---
title: Codium Windurf System Monitoring Dataset
tags:
- system-monitoring
- time-series
- anomaly-detection
- predictive-maintenance
- macOS
license: mit
language:
- en
---

# Codium Windurf System Monitoring Dataset (100% Open-Source)

## Dataset Summary
The **Codium Windurf System Monitoring Dataset** is a **100% open-source dataset** designed for **time-series analysis, anomaly detection, and system performance optimization**. It captures **real-time system metrics** from macOS, providing a structured collection of CPU, memory, disk, network, and thermal sensor data. The dataset is ideal for **fine-tuning AI models** for predictive maintenance, anomaly detection, and system load forecasting.

## Dataset Features
- **OS Compatibility:** macOS  
- **Data Collection Interval:** 1-5 seconds  
- **Total Storage Limit:** 4GB  
- **File Format:** CSV & Parquet  
- **Data Fields:**  
  - `timestamp`: Date and time of capture  
  - `cpu_usage`: CPU usage percentage per core  
  - `memory_used_mb`: RAM usage in MB  
  - `disk_read_mb`: Disk read in MB  
  - `disk_write_mb`: Disk write in MB  
  - `net_sent_mb`: Network upload in MB  
  - `net_recv_mb`: Network download in MB  
  - `battery_status`: Battery percentage (Mac only)  
  - `cpu_temp`: CPU temperature in °C  

## Usage Examples
### **1️⃣ Load in Python**
```python
from datasets import load_dataset

dataset = load_dataset("bniladridas/codium-windurf-system-monitoring")
df = dataset["train"].to_pandas()

print(df.head())
```

### **2️⃣ Train an Anomaly Detection Model**
```python
from sklearn.ensemble import IsolationForest

# Convert time-series to numerical format
df["cpu_usage_avg"] = df["cpu_usage"].apply(lambda x: sum(x) / len(x))

# Train model
model = IsolationForest(contamination=0.05)
model.fit(df[["cpu_usage_avg", "memory_used_mb", "disk_read_mb", "disk_write_mb"]])

# Predict anomalies
df["anomaly"] = model.predict(df[["cpu_usage_avg", "memory_used_mb", "disk_read_mb", "disk_write_mb"]])
```

## Potential Use Cases
✅ **AI Fine-Tuning** for real-time system monitoring models  
✅ **Time-Series Forecasting** of CPU & memory usage  
✅ **Anomaly Detection** for overheating and system failures  
✅ **Predictive Maintenance** for proactive issue detection  

## Licensing & Contributions
- **License:** MIT (100% Open-Source & Free)  
- **Contributions:** PRs are welcome! Open an issue for improvements.  

## How to Upload to Hugging Face
### **1️⃣ Install Hugging Face CLI**
```bash
pip install huggingface_hub
huggingface-cli login
```

### **2️⃣ Push the Dataset**
```python
from datasets import Dataset
import pandas as pd

# Load the dataset
df = pd.read_csv("system_monitoring_dataset.csv")
dataset = Dataset.from_pandas(df)

# Upload to Hugging Face
dataset.push_to_hub("bniladridas/codium-windurf-system-monitoring")
```

## Contact
For questions or feedback, please contact bniladridas@gmail.com