It is highly recommended to install HXCoreOL within a virtual environment. This prevents version conflicts with other Python projects on your system. To create a virtual environment, run: python -m venv hxcore_env Activate the environment: Linux/macOS: source hxcore_env/bin/activate Windows: .\hxcore_env\Scripts\activate Step 2: The HXCoreOL Installation Process
Permission Denied: If you encounter permission errors on Linux, avoid using sudo with pip. Instead, use a virtual environment or the --user flag.
Update the "Base_Directory" and "Logs_Path" to match your local folder structure. Step 4: Verification and First Run hxcoreol install
After the installation completes, you must initialize the configuration. HXCoreOL relies on a .yaml or .json configuration file to define data paths and API endpoints. Generate a default config: hxcoreol --init
To confirm that the hxcoreol install was successful, run the built-in diagnostic tool. This checks for missing libraries and validates your configuration file. hxcoreol --check If everything is green, launch the core service: hxcoreol start Troubleshooting Common Installation Issues It is highly recommended to install HXCoreOL within
Getting HXCoreOL up and running is a straightforward process once you understand the dependencies and environment requirements. This guide covers the complete installation lifecycle, from system preparation to verifying your first successful run.
Memory: Minimum 4GB RAM (8GB recommended for larger datasets). Step 1: Environment Preparation Instead, use a virtual environment or the --user flag
OS: Linux (Ubuntu 20.04+ recommended), macOS, or Windows via WSL2. Python: Version 3.8 or higher. Package Manager: pip (latest version) or Conda.