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INSTALLATION.md

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Self-Evolution Installation Guide

📦 System Requirements

Minimum Requirements

  • Python 3.8+
  • 4GB RAM
  • 10GB free disk space
  • Linux/macOS/Windows

Recommended Requirements

  • Python 3.10+
  • 8GB RAM
  • 20GB free disk space
  • Linux (Ubuntu 20.04+)
  • SSD storage

Optional Requirements

  • CUDA-capable GPU (for deep learning components)
  • Docker (for containerized deployment)
  • Poetry or pip (for package management)

🚀 Installation Methods

Method 1: Direct Installation

Step 1: Clone Repository

git clone https://github.com/openclaw/self-evolution.git
cd self-evolution

Step 2: Create Virtual Environment

# Using venv
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Using conda
conda create -n self-evolution python=3.10
conda activate self-evolution

Step 3: Install Dependencies

pip install -r requirements.txt

Step 4: Run Tests

# Run all tests
python3 run_tests.py

# Run specific phase tests
python3 -m pytest evolution/tests/ -v
python3 -m pytest strategies/tests/ -v
python3 -m pytest metalearning/tests/ -v
python3 -m pytest advanced/tests/ -v

Method 2: Pip Installation

# Install from PyPI (when available)
pip install openclaw-self-evolution

Method 3: Docker Installation

Step 1: Build Docker Image

docker build -t self-evolution:latest .

Step 2: Run Container

docker run --rm -it self-evolution:latest bash

Step 3: Run Tests in Container

docker run --rm self-evolution:latest python3 run_tests.py

Method 4: Development Installation

# Install in editable mode
cd self-evolution
pip install -e .

🔧 Configuration

Environment Variables

# Workspace path
export SELF_EVOLUTION_WORKSPACE="/path/to/workspace"

# Log level
export SELF_EVOLUTION_LOG_LEVEL="INFO"

# Safety level
export SELF_EVOLUTION_SAFETY_LEVEL="strict"

# Resource limits
export SELF_EVOLUTION_MAX_MEMORY="4GB"
export SELF_EVOLUTION_MAX_CPU="4"

Configuration Files

Create config.yaml:

# Evolution Configuration
evolution:
  workspace_path: "/path/to/workspace"
  log_file: "evolution.log"
  max_iterations: 1000
  auto_confirm: false
  safety_checks: true

# Safety Configuration
safety:
  level: "strict"
  auto_rollback: true
  validation_rules:
    - "no_critical_changes_without_approval"
    - "no_deleting_source_code"
    - "no_modifying_safety_guards"

# Performance Configuration
performance:
  max_memory: "4GB"
  max_cpu: "4"
  monitoring_enabled: true
  metrics_interval: 60

# Logging Configuration
logging:
  level: "INFO"
  file: "self-evolution.log"
  console: true
  format: "%(asctime)s - %(name)s - %(levelname)s - %(message)s"

✅ Verification

Run Verification Script

python3 verify_installation.py

This will check:

  • Python version compatibility
  • All dependencies installed
  • File permissions correct
  • Tests can run successfully
  • Configuration valid

Expected Output

✅ Python version: 3.10.12
✅ All dependencies installed
✅ File permissions: OK
✅ Tests: PASSING (20/20)
✅ Configuration: VALID

🧪 Testing Installation

Run Unit Tests

python3 -m pytest evolution/tests/ -v --tb=short
python3 -m pytest strategies/tests/ -v --tb=short
python3 -m pytest metalearning/tests/ -v --tb=short
python3 -m pytest advanced/tests/ -v --tb=short

Run Integration Tests

python3 integration_test.py

Run All Tests

python3 run_tests.py --verbose

🐛 Troubleshooting

Issue: Import Error

Symptom: ModuleNotFoundError: No module named 'evolution'

Solution:

# Install in editable mode
pip install -e .

Issue: Permission Denied

Symptom: PermissionError: [Errno 13] Permission denied

Solution:

# Fix permissions
chmod +x run_tests.py
chmod -R +x evolution/
chmod -R +x strategies/
chmod -R +x metalearning/
chmod -R +x advanced/

Issue: Tests Failing

Symptom: Tests failing with import errors

Solution:

# Ensure correct Python version
python3 --version  # Should be 3.8+

# Reinstall dependencies
pip install --upgrade -r requirements.txt

# Clear Python cache
find . -type d -name __pycache__ -exec rm -rf {} +

Issue: Out of Memory

Symptom: MemoryError during training

Solution:

# Reduce batch size
# Reduce number of layers
# Use gradient accumulation
# Enable memory optimization

🔒 Security Configuration

Enable Safety Mode

from evolution.core.evolution_cycle import EvolutionCycle

evolution = EvolutionCycle(safety_level="strict")

Configure Rollback

evolution = EvolutionCycle(
    safety_level="strict",
    auto_rollback=True,
    backup_before_change=True
)

Set Resource Limits

evolution = EvolutionCycle(
    max_memory="4GB",
    max_cpu=4,
    max_gpu_memory="8GB"
)

📦 Optional Dependencies

For GPU Acceleration

pip install torch torchvision
pip install tensorflow-gpu

For Advanced Logging

pip install loguru
pip install rich

For Distributed Training

pip install horovod
pip install ray

For Better Visualization

pip install tensorboard
pip install matplotlib
pip install seaborn

🚀 Quick Start

After installation, try this:

from evolution.core import EvolutionCycle

# Create evolution cycle
evolution = EvolutionCycle()

# Run test evolution
result = evolution.run_evolution(
    num_iterations=10,
    safety_checks=True
)

print(f"Evolution status: {result['status']}")

📝 Next Steps

  1. Read USAGE.md for usage guide
  2. Review ARCHITECTURE.md for system architecture
  3. Check API.md for API documentation
  4. See PHASE_SUMMARY.md for phase summaries

Installation Version: 1.0.0
Last Updated: 2026-03-08