---
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: ziji.chen@eng.ox.ac.uk)  
- **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, Memory, Network, Storage}  

**Additional Notes**

A complete list of metrics and their descriptions can be found in `MetricDescription.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   

#### 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 (NVIDIA V100 & H100, Intel Xeon CPUs, InfiniBand HDR100)  

#### 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}, 
}