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Challenge

Build a Multi-GPU Interconnect Fabric Degradation Triage Engine

Parse normalized DCGM interconnect error counters to diagnose link degradation. Generate technician remediation itineraries and isolation steps using hosted Claude.

EngineeringHosted by Vera
Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Parse normalized DCGM interconnect error counters to diagnose link degradation. Generate technician remediation itineraries and isolation steps using hosted Claude.

Cluster Reliability Engineer

Ingest DCGM fabric and link error metrics, including replay counters and uncorrectable error registers.

Identify specific PCIe or NVLink lane degradations causing collective communication tail latency.

Prompt hosted Claude to interpret error bitmasks and generate precise physical