| --- |
| 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* |
|
|
