File size: 5,183 Bytes
92ff720 911259f 92ff720 911259f c717a65 911259f 1f8bf6c 911259f a9fceb6 1f8bf6c a9fceb6 911259f 1f8bf6c 911259f 1f8bf6c 911259f 731dd74 911259f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 |
---
license: cc-by-4.0
language:
- en
tags:
- hardware
- infrastructure
- system
- subsystem
- CPU
- GPU
- memory
- network
- storage
- telemetry
- anomaly-detection
- performance
pretty_name: Reveal
---
# 🛰️ Dataset Card for **Reveal: Hardware Telemetry Dataset for Machine Learning Infrastructure Profiling and Anomaly Detection**
## Dataset Details
### Dataset Description
**Reveal** is a large-scale, curated dataset of **hardware telemetry** collected from high-performance computing (HPC) while running diverse machine learning (ML) workloads.
It enables reproducible research on **system-level profiling**, **unsupervised anomaly detection**, and **ML infrastructure optimization**.
The dataset accompanies the paper
📄 *“Detecting Anomalies in Systems for AI Using Hardware Telemetry”* (Chen *et al.*, University of Oxford, 2025).
Reveal captures low-level hardware and operating system metrics—fully accessible to operators—allowing anomaly detection **without requiring workload knowledge or instrumentation**.
- **Curated by:** Ziji Chen, Steven W. D. Chien, Peng Qian, Noa Zilberman (University of Oxford, Department of Engineering Science)
- **Shared by:** Ziji Chen (contact: [email protected])
- **Language(s):** English (metadata and documentation)
- **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
---
### Dataset Sources
- **Paper:** [Detecting Anomalies in Systems for AI Using Hardware Telemetry](https://arxiv.org/abs/2510.26008)
- **DOI:** [10.5281/zenodo.17470313](https://doi.org/10.5281/zenodo.17470313)
---
## Uses
### Direct Use
Reveal can be used for:
- Research on **unsupervised anomaly detection** in system telemetry
- Modeling **multivariate time-series** from hardware metrics
- Studying **cross-subsystem interactions** (CPU, GPU, memory, network, storage)
- Developing **performance-aware ML infrastructure tools**
- Training or benchmarking anomaly detection models for **AIOps** and **ML system health monitoring**
### Out-of-Scope Use
The dataset **should not** be used for:
- Inferring or reconstructing user workloads or model behavior
- Benchmarking end-user application performance
- Any use involving personal, confidential, or proprietary data reconstruction
---
## Dataset Structure
Reveal consists of time-series telemetry, derived features, and automatically labeled anomaly segments.
**Core fields include:**
- `timestamp`: UTC time of sample
- `host_id`: host or node identifier
- `metric_name`: name of the measured counter
- `value`: recorded numeric value
- `subsystem`: {CPU, *GPU (if supported by the underlying infrastructure), Memory, Network, Storage}
**Additional Notes**
A complete list of metrics and their descriptions can be found in `MetricDescriptionCPU.md` and `MetricDescriptionGPU.md`.
After downloading and extracting the dataset zip, place the `meta.csv` file and the `example Jupyter notebooks` inside the `Reveal/` directory before running.
---
## Dataset Creation
### Curation Rationale
Modern ML workloads are complex and opaque to operators due to virtualization and containerization. Reveal was created to **enable infrastructure-level observability** and anomaly detection purely from hardware telemetry, without access to user workloads.
### Source Data
#### Data Collection and Processing
- Collected using: `perf`, `procfs`, `nvidia-smi`, and standard Linux utilities
- Sampling interval: 100 ms
- ~150 raw metric types per host, expanded to ~700 time-series channels, including metrics related to GPUs.
#### Workloads and Systems
- **Workloads:** >30 ML applications (BERT, BART, ResNet, ViT, VGG, DeepSeek, LLaMA, Mistral)
- **Datasets:** GLUE/SST2, WikiSQL, PASCAL VOC, CIFAR, MNIST
- **Systems:**
- Dual-node GPU HPC cluster: Two nodes, each with two NVIDIA V100 GPUs (32 GB), an Intel Xeon Platinum 8628 CPU (48 cores), 384 GB memory, connected through InfiniBand HDR100. Packaged as `Reveal.zip`.
- Nine-node CPU cluster: Nine servers, each running 11 Apptainer containers (four threads and 20 GB memory per container), powered by AMD EPYC 7443P CPUs. Packaged as `RevealCPURun<n>.zip`.
#### Who are the data producers?
All data was generated by the authors in controlled environments using synthetic workloads.
No user or private information is included.
### Annotations
#### Personal and Sensitive Information
No personal, identifiable, or proprietary data.
All records are machine telemetry and anonymized.
---
## Bias, Risks, and Limitations
- Collected on specific hardware (NVIDIA/AMD CPUs, NVIDIA GPUs); behavior may differ on other architectures.
- Reflects **controlled test conditions**, not production cloud variability.
---
## Citation
**BibTeX:**
```bibtex
@misc{chen2025detectinganomaliesmachinelearning,
title={Detecting Anomalies in Machine Learning Infrastructure via Hardware Telemetry},
author={Ziji Chen and Steven W. D. Chien and Peng Qian and Noa Zilberman},
year={2025},
eprint={2510.26008},
archivePrefix={arXiv},
primaryClass={cs.PF},
url={https://arxiv.org/abs/2510.26008},
}
|