🛰️ Reveal: Curated Telemetry Dataset for Machine Learning Infrastructure Profiling and Anomaly Detection
Authors: Ziji Chen, Steven W. D. Chien, Peng Qian, Noa Zilberman
Institution: University of Oxford
Paper: Detecting Anomalies in Systems for AI Using Hardware Telemetry (arXiv, 2025)
License: CC BY 4.0
Version: 1.0
📘 Overview
Reveal is a large-scale, curated telemetry dataset for studying performance profiling, anomaly detection, and infrastructure optimization in modern machine learning (ML) systems.
It captures hardware-level signals from both GPU-equipped clusters while running over 30 popular ML workloads across NLP and computer vision domains.
This dataset was collected using the Reveal framework, a hardware-centric profiling and unsupervised anomaly detection system introduced in our paper.
Reveal observes CPU, GPU, memory, network, and storage metrics without requiring access to user workloads, making the dataset ideal for operator, side anomaly detection and system performance analysis.
🧠 Motivation
Modern ML systems are tightly coupled across hardware and software layers, yet operators often lack visibility into workloads due to virtualization and containerization.
Reveal bridges this gap by providing a hardware-only telemetry view, enabling anomaly detection and performance diagnosis without application instrumentation.
🧩 Dataset Description
| Category | Description |
|---|---|
| Sampling rate | 100 ms per metric |
| Metric types | ~150 raw metric types per host |
| Subsystems covered | CPU, GPU, Memory, Network, Disk |
| Time-series channels | ~700 per host |
| Workloads | 30+ ML applications including BERT, BART, ResNet, ViT, VGG, DeepSeek, LLaMA, Mistral |
| Datasets used | GLUE/SST2, WikiSQL, PASCAL VOC, CIFAR, MNIST |
| Systems | Dual-host GPU HPC cluster |
| Telemetry tools | perf, procfs, nvidia-smi, Linux utilities |
Each record corresponds to a time-series window of low-level system metrics.