Installation

Complete guide to installing NeuroShard on different platforms.

System Requirements

Minimum Requirements

Component	Minimum	Recommended
RAM	2GB	8GB+
Storage	1GB	10GB+
CPU	2 cores	4+ cores
Python	3.10	3.11
Network	10 Mbps	100 Mbps+

GPU Support

NeuroShard automatically detects and uses available GPUs:

GPU	Support Level
NVIDIA CUDA	✅ Full support (recommended)
Apple Metal (M1/M2/M3)	✅ Full support
AMD ROCm	⚠️ Experimental
CPU Only	✅ Supported (slower)

Installation Methods

Method 1: pip (Recommended)

The simplest way to install NeuroShard:

# Create a virtual environment (recommended)
python -m venv neuroshard-env
source neuroshard-env/bin/activate  # On Windows: neuroshard-env\Scripts\activate

# Install NeuroShard
pip install nexaroa

With GPU Support (NVIDIA)

# Install PyTorch with CUDA first
pip install torch --index-url https://download.pytorch.org/whl/cu118

# Then install NeuroShard
pip install nexaroa

With GPU Support (Apple Silicon)

PyTorch on macOS with Apple Silicon automatically uses Metal:

pip install torch
pip install nexaroa

Method 2: Docker

Run NeuroShard in a Docker container:

# Pull the official image
docker pull neuroshard/node:latest

# Run with GPU support
docker run --gpus all -p 8000:8000 -p 9000:9000 \
  -e NEUROSHARD_TOKEN=YOUR_TOKEN \
  neuroshard/node:latest

Docker Compose

version: '3.8'
services:
  neuroshard-node:
    image: neuroshard/node:latest
    ports:
      - "8000:8000"
      - "9000:9000"
    environment:
      - NEUROSHARD_TOKEN=${NEUROSHARD_TOKEN}
    volumes:
      - neuroshard_data:/data
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]

volumes:
  neuroshard_data:

Verify Installation

After installation, verify everything works:

# Check version
neuroshard --version
# Output: NeuroShard 0.0.6

# Check available options
neuroshard --help

# Test without running (shows GPU detection)
python -c "import torch; print(f'CUDA: {torch.cuda.is_available()}')"

Platform-Specific Notes

Windows

1. Install Python: Download from python.org
2. Enable Long Paths: Run as admin:

   Set-ItemProperty -Path "HKLM:\SYSTEM\CurrentControlSet\Control\FileSystem" -Name "LongPathsEnabled" -Value 1

3. Install CUDA Toolkit (if using NVIDIA GPU): Download from NVIDIA

macOS

1. Install Homebrew: /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
2. Install Python: brew install python@3.11
3. For M1/M2/M3: PyTorch automatically uses Metal Performance Shaders

Linux (Ubuntu/Debian)

# Install dependencies
sudo apt update
sudo apt install python3 python3-pip python3-venv

# For NVIDIA GPU
sudo apt install nvidia-driver-535 nvidia-cuda-toolkit

Linux (Fedora/RHEL)

sudo dnf install python3 python3-pip

# For NVIDIA GPU
sudo dnf install akmod-nvidia xorg-x11-drv-nvidia-cuda

Updating

pip

pip install --upgrade nexaroa

Uninstalling

pip

pip uninstall nexaroa

Remove Data

# Remove checkpoints and cache
rm -rf ~/.neuroshard

# On Windows
rd /s /q %USERPROFILE%\.neuroshard

Next Steps

• Running a Node — Configure and start your node
• Quick Start — 5-minute setup guide
• CLI Reference — All command options