Dataset Viewer
timestamp
stringclasses 10
values | cpu_usage
sequencelengths 8
8
| memory_used_mb
float64 3.37k
3.41k
| disk_read_mb
float64 4.31M
4.31M
| disk_write_mb
float64 1.61M
1.61M
| net_sent_mb
float64 6.06k
6.06k
| net_recv_mb
float64 2.94k
2.94k
| battery_status
int64 35
35
| cpu_temp
stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|
2025-03-02 12:07:43 | [
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2025-03-02 12:07:44 | [
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] | 3,405.6875 | 4,305,889.695313 | 1,611,208.207031 | 6,063.010742 | 2,937.003906 | 35 | N/A |
2025-03-02 12:07:45 | [
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2025-03-02 12:07:46 | [
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2025-03-02 12:07:47 | [
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2025-03-02 12:07:48 | [
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2025-03-02 12:07:49 | [
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2025-03-02 12:07:50 | [
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2025-03-02 12:07:51 | [
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2025-03-02 12:07:52 | [
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] | 3,377.140625 | 4,306,966.347656 | 1,611,401.945313 | 6,063.274414 | 2,937.210938 | 35 | N/A |
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 capturecpu_usage: CPU usage percentage per corememory_used_mb: RAM usage in MBdisk_read_mb: Disk read in MBdisk_write_mb: Disk write in MBnet_sent_mb: Network upload in MBnet_recv_mb: Network download in MBbattery_status: Battery percentage (Mac only)cpu_temp: CPU temperature in °C
Usage Examples
1️⃣ Load in 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
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
pip install huggingface_hub
huggingface-cli login
2️⃣ Push the Dataset
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 [email protected]
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