Yijia_Sun_PS2: System Configuration and Setup Guide

This document provides detailed setup instructions for running the comorbidity network analysis system on both cloud and local environments.

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1. System Requirements

1.1 Hardware Specifications

Component	Minimum Requirements
CPU	Intel Xeon CPU @ 2.20GHz (x86_64)
Cores	2 Cores (Hyperthreaded)
Memory	12 GB RAM
Storage	At least 100 GB disk space
GPU	No GPU detected (CPU-based processing)

1.2 Software Specifications

Component	Version
Operating System	Ubuntu-based (KVM Virtualized)
Python Version	3.11.11
Key Dependencies	Listed in requirements.txt

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2. Local Setup

2.1 Install Required Packages

Ensure you have Python 3.11+ installed. If not, install it via:

sudo apt update && sudo apt install python3.11 python3.11-venv python3.11-dev -y

Next, create and activate a virtual environment:

python3.11 -m venv env
source env/bin/activate

Install dependencies from requirements.txt:

python --version
pip list

2.2 Verify Python and Package Installation

Check if Python and required libraries are correctly installed:

python --version
pip list

Expected output:

Python 3.11.11

2.3 Configure Data Storage

Ensure at least 100GB of free space for data processing:

df -h

If needed, extend disk space before running large-scale computations.

2.4 Running the System

Execute the comorbidity network analysis with:

python main.py --input data/prevalence_data.csv --output results/

Results will be stored in the results/ folder.

3. Cloud Deployment (AWS, GCP, or Azure)

This section provides a step-by-step guide for deploying the comorbidity network analysis system on cloud platforms like AWS EC2, Google Cloud Compute Engine (GCP), and Microsoft Azure Virtual Machines.

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3.1 Setting Up a Cloud Instance

Select a compute-optimized virtual machine (VM) for optimal performance:

• AWS EC2: c5.large (2 vCPUs, 12 GB RAM)
• Google Cloud Compute Engine: n2-standard-2
• Azure Virtual Machine: Standard_D2s_v3

Create and launch the instance using the Ubuntu LTS image.

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3.2 Install Required Dependencies

After connecting to the instance via SSH, update system packages:

sudo apt update && sudo apt upgrade -y

Install Python and required system dependencies:

sudo apt install python3.11 python3.11-venv python3.11-dev -y

Clone the GitHub repository and navigate to the project directory:

git clone https://github.com/YOUR_REPO_NAME.git
cd YOUR_REPO_NAME

Create a virtual environment and install dependencies:

python3.11 -m venv env
source env/bin/activate
pip install -r requirements.txt

Verify Python and package installations:

python --version
pip list

3.3 Managing Large Datasets

Since cloud instances may have limited storage, consider using cloud storage solutions:

AWS S3

aws s3 cp s3://your-bucket/prevalence_data.csv data/

Google Cloud Storage (GCP)

gsutil cp gs://your-bucket/prevalence_data.csv data/

3.4 Running the Analysis in Cloud

Run the main script with cloud-based data:

python main.py --input data/prevalence_data.csv --output results/

Monitor processing logs:

tail -f logs/output.log

After execution, download results locally:

scp -r user@your-cloud-instance-ip:/path/to/results ./local_results/