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 value |
|---|---|---|---|---|---|---|---|---|
2025-03-02 12:07:43 | [
96,
95,
96,
95,
5.9,
2.9,
2,
1
] | 3,404.828125 | 4,305,862.5 | 1,611,196.0625 | 6,061.246094 | 2,935.239258 | 35 | N/A |
2025-03-02 12:07:44 | [
98,
97,
96.1,
97,
8.9,
5,
1,
1
] | 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 | [
93.1,
92,
92,
92,
10.1,
8,
5,
4
] | 3,410.828125 | 4,306,019.230469 | 1,611,242.589844 | 6,063.019531 | 2,937.009766 | 35 | N/A |
2025-03-02 12:07:46 | [
93,
92,
92,
91,
22,
14,
9.9,
11
] | 3,397.796875 | 4,306,190.113281 | 1,611,267.597656 | 6,063.024414 | 2,937.026367 | 35 | N/A |
2025-03-02 12:07:47 | [
86,
86.1,
84.8,
83,
13,
9.9,
5,
3
] | 3,386.1875 | 4,306,302.285156 | 1,611,317.910156 | 6,063.026367 | 2,937.02832 | 35 | N/A |
2025-03-02 12:07:48 | [
97,
98,
97,
98,
5.9,
3,
1,
3
] | 3,382.96875 | 4,306,456.160156 | 1,611,334.332031 | 6,063.073242 | 2,937.041016 | 35 | N/A |
2025-03-02 12:07:49 | [
97.1,
97,
97,
97,
13.1,
11.9,
7.9,
5
] | 3,397.90625 | 4,306,604.78125 | 1,611,348.902344 | 6,063.084961 | 2,937.048828 | 35 | N/A |
2025-03-02 12:07:50 | [
94.9,
95,
94,
94,
0,
0,
0,
0
] | 3,377.75 | 4,306,715.691406 | 1,611,388.242188 | 6,063.087891 | 2,937.152344 | 35 | N/A |
2025-03-02 12:07:51 | [
95,
96,
95,
95,
10,
3,
0,
0
] | 3,372.765625 | 4,306,849.769531 | 1,611,389.765625 | 6,063.231445 | 2,937.178711 | 35 | N/A |
2025-03-02 12:07:52 | [
94.9,
95,
95,
94.9,
7.9,
4,
1,
0
] | 3,377.140625 | 4,306,966.347656 | 1,611,401.945313 | 6,063.274414 | 2,937.210938 | 35 | N/A |
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 harpertoken@icloud.com
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