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Installation Guide

This guide covers everything you need to install and set up Entropic AI on your system.

Quick Installation

The easiest way to install Entropic AI is through PyPI:

pip install entropic-ai

With Optional Dependencies

For enhanced functionality, install with optional dependencies:

# For GPU acceleration
pip install entropic-ai[gpu]

# For molecular applications
pip install entropic-ai[molecules]

# For circuit design
pip install entropic-ai[circuits]

# For all features
pip install entropic-ai[full]

Prerequisites

System Requirements

Minimum Requirements:

  • Python 3.9 or higher
  • 8GB RAM
  • 2GB available disk space
  • CPU with AVX2 support (Intel Sandy Bridge+ or AMD Bulldozer+)

Recommended Requirements:

  • Python 3.10 or higher
  • 16GB+ RAM
  • 5GB available disk space
  • GPU with CUDA 11.8+ support
  • SSD storage for better I/O performance

Python Environment

We strongly recommend using a virtual environment:

# Using conda (recommended)
conda create -n entropic-ai python=3.10
conda activate entropic-ai

# Using venv
python -m venv entropic-ai-env
source entropic-ai-env/bin/activate  # Linux/Mac
# or
entropic-ai-env\Scripts\activate     # Windows

Installation Methods

Method 1: PyPI Installation

Install the latest stable release:

pip install entropic-ai

Verify installation:

python -c "import entropic-ai; print(entropic-ai.__version__)"

Method 2: Development Installation

For the latest features and development:

git clone https://github.com/krish567366/Entropic-AI.git
cd Entropic-AI
pip install -e .

This installs in "editable" mode, allowing you to modify the source code.

Method 3: Docker Installation

Run Entropic AI in a containerized environment:

docker pull krish567366/entropic-ai:latest
docker run -it krish567366/entropic-ai:latest

For GPU support:

docker run --gpus all -it krish567366/entropic-ai:gpu

GPU Support

CUDA Installation

For GPU acceleration, install CUDA-compatible PyTorch:

# Check CUDA version
nvidia-smi

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

Verify GPU Support

import torch
print(f"CUDA available: {torch.cuda.is_available()}")
print(f"CUDA devices: {torch.cuda.device_count()}")
if torch.cuda.is_available():
    print(f"Current device: {torch.cuda.get_device_name()}")

Optional Dependencies

Molecular Modeling

For molecular design applications:

pip install rdkit-pypi py3dmol biopython

Circuit Design

For electronic circuit applications:

pip install ngspice-python schemdraw electronics

Visualization

For enhanced plotting and visualization:

pip install plotly bokeh seaborn

Scientific Computing

For advanced scientific applications:

pip install sympy numba cupy-cuda118

Platform-Specific Instructions

Windows

  1. Install Visual Studio Build Tools:
  2. Download from Microsoft

  3. Install C++ build tools

  4. Install Entropic AI:

pip install entropic-ai
  1. For GPU support:
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

macOS

  1. Install Xcode Command Line Tools:
xcode-select --install
  1. Install using Homebrew Python (recommended):
brew install python@3.10
pip3.10 install entropic-ai
  1. For M1/M2 Macs:
# Use MPS backend for GPU acceleration
pip install torch torchvision torchaudio

Linux (Ubuntu/Debian)

  1. Install system dependencies:
sudo apt update
sudo apt install python3-pip python3-dev build-essential
  1. Install Entropic AI:
pip3 install entropic-ai
  1. For GPU support:
# Install NVIDIA drivers and CUDA
sudo apt install nvidia-driver-525 nvidia-cuda-toolkit
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

Linux (CentOS/RHEL)

  1. Install system dependencies:
sudo yum install python3-pip python3-devel gcc gcc-c++
  1. Install Entropic AI:
pip3 install entropic-ai

Configuration

Environment Variables

Set up environment variables for optimal performance:

# Bash/Zsh
export entropic-ai_CACHE_DIR="$HOME/.entropic-ai/cache"
export entropic-ai_DATA_DIR="$HOME/.entropic-ai/data"
export entropic-ai_NUM_THREADS="8"

# Windows CMD
set entropic-ai_CACHE_DIR=%USERPROFILE%\.entropic-ai\cache
set entropic-ai_DATA_DIR=%USERPROFILE%\.entropic-ai\data
set entropic-ai_NUM_THREADS=8

Configuration File

Create a configuration file at ~/.entropic-ai/config.yaml:

# Entropic AI Configuration
general:
  log_level: INFO
  cache_enabled: true
  num_threads: auto

thermodynamics:
  default_temperature: 1.0
  cooling_schedule: exponential
  entropy_regularization: 0.1

performance:
  use_gpu: auto
  memory_fraction: 0.8
  mixed_precision: true

applications:
  molecules:
    force_field: universal
    implicit_solvent: true
  circuits:
    simulator: ngspice
    optimization_level: 2

Verification

Basic Installation Test

import entropic-ai
from entropic-ai import EntropicNetwork, GenerativeDiffuser
import torch

print(f"Entropic AI version: {entropic-ai.__version__}")

# Create a simple network
network = EntropicNetwork(nodes=32)
diffuser = GenerativeDiffuser(network)

# Test evolution
chaos = torch.randn(1, 32)
order = diffuser.evolve(chaos)

print("✅ Basic installation test passed!")

Performance Benchmark

from entropic-ai.benchmarks import installation_benchmark

# Run installation benchmark
results = installation_benchmark()
print(f"Performance score: {results.score}")
print(f"GPU acceleration: {results.gpu_available}")
print(f"All tests passed: {results.all_passed}")

Application Tests

# Test molecular evolution
from entropic-ai.applications import MoleculeEvolution
mol_evolver = MoleculeEvolution()
print("✅ Molecular evolution available")

# Test circuit design
from entropic-ai.applications import CircuitEvolution
circuit_evolver = CircuitEvolution()
print("✅ Circuit evolution available")

# Test theory discovery
from entropic-ai.applications import TheoryDiscovery
theory_evolver = TheoryDiscovery()
print("✅ Theory discovery available")

Troubleshooting

Common Issues

ImportError: No module named 'entropic-ai'

  • Solution: Ensure you're in the correct virtual environment
  • Check: pip list | grep entropic-ai

CUDA out of memory

  • Solution: Reduce batch size or use CPU
  • Set: export CUDA_VISIBLE_DEVICES=""

Slow performance

  • Solution: Install with GPU support
  • Check: torch.cuda.is_available()

Permission denied errors

  • Solution: Use virtual environment or --user flag
  • Command: pip install --user entropic-ai

Getting Help

If you encounter issues:

  1. Check the FAQ: Frequently Asked Questions
  2. Search existing issues: GitHub Issues
  3. Ask for help: GitHub Discussions

System Information

To report issues, include system information:

import entropic-ai
entropic-ai.print_system_info()

This will output:

  • Entropic AI version
  • Python version
  • PyTorch version
  • CUDA version (if available)
  • Operating system
  • Hardware details

Next Steps

After installation, check out:

Updates

Keeping Entropic AI Updated

Check for updates regularly:

pip list --outdated | grep entropic-ai
pip install --upgrade entropic-ai

Beta Releases

To try beta features:

pip install --pre entropic-ai

Development Snapshots

For the absolute latest code:

pip install git+https://github.com/krish567366/Entropic-AI.git

Uninstallation

To remove Entropic AI:

pip uninstall entropic-ai

# Remove cache and data directories
rm -rf ~/.entropic-ai/  # Linux/Mac
rmdir /s %USERPROFILE%\.entropic-ai  # Windows

Welcome to the world of thermodynamic intelligence! 🌌