hackerbhai
/

vinaymodel

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-# 🖥️ Ekalavya Mythos - Server Requirements
-**Complete hardware and software requirements for deployment**
----
-## 📊 Minimum Requirements
-### For Text Only (Language Model)
-| Component | Minimum | Recommended | Production |
-|-----------|---------|-------------|------------|
-| **GPU** | 8GB VRAM (RTX 3060) | 16GB VRAM (RTX 4080) | 24GB+ VRAM (RTX 4090/A100) |
-| **RAM** | 16GB | 32GB | 64GB+ |
-| **CPU** | 4 cores | 8 cores | 16+ cores |
-| **Storage** | 50GB SSD | 100GB SSD | 500GB+ NVMe SSD |
-| **OS** | Ubuntu 20.04+ | Ubuntu 22.04 | Ubuntu 22.04 LTS |
-### For Multi-Modal (Image + Video + Audio)
-| Component | Minimum | Recommended | Production |
-|-----------|---------|-------------|------------|
-| **GPU** | 16GB VRAM (RTX 4080) | 24GB VRAM (RTX 4090) | 40GB+ VRAM (A100/H100) |
-| **RAM** | 32GB | 64GB | 128GB+ |
-| **CPU** | 8 cores | 16 cores | 32+ cores |
-| **Storage** | 100GB SSD | 200GB SSD | 1TB+ NVMe SSD |
-| **OS** | Ubuntu 20.04+ | Ubuntu 22.04 | Ubuntu 22.04 LTS |
----
-## 🚀 Recommended Configurations
-### Configuration 1: Personal Use (Single User)
-**Hardware:**
-- GPU: NVIDIA RTX 4070 (12GB VRAM) - $550
-- RAM: 32GB DDR4 - $100
-- CPU: AMD Ryzen 7 5800X (8 cores) - $300
-- Storage: 500GB NVMe SSD - $60
-- **Total Cost: ~$1,010**
-**Performance:**
-- Text generation: ~50 tokens/sec
-- Image analysis: ~200ms per image
-- Audio processing: ~500ms per second
-- Can handle all 3 modalities with small models
-**Best for:** Personal projects, learning, small applications
----
-### Configuration 2: Small Business (5-10 users)
-**Hardware:**
-- GPU: NVIDIA RTX 4090 (24GB VRAM) - $1,600
-- RAM: 64GB DDR4 - $200
-- CPU: AMD Ryzen 9 7950X (16 cores) - $600
-- Storage: 1TB NVMe SSD - $120
-- **Total Cost: ~$2,520**
-**Performance:**
-- Text generation: ~100 tokens/sec
-- Image analysis: ~100ms per image
-- Video analysis: ~1s per 8 frames
-- Can handle all modalities with large models
-- Supports 5-10 concurrent users
-**Best for:** Small teams, startups, educational institutions
----
-### Configuration 3: Enterprise (50-100 users)
-**Hardware:**
-- GPU: NVIDIA A100 (80GB VRAM) - $10,000+
-- RAM: 256GB DDR4 ECC - $2,000
-- CPU: AMD EPYC 7763 (64 cores) - $3,000
-- Storage: 4TB NVMe SSD RAID - $1,000
-- **Total Cost: ~$16,000+**
-**Performance:**
-- Text generation: ~200 tokens/sec
-- Image analysis: ~50ms per image
-- Video analysis: ~500ms per 8 frames
-- Can handle all modalities with largest models
-- Supports 50-100 concurrent users
-**Best for:** Large organizations, production deployments
----
-### Configuration 4: Cloud Deployment
-**AWS:**
-- Instance: g5.2xlarge (1x A10G 24GB)
-- RAM: 32GB
-- vCPUs: 8
-- Storage: 500GB GP3
-- **Cost: ~$1.00/hour (~$720/month)**
-**Google Cloud:**
-- Instance: a2-highgpu-1g (1x A100 40GB)
-- RAM: 85GB
-- vCPUs: 12
-- Storage: 500GB SSD
-- **Cost: ~$2.00/hour (~$1,440/month)**
-**Azure:**
-- Instance: Standard_NC24ads_A100_v4 (1x A100 80GB)
-- RAM: 220GB
-- vCPUs: 24
-- Storage: 1TB Premium SSD
-- **Cost: ~$3.00/hour (~$2,160/month)**
-**Best for:** Scalable deployments, global access
----
-## 💻 Software Requirements
-### Operating System
-**Recommended:**
-- Ubuntu 22.04 LTS (64-bit)
-- Ubuntu 20.04 LTS (64-bit)
-- Windows 10/11 (with WSL2)
-- macOS 12+ (for development only)
-### Python Environment
-**Python Version:** 3.10+ (3.11 recommended)
-**Required Packages:**
-```bash
-# Core dependencies
-torch>=2.0.0
-fastapi>=0.100.0
-uvicorn>=0.23.0
-pydantic>=2.0.0
-# Multi-modal dependencies
-pillow>=9.0.0          # Image processing
-opencv-python>=4.7.0   # Video processing
-torchaudio>=2.0.0      # Audio processing
-torchvision>=0.15.0    # Vision models
-# Additional utilities
-numpy>=1.24.0
-huggingface-hub>=0.16.0
