERHUB_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_TOKEN }}
+
+      - name: Build and push AI Worker Image
+        uses: docker/build-push-action@v4
+        with:
+          context: .
+          file: runtime/images/worker.Dockerfile
+          push: true
+          tags: khulnasoft/ai-worker:latest
+
+  deploy-to-k8s:
+    needs: build-and-push
+    runs-on: ubuntu-latest
+    if: github.ref == 'refs/heads/main'
+    steps:
+      - uses: actions/checkout@v3
+
+      - name: Set up Kubeconfig
+        run: |
+          mkdir -p ~/.kube
+          echo "${{ secrets.KUBECONFIG }}" | base64 -d > ~/.kube/config
+
+      - name: Deploy Infrastructure
+        run: |
+          kubectl apply -f infra/k8s/gpu/device-plugin.yaml
+          kubectl apply -f infra/k8s/ray/head.yaml
+          kubectl apply -f infra/k8s/ray/worker.yaml

README.md

@@ -1,81 +1,71 @@
-# CtxPod - AI-Powered IDE Platform
+# 🚀 KhulnaSoft AI Compute Grid
 
-A Gitpod-like development environment with autonomous AI agents that can plan, code, test, debug, and deploy automatically.
+A production-grade deployment pack for a hybrid GPU cluster (local + cloud) tailored for AI-native platforms and distributed pentesting pipelines.
 
-## 🚀 Quick Start
+## 🌟 Features
 
-### Prerequisites
-- Docker and Docker Compose
-- Node.js 16+
-- OpenAI API key (for AI agents)
+- **Hybrid GPU Scaling**: Routes jobs between local and cloud clusters based on availability and priority.
+- **Ray-Based Compute**: Leverages [Ray](https://ray.io) for seamless distributed Python execution.
+- **K8s & GPU Ready**: Includes manifests for NVIDIA Device Plugin and GPU-aware worker deployments.
+- **Secure by Default**: Example security contexts and isolated runtime configurations.
+- **Smart Control Plane**: FastAPI-based API with Redis job tracking and status monitoring.
 
-### Installation
+## 🏗️ Repository Structure
 
-1. Clone and setup:
 ```bash
-cp .env.example .env
-# Edit .env with your OpenAI key
+infra/
+  terraform/cloud/    # AWS/Cloud GPU provisioning
+  ansible/            # Node bootstrapping (Docker, NVIDIA drivers)
+  k8s/                # K8s manifests (GPU plugin, Ray cluster)
+platform/
+  api/                # FastAPI job submission API
+  scheduler/          # Smart routing logic
+runtime/
+  ray/                # AI task definitions
+agents/               # Example client implementations
 ```
 
-2. Build workspace image:
+## 🚀 Quick Start
+
+### 1. Provision Cloud Infrastructure
 ```bash
-npm run build:docker
+cd infra/terraform/cloud
+terraform init
+terraform apply
 ```
 
-3. Start the platform:
+### 2. Bootstrap Nodes (Local or Cloud)
 ```bash
-npm run docker:run
+cd infra/ansible
+ansible-playbook -i inventory.ini gpu-node.yml
 ```
 
-4. Create a workspace:
+### 3. Deploy Kubernetes Cluster
 ```bash
-curl -X POST http://localhost:3000/workspace
+kubectl apply -f infra/k8s/gpu/device-plugin.yaml
+kubectl apply -f infra/k8s/ray/head.yaml
+kubectl apply -f infra/k8s/ray/worker.yaml
 ```
 
-## 📋 Architecture
-
-- **Control Server** (Node.js) - Manages workspaces and orchestrates agents
-- **code-server** - Browser-based VS Code IDE
-- **Docker** - Isolated workspace containers
-- **Redis + BullMQ** - Queue-based agent system
-- **AI Agents** - Autonomous coding agents
-
-## 🛠️ Development
-
+### 4. Run the Platform
 ```bash
-# Start control server only
-npm start
+# Start API (Port 8000)
+uvicorn platform.api.main:app --host 0.0.0.0
 
-# Start with Docker Compose (includes Redis)
-npm run docker:run
-
-# Build Docker workspace image
-npm run build:docker
+# Start Scheduler
+python platform/scheduler/scheduler.py
 ```
 
-## 📚 API Endpoints
-
-- `POST /workspace` - Create new workspace
-- `GET /workspaces` - List all workspaces  
-- `GET /workspace/:id` - Get workspace details
-- `DELETE /workspace/:id` - Stop workspace
-
-## 🤖 AI Agents
-
-Coming soon:
-- **Planner Agent** - Breaks tasks into steps
-- **Coder Agent** - Writes production code
-- **Tester Agent** - Runs automated tests
-- **Debugger Agent** - Fixes errors
-- **Deployer Agent** - Ships to production
+### 5. Submit a Job
+```bash
+python agents/example_agent.py
+```
 
-## 🔧 Configuration
+## 🔐 Security & Observability
 
-Edit `.env` for:
-- OpenAI API key
-- Redis connection
-- Server settings
+- **Sandboxing**: Pods are configured with restricted security contexts. Advanced isolation via gVisor is recommended for multi-tenant environments.
+- **Monitoring**: Integration with Prometheus & Grafana is supported via Ray's native exporters.
 
