Download code/src/training/system_check.py from DuyTa/cve-kgrag-db: direct link, hf CLI and curl.
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https://huggingface.co/datasets/DuyTa/cve-kgrag-db/resolve/main/code/src/training/system_check.py
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5.33 kB
| #!/usr/bin/env python3 | |
| """ | |
| System Resource Check for Ollama | |
| Checks system resources that might affect Ollama performance | |
| """ | |
| import psutil | |
| import platform | |
| import logging | |
| from typing import Dict, Any | |
| # Configure logging | |
| logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') | |
| logger = logging.getLogger(__name__) | |
| def check_system_resources() -> Dict[str, Any]: | |
| """Check system resources""" | |
| logger.info("🔍 Checking system resources...") | |
| # CPU info | |
| cpu_count = psutil.cpu_count() | |
| cpu_percent = psutil.cpu_percent(interval=1) | |
| # Memory info | |
| memory = psutil.virtual_memory() | |
| memory_total_gb = memory.total / (1024**3) | |
| memory_available_gb = memory.available / (1024**3) | |
| memory_percent = memory.percent | |
| # Disk info | |
| disk = psutil.disk_usage('/') | |
| disk_total_gb = disk.total / (1024**3) | |
| disk_free_gb = disk.free / (1024**3) | |
| disk_percent = (disk.used / disk.total) * 100 | |
| # System info | |
| system_info = { | |
| 'platform': platform.system(), | |
| 'platform_version': platform.version(), | |
| 'architecture': platform.architecture()[0], | |
| 'cpu_count': cpu_count, | |
| 'cpu_percent': cpu_percent, | |
| 'memory_total_gb': round(memory_total_gb, 2), | |
| 'memory_available_gb': round(memory_available_gb, 2), | |
| 'memory_percent': memory_percent, | |
| 'disk_total_gb': round(disk_total_gb, 2), | |
| 'disk_free_gb': round(disk_free_gb, 2), | |
| 'disk_percent': round(disk_percent, 2) | |
| } | |
| logger.info("📊 System Resources:") | |
| logger.info(f" Platform: {system_info['platform']} {system_info['platform_version']}") | |
| logger.info(f" Architecture: {system_info['architecture']}") | |
| logger.info(f" CPU: {system_info['cpu_count']} cores, {system_info['cpu_percent']}% usage") | |
| logger.info(f" Memory: {system_info['memory_total_gb']} GB total, {system_info['memory_available_gb']} GB available ({system_info['memory_percent']}% used)") | |
| logger.info(f" Disk: {system_info['disk_total_gb']} GB total, {system_info['disk_free_gb']} GB free ({system_info['disk_percent']}% used)") | |
| # Check for potential issues | |
| issues = [] | |
| if memory_available_gb < 8: | |
| issues.append(f"⚠️ Low available memory: {memory_available_gb:.1f} GB (recommended: 8+ GB for 8B model)") | |
| if disk_free_gb < 10: | |
| issues.append(f"⚠️ Low disk space: {disk_free_gb:.1f} GB free") | |
| if cpu_percent > 90: | |
| issues.append(f"⚠️ High CPU usage: {cpu_percent}%") | |
| if issues: | |
| logger.warning("🚨 Potential resource issues detected:") | |
| for issue in issues: | |
| logger.warning(f" {issue}") | |
| else: | |
| logger.info("✅ System resources look adequate for Ollama") | |
| return system_info | |
| def check_ollama_processes() -> Dict[str, Any]: | |
| """Check for Ollama-related processes""" | |
| logger.info("🔍 Checking for Ollama processes...") | |
| ollama_processes = [] | |
| for proc in psutil.process_iter(['pid', 'name', 'memory_info', 'cpu_percent']): | |
| try: | |
| if 'ollama' in proc.info['name'].lower(): | |
| ollama_processes.append({ | |
| 'pid': proc.info['pid'], | |
| 'name': proc.info['name'], | |
| 'memory_mb': proc.info['memory_info'].rss / (1024**2), | |
| 'cpu_percent': proc.info['cpu_percent'] | |
| }) | |
| except (psutil.NoSuchProcess, psutil.AccessDenied): | |
| continue | |
| if ollama_processes: | |
| logger.info(f"✅ Found {len(ollama_processes)} Ollama processes:") | |
| total_memory = 0 | |
| for proc in ollama_processes: | |
| logger.info(f" PID {proc['pid']}: {proc['name']} - {proc['memory_mb']:.1f} MB RAM, {proc['cpu_percent']}% CPU") | |
| total_memory += proc['memory_mb'] | |
| logger.info(f" Total Ollama memory usage: {total_memory:.1f} MB") | |
| else: | |
| logger.warning("⚠️ No Ollama processes found") | |
| return {'ollama_processes': ollama_processes} | |
| def main(): | |
| """Main function""" | |
| logger.info("🚀 Starting system resource check...") | |
| # Check system resources | |
| system_info = check_system_resources() | |
| # Check Ollama processes | |
| process_info = check_ollama_processes() | |
| # Summary | |
| logger.info("\n📋 Summary:") | |
| logger.info(f" System: {system_info['platform']} with {system_info['cpu_count']} cores") | |
| logger.info(f" Memory: {system_info['memory_available_gb']:.1f} GB available") | |
| logger.info(f" Ollama processes: {len(process_info['ollama_processes'])}") | |
| # Recommendations | |
| logger.info("\n💡 Recommendations:") | |
| if system_info['memory_available_gb'] < 8: | |
| logger.info(" - Close other applications to free up memory") | |
| logger.info(" - Consider using a smaller model (e.g., 3B instead of 8B)") | |
| if len(process_info['ollama_processes']) == 0: | |
| logger.info(" - Start Ollama: ollama serve") | |
| logger.info(" - For 8B models, ensure at least 8GB RAM is available") | |
| logger.info(" - Consider using GPU acceleration if available") | |
| if __name__ == "__main__": | |
| main() |