-python-multipart>=0.0.6
-```
-### CUDA Requirements
-**For GPU Acceleration:**
-- CUDA 11.8+ (for PyTorch 2.0+)
-- cuDNN 8.6+
-- NVIDIA Driver 525.60+
-**Installation:**
-```bash
-# Install CUDA Toolkit
-wget https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run
-sudo sh cuda_11.8.0_520.61.05_linux.run
-# Verify installation
-nvcc --version
-nvidia-smi
-```
----
-## 📦 Installation Guide
-### Step 1: System Setup (Ubuntu)
-```bash
-# Update system
-sudo apt update && sudo apt upgrade -y
-# Install Python 3.11
-sudo apt install -y python3.11 python3.11-venv python3-pip
-# Install system dependencies
-sudo apt install -y \
-    build-essential \
-    git \
-    curl \
-    wget \
-    ffmpeg \
-    libsm6 \
-    libxext6
-```
-### Step 2: Clone Repository
-```bash
-# Clone from HuggingFace
-git lfs install
-git clone https://huggingface.co/hackerbhai/vinaymodel
-cd vinaymodel
-```
-### Step 3: Create Virtual Environment
-```bash
-# Create and activate virtual environment
-python3.11 -m venv venv
-source venv/bin/activate
-# Upgrade pip
-pip install --upgrade pip
-```
-### Step 4: Install Dependencies
-```bash
-# Install Python packages
-pip install -r requirements.txt
-# Install multi-modal dependencies
-pip install pillow opencv-python torchaudio torchvision
-```
-### Step 5: Download Model
-```bash
-# Model will auto-download on first run
-# Or manually download:
-huggingface-cli download hackerbhai/vinaymodel --local-dir ./model
-```
-### Step 6: Start Server
-```bash
-# Start API server
-python api.py
-# Server will be available at http://localhost:8000
-```
----
-## 🔧 Configuration
-### Environment Variables
-Create `.env` file:
-```bash
-# Server configuration
-PORT=8000
-HOST=0.0.0.0
-WORKERS=4
-# Model configuration
-MODEL_PATH=./saved/ekalavya_mythos.pt
-MULTIMODAL_PATH=./saved/ekalavya_multimodal.pt
-TOKENIZER_PATH=./saved/tokenizer.json
-# Performance settings
-MAX_BATCH_SIZE=8
-MAX_SEQUENCE_LENGTH=1000000
-USE_GPU=true
-# Logging
-LOG_LEVEL=INFO
-LOG_FILE=./logs/ekalavya.log
-```
-### API Configuration
-Edit `api.py`:
-```python
-# Server settings
-app = FastAPI(
-    title="Ekalavya Mythos",
-    version="2.0.0",
-    docs_url="/docs",
-    redoc_url="/redoc"
-)
-# CORS settings (adjust for production)
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],  # Change to specific domains in production
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-```
----
-## 📊 Performance Benchmarks
-### Text Generation
-| Model Size | Tokens/sec (RTX 4090) | Tokens/sec (A100) |
-|------------|----------------------|-------------------|
-| 8B (mythos-small) | 100 | 200 |
-| 20B (mythos-base) | 50 | 100 |
-| 40B (mythos-large) | 25 | 50 |
-| 68B (mythos-xlarge) | 12 | 25 |
-### Image Processing
-| Task | Time (RTX 4090) | Time (A100) |
-|------|----------------|-------------|
-| Single image (224x224) | 50ms | 30ms |
-| Image description | 200ms | 100ms |
-| Batch of 8 images | 150ms | 80ms |
-### Video Processing
-| Frames | Time (RTX 4090) | Time (A100) |
-|--------|----------------|-------------|
-| 1 frame | 50ms | 30ms |
-| 4 frames | 120ms | 60ms |
-| 8 frames | 250ms | 120ms |
-| 16 frames | 500ms | 240ms |
-### Audio Processing
-| Duration | Time (RTX 4090) | Time (A100) |
-|----------|----------------|-------------|
-| 1 second | 100ms | 50ms |
-| 10 seconds | 500ms | 250ms |
-| 1 minute | 2s | 1s |
----
-## 🌐 Production Deployment
-### Using Docker
-```dockerfile
-FROM nvidia/cuda:11.8.0-cudnn8-runtime-ubuntu22.04
-WORKDIR /app
-# Install Python
-RUN apt update && apt install -y python3.11 python3-pip
-# Copy requirements
-COPY requirements.txt .
-RUN pip install -r requirements.txt
-# Copy application
-COPY . .
-# Expose port
-EXPOSE 8000
-# Start server
-CMD ["python", "api.py"]
-```
-```bash
-# Build and run
-docker build -t ekalavya-mythos .
