Dataset Viewer
timestamp
stringdate 2025-03-02 12:07:43
2025-03-02 12:07:52
| cpu_usage
listlengths 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
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2025-03-02 12:07:43
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2025-03-02 12:07:44
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2025-03-02 12:07:45
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2025-03-02 12:07:46
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2025-03-02 12:07:52
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N/A
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harpertokenSysMon Dataset
Dataset Summary
This open-source dataset captures real-time system metrics from macOS for time-series analysis, anomaly detection, and predictive maintenance.
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
1. Load in Python
from datasets import load_dataset
dataset = load_dataset("harpertoken/harpertokenSysMon")
df = dataset["train"].to_pandas()
print(df.head())
2. 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
- License: MIT
Contact
For questions or feedback, please contact [email protected]
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