Reveal: Hardware Telemetry Dataset for ML Infrastructure Profiling

Reveal is a large-scale, curated dataset of hardware telemetry collected from high-performance computing systems running diverse machine learning workloads. It was created by subsetchen and last updated on January 14, 2026. The dataset is intended to enable reproducible research on system-level profiling and anomaly detection.

Use Cases

• Unsupervised anomaly detection based on hardware telemetry signals.
• System-level profiling of ML workloads based on performance metrics.
• ML infrastructure optimization based on hardware behavior patterns.

Strengths

• Described as a large-scale dataset.
• Curated from high-performance computing environments.
• Covers diverse machine learning workloads.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Freshness should be verified as the last update timestamp is in the future (2026).

Provenance

Source

subsetchen

Collection Method

Collected from high-performance computing systems running ML workloads.

Freshness

Last updated 2026-01-14 12:38:33