subsetchen/RevealTelemetryDatasetforMLInfraProfilingAnomalyDetection

🛰️ 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

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Dataset Sources

• Paper: Detecting Anomalies in Systems for AI Using Hardware Telemetry
• DOI: 10.5281/zenodo.17470313

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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

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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}

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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.

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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.

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Citation

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}, 
}