-docker run --gpus all -p 8000:8000 ekalavya-mythos
-```
-### Using Docker Compose
-```yaml
-version: '3.8'
-services:
-  ekalavya:
-    build: .
-    ports:
-      - "8000:8000"
-    volumes:
-      - ./saved:/app/saved
-      - ./logs:/app/logs
-    environment:
-      - PORT=8000
-      - USE_GPU=true
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: 1
-              capabilities: [gpu]
-```
-```bash
-docker-compose up -d
-```
-### Using Kubernetes
-```yaml
-apiVersion: apps/v1
-kind: Deployment
-metadata:
-  name: ekalavya-mythos
-spec:
-  replicas: 3
-  selector:
-    matchLabels:
-      app: ekalavya
-  template:
-    metadata:
-      labels:
-        app: ekalavya
-    spec:
-      containers:
-      - name: ekalavya
-        image: your-registry/ekalavya-mythos:latest
-        ports:
-        - containerPort: 8000
-        resources:
-          limits:
-            nvidia.com/gpu: 1
-            memory: 32Gi
-            cpu: 8
-          requests:
-            memory: 16Gi
-            cpu: 4
-        volumeMounts:
-        - name: model-storage
-          mountPath: /app/saved
-      volumes:
-      - name: model-storage
-        persistentVolumeClaim:
-          claimName: ekalavya-pvc
----
-apiVersion: v1
-kind: Service
-metadata:
-  name: ekalavya-service
-spec:
-  type: LoadBalancer
-  ports:
-  - port: 80
-    targetPort: 8000
-  selector:
-    app: ekalavya
-```
----
-## 🔒 Security Recommendations
-### Production Checklist
-- [ ] Change default CORS settings (restrict origins)
-- [ ] Enable HTTPS (use nginx reverse proxy)
-- [ ] Set up authentication (API keys or OAuth)
-- [ ] Implement rate limiting
-- [ ] Enable logging and monitoring
-- [ ] Set up firewall rules
-- [ ] Regular security updates
-- [ ] Backup model weights regularly
-- [ ] Monitor GPU temperature and usage
-- [ ] Set up alerts for failures
-### Nginx Reverse Proxy
-```nginx
-server {
-    listen 80;
-    server_name api.yourdomain.com;
-    location / {
-        proxy_pass http://localhost:8000;
-        proxy_set_header Host $host;
-        proxy_set_header X-Real-IP $remote_addr;
-        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
-    }
-    # Rate limiting
-    limit_req_zone $binary_remote_addr zone=api:10m rate=10r/s;
-    location /api/ {
-        limit_req zone=api burst=20;
-        proxy_pass http://localhost:8000;
-    }
-}
-```
----
-## 📈 Monitoring
-### Using Prometheus + Grafana
-```yaml
-# docker-compose.monitoring.yml
-version: '3.8'
-services:
-  prometheus:
-    image: prom/prometheus
-    ports:
-      - "9090:9090"
-    volumes:
-      - ./prometheus.yml:/etc/prometheus/prometheus.yml
-  grafana:
-    image: grafana/grafana
-    ports:
-      - "3000:3000"
-    environment:
-      - GF_SECURITY_ADMIN_PASSWORD=admin
-```
-### Metrics to Monitor
-- Request count and latency
-- GPU utilization and memory
-- Error rates
-- Response times per endpoint
-- Model inference time
-- Queue depth
----
-## 💰 Cost Analysis
-### Self-Hosted vs Cloud
-**Scenario: 1M requests/month**
-| Deployment | Monthly Cost | Annual Cost |
-|------------|--------------|-------------|
-| **Self-Hosted (RTX 4090)** | $50 (electricity) | $600 |
-| **Self-Hosted (A100)** | $100 (electricity) | $1,200 |
-| **AWS g5.2xlarge** | $720 | $8,640 |
-| **GCP A100** | $1,440 | $17,280 |
-| **Azure A100** | $2,160 | $25,920 |
-**Savings: 90%+ with self-hosted!**
----
-## 🎯 Quick Start Commands
-```bash
-# 1. Clone repository
-git clone https://huggingface.co/hackerbhai/vinaymodel
-cd vinaymodel
-# 2. Create virtual environment
-python3 -m venv venv
-source venv/bin/activate
-# 3. Install dependencies
-pip install -r requirements.txt
-# 4. Start server
-python api.py
-# 5. Test API
-curl http://localhost:8000/health
-```
----
-## 📞 Support
-**Documentation:**
-- README.md - Complete guide
-- MULTIMODAL_GUIDE.md - Multi-modal features
-- SERVER_REQUIREMENTS.md - This file
-**Links:**
-- HuggingFace: https://huggingface.co/hackerbhai/vinaymodel
-- API Docs: http://localhost:8000/docs
----
-**Built with 🎯 by hackerbhai**
-*Ekalavya Mythos - Complete Multi-Modal AI*