-## 📄 License
+## 🤝 Contributing
 
-MIT
+This is a production blueprint. Please adapt configurations (AMIs, regions, instance types) to your specific requirements before final deployment.

agents/example_agent.py

@@ -0,0 +1,48 @@
+import requests
+import time
+import json
+import logging
+
+# Setup logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger("AIAgent")
+
+API_URL = "http://localhost:8000"
+
+def submit_and_wait(task_type, params):
+    payload = {
+        "task_type": task_type,
+        "params": params,
+        "priority": 1
+    }
+
+    try:
+        # Submit job
+        response = requests.post(f"{API_URL}/submit", json=payload)
+        response.raise_for_status()
+        job_id = response.json()["job_id"]
+        logger.info(f"Submitted {task_type} job. ID: {job_id}")
+
+        # Poll for status
+        while True:
+            status_resp = requests.get(f"{API_URL}/job/{job_id}")
+            status_resp.raise_for_status()
+            job_data = status_resp.json()
+
+            status = job_data.get("status")
+            logger.info(f"Job {job_id} status: {status}")
+
+            if "running" in status or "completed" in status:
+                return job_data
+
+            time.sleep(2)
+
+    except Exception as e:
+        logger.error(f"Error communicating with Grid API: {str(e)}")
+        return None
+
+if __name__ == "__main__":
+    logger.info("Starting AI Agent example...")
+    result = submit_and_wait("CVE-Scanner", {"target": "khulnasoft.com", "depth": "advanced"})
+    if result:
+        print(f"Final Job State: {json.dumps(result, indent=2)}")

agents/types/base_agent.py

@@ -0,0 +1,30 @@
+import abc
+import logging
+from typing import Dict, Any
+
+logger = logging.getLogger("GridAgent")
+
+class Agent(abc.ABC):
+    """
+    Formalized Base Agent for the multi-agent orchestration system.
+    Every agent must implement the 'run' method with input_data and context.
+    """
+    def __init__(self, agent_id: str, role: str, config: Dict[str, Any] = None):
+        self.id = agent_id
+        self.role = role
+        self.config = config or {}
+        self.status = "idle"
+
+    @abc.abstractmethod
+    def run(self, input_data: Any, context: Dict[str, Any] = None) -> Dict[str, Any]:
+        """
+        Main execution logic.
+        """
+        pass
+
+    def log_decision(self, action: str, rationale: str, data: Any = None):
+        """
+        Helper to record autonomous decisions for the audit trail.
+        """
+        logger.info(f"[{self.role}] ACTION: {action} | RATIONALE: {rationale}")
+        # In production, this would call the platform/audit/logger.py

agents/types/specialized.py

@@ -0,0 +1,45 @@
+from typing import Dict, Any, List
+from agents.types.base_agent import Agent
+
+class PlannerAgent(Agent):
+    """
+    Agent specialized in decomposing complex goals into DSL workflows.
+    """
+    def __init__(self, agent_id: str, config: Dict[str, Any] = None):
+        super().__init__(agent_id, "Planner", config)
+
+    def run(self, goal: Any, context: Dict[str, Any] = None) -> Dict[str, Any]:
+        self.log_decision("Generate Workflow", f"Decomposing goal: {goal}")
+        # Simplified mock logic - would call workflow_generator.py
+        return {
+            "type": "autonomous_scan",
+            "workflow": [
+                {"name": "Recon", "agent": "recon", "params": {"target": goal}}
+            ]
+        }
+
+class CriticAgent(Agent):
+    """
+    Agent specialized in safety audit and efficiency review.
+    """
+    def __init__(self, agent_id: str, config: Dict[str, Any] = None):
+        super().__init__(agent_id, "Critic", config)
+
+    def run(self, plan: Any, context: Dict[str, Any] = None) -> Dict[str, Any]:
+        self.log_decision("Audit Plan", "Reviewing proposed workflow for safety breaches.")
+        # Logic to check for blocked agents or dangerous commands
+        return {"approved": True, "score": 0.95}
+
+class ObserverAgent(Agent):
+    """
+    Agent specialized in real-time execution monitoring and anomaly detection.
+    """
+    def __init__(self, agent_id: str, config: Dict[str, Any] = None):
+        super().__init__(agent_id, "Observer", config)
+
+    def run(self, job_telemetry: Any, context: Dict[str, Any] = None) -> Dict[str, Any]:
+        runtime = job_telemetry.get("runtime", 0)
+        if runtime > 600:
+             self.log_decision("Alert Anomaly", f"Job runtime ({runtime}s) exceeds threshold.")
+             return {"anomaly": True, "action": "Kill"}
+        return {"anomaly": False}

infra/ansible/gpu-node.yml

@@ -0,0 +1,54 @@
+---
+- name: Bootstrap KhulnaSoft AI GPU Node
+  hosts: gpu_nodes
+  become: yes
+  tasks:
+    - name: Update apt cache
+      apt:
+        update_cache: yes
+
+    - name: Install baseline dependencies
+      apt:
+        name:
+          - curl
+          - ca-certificates
+          - gnupg
+          - lsb-release
+        state: present
+
+    - name: Install Docker
+      apt:
+        name: docker.io
+        state: present
+
+    - name: Install NVIDIA drivers
+      apt:
+        name: nvidia-driver-535
+        state: present
+
+    - name: Install NVIDIA Container Toolkit
+      shell: |
+        curl -s -L https://nvidia.github.io/libnvidia-container/gpgkey | apt-key add -
+        curl -s -L https://nvidia.github.io/libnvidia-container/$(. /etc/os-release;echo $ID$VERSION_ID)/libnvidia-container.list | tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
+        apt-get update && apt-get install -y nvidia-container-toolkit
+      args:
+        creates: /usr/bin/nvidia-container-toolkit
+
+    - name: Configure Docker to use NVIDIA runtime
+      copy:
+        content: |
+          {
+              "default-runtime": "nvidia",
+              "runtimes": {
+                  "nvidia": {
+                      "path": "nvidia-container-runtime",
+                      "runtimeArgs": []
+                  }
+              }
+          }
+        dest: /etc/docker/daemon.json
+
+    - name: Restart Docker
+      service:
+        name: docker
+        state: restarted