| # 🛰️ 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)](https://arxiv.org/abs/submit/6934461) | |
| **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) | |
| **Version:** 1.0 | |
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| ## 📘 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. | |
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| ## 🧠 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**. | |
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| ## 🧩 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. | |
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