README.md

---
pretty_name: Gaming Telemetry
license:
- mit
- apache-2.0
task_categories:
- time-series-forecasting
- reinforcement-learning
language:
- en
size_categories:
- 10K<n<100K
configs:
- config_name: re4
  data_files:
  - split: train
    path:
    - re4/train-*.parquet
dataset_info:
- config_name: re4
  features:
  - name: timestamp_ms
    dtype: int64
  - name: power_usage_mw
    dtype: uint32
  - name: temperature_c
    dtype: uint32
  - name: graphics_clock_mhz
    dtype: uint32
  - name: memory_clock_mhz
    dtype: uint32
  - name: pcie_rx_kbps
    dtype: uint32
  - name: pcie_tx_kbps
    dtype: uint32
  - name: pstate
    dtype: uint32
  - name: throttle_reasons_bitmask
    dtype: uint64
  - name: fan_speed_perc
    dtype: uint32
  - name: memory_used_mb
    dtype: uint64
  - name: memory_total_mb
    dtype: uint64
  - name: encoder_util_perc
    dtype: uint32
  - name: decoder_util_perc
    dtype: uint32
  - name: cpu_tctl_c
    dtype: float32
  - name: cpu_ccd1_c
    dtype: float32
  - name: cpu_ccd2_c
    dtype: float32
  - name: cpu_package_power_w
    dtype: float32
  - name: session_label
    dtype: string
  splits:
  - name: train
    num_bytes: 9109835
    num_examples: 95893
  download_size: 2607671
  dataset_size: 9109835
tags:
- telemetry
- gpu-telemetry
- nvml
- gaming
- path-tracing
- dlss
- neuromorphic
- spiking-neural-networks
- snn
- hardware-aware-ai
- time-series
- rust
- rtx-5080
- spikenaut
---

# 🎮 Gaming Telemetry

High-frequency bare-metal GPU/CPU telemetry captured **while playing games at
maximum settings** — DLSS 4.0, path tracing, the works — on an NVIDIA RTX 5080
workstation. Collected for neuromorphic / spiking-neural-network research: a
game session turns out to be a far richer stress instrument than crypto mining
or sync-node workloads, with genuine idle → ramp → sustained-load → recovery
dynamics, real wall-clock timestamps, and the NVML throttle bitmask.

This is the **canonical multi-title hardware corpus** for the Spikenaut /
Limen-Neural ecosystem. One config per capture session; every config shares the
same collector schema, Parquet only.

```python
from datasets import load_dataset

re4 = load_dataset("rmems/gaming-telemetry", "re4", split="train")
```

## 📊 Sessions

| Config | Title | Rows | Span | Cadence (mean) | Notes |
|--------|-------|-----:|------|------|-------|
| `re4` | Resident Evil 4 Remake (path tracing) | 95,893 | 90.1 min | 56.4 ms | 9.3% of samples at SW power cap (`0x4`), peak 364.9 W, max 66 °C, VRAM to 15.9 GB |

Future sessions (Cyberpunk 2077, KCD2, …) land as new configs — the
[collector](https://github.com/rmems/gaming-telemetry) is game-agnostic; a new
title only needs a `SESSION_LABEL` and a play session.

## 🔍 Schema (`system_telemetry_v1` + `session_label`)

| Column | Type | Unit / meaning |
|--------|------|----------------|
| `timestamp_ms` | int64 | wall clock, ms since Unix epoch |
| `power_usage_mw` | uint32 | GPU board power, milliwatts |
| `temperature_c` | uint32 | GPU core temperature |
| `graphics_clock_mhz`, `memory_clock_mhz` | uint32 | clocks |
| `pcie_rx_kbps`, `pcie_tx_kbps` | uint32 | PCIe throughput |
| `pstate` | uint32 | NVML performance state (P0=0 …) |
| `throttle_reasons_bitmask` | uint64 | `nvmlClocksThrottleReasons` bits (`0x4` = SW power cap, `0x20`/`0x40` = SW/HW thermal, `0x80` = HW power brake) |
| `fan_speed_perc` | uint32 | fan duty |
| `memory_used_mb`, `memory_total_mb` | uint64 | VRAM |
| `encoder_util_perc`, `decoder_util_perc` | uint32 | NVENC/NVDEC utilization |
| `cpu_tctl_c`, `cpu_ccd1_c`, `cpu_ccd2_c` | float32 | CPU temperatures (hwmon) |
| `cpu_package_power_w` | float32 | CPU package power (RAPL energy delta) |
| `session_label` | string | capture session id, constant per config |

Missing readings are `null`, never `0`.

## 📈 Provenance

```
rmems/gaming-telemetry (GitHub)   Rust collector: NVML + hwmon/RAPL → Parquet batches
        ↓
rmems/gaming-telemetry (this dataset)   canonical per-session configs
        ↓
rmems/SEMM-Latent-Telemetry       SAAQ / SEMM routing research (RE4 was its hardware drive)
rmems/Spikenaut-SNN-Telemetry     supervisor-trajectory dataset (state_telemetry backfill candidate)
```

**`re4` normalization, for the record.** The seed capture was first published as
48 raw batch files in
[`SEMM-Latent-Telemetry/origin_hardware_baselines/resident_evil_4/`](https://huggingface.co/datasets/rmems/SEMM-Latent-Telemetry/tree/main/origin_hardware_baselines/resident_evil_4)
(`system_telemetry_v1_batch_1..48.parquet`), which remain the untouched origin
artifact. This dataset's `re4/train-00000.parquet` is those 48 batches
concatenated, **sorted by `timestamp_ms`** (lexicographic batch globbing would
interleave time), with a constant `session_label = "re4"` column appended to
match the collector's v2 convention. Row count (95,893), null counts, and
per-column sums were verified identical to the raw batches before publishing;
no value was altered.

The capture predates the collector's v2 schema, so v2-only fields beyond
`session_label` are absent here; future sessions will carry the current
collector schema and a documented `POLL_INTERVAL_MS`.

## ⚠️ Honest caveats

- **Single machine, single session so far.** One RTX 5080 workstation, one
  ~90-minute RE4 run. No cross-GPU generality is claimed.
- The effective cadence (~56 ms mean) is the observed spacing of this capture,
  not a declared collector setting for it.
- The GPU never left its comfort zone: max 66 °C, no thermal or HW-slowdown
  throttle bits fired — the only non-zero mask is SW power cap. Good "healthy
  under load" baseline; not an incident corpus.
- Gameplay content is **not** recorded — no frames, inputs, audio, or overlay
  data. Hardware counters only.

## 📜 Citation

```bibtex
@dataset{gaming_telemetry,
  author={Montoya Cardenas, Raul},
  title={Gaming Telemetry: bare-metal GPU/CPU traces under max-settings gameplay},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/rmems/gaming-telemetry}
}
```

## ⚖️ License

**MIT OR Apache-2.0** — dual-licensed; use whichever fits your project.

## 🙏 Acknowledgments

- **Capcom's RE Engine team** — unknowingly the best GPU stress instrument in this lab
- **Claude Fable 5** (Anthropic) — dataset normalization, validation, and this card, in [Claude Code](https://claude.com/claude-code) under human review

*Built by Raul Montoya Cardenas — WGU AI Engineering*