Diagnosing Distributed AI Workload Failures with NVIDIA Attribution
An open-source framework for correlating cluster telemetry into actionable root-cause diagnoses.
1The Challenge of Distributed Failures
In large-scale AI training, a single hardware fault often triggers cascading symptoms across multiple nodes. A localized GPU issue, such as an XID 149 error, frequently manifests as widespread NCCL communication failures. This makes identifying the true origin of a workload crash difficult without automated, context-aware diagnostic tools for complex clusters.
2Introducing NVIDIA Attribution
NVIDIA Attribution is an open-source tool designed to correlate logs, metrics, and scheduler states into structured diagnoses. By analyzing failures within the specific context of a workload rather than isolated node events, it provides a clear evidence trail. It identifies the root cause, affected nodes, and supporting symptoms for infrastructure teams.
3System Architecture and Components
The system consists of two primary components: the Attribution Service and the Attribution Blueprint. The Service exposes a REST API for the core attribution engine, while the Blueprint acts as a Python orchestrator. It connects cluster schedulers like Slurm or Kueue and telemetry sources like Loki and PromQL to the analysis engine.
4Four-Stage Diagnostic Process
The attribution process follows four distinct stages: scoping the workload, collecting observations, extracting and correlating events, and returning a structured result. Deterministic rules process raw inputs to filter noise. This ensures that the final output provides a terminal state and attributed domain, allowing for reproducible results across various distributed AI environments.
5Real-World Production Scale
NVIDIA has utilized this system internally for years to manage massive AI infrastructure. It currently analyzes millions of workloads weekly across hundreds of thousands of GPUs. In one RDMA-backed cluster, the tool successfully correlated port errors and connection resets to identify specific fabric failure patterns, significantly accelerating hardware repair and congestion tuning.
6Decoding Complex Failure Patterns
During a failure, the tool performs per-node scrubbing and job-wide aggregation. For instance, if an NVLink signal integrity issue occurs, the system decodes the error words and demotes secondary NCCL reports to 'interesting' evidence. This refinement process ensures that the primary hardware fault is surfaced as the definitive root cause.
7Deployment and Customization Flexibility
Teams can deploy the Attribution Service and Blueprint in various configurations, such as one-to-one or one-to-many setups. Because the engine uses standardized contracts for execution context and observations, developers can easily implement custom connectors for their specific schedulers. This modularity allows the same diagnostic logic to be applied across heterogeneous cluster environments.
8Getting Started with Quickstart
Practitioners can begin by cloning the repository and running the provided sample workflow using Docker and the uv package manager. The quickstart includes sample evidence that demonstrates how to inspect diagnoses via a local dashboard. This allows users to verify the rule-based analysis before integrating the tool into their live production clusters.
9Strategic Value for Infrastructure
Open-sourcing this framework enables teams to encode failure patterns once and apply them globally. By routing structured results into incident management systems, organizations can automate recovery actions. This transition from manual log parsing to deterministic, workload-centric attribution is essential for maintaining high availability in modern, large-scale distributed AI training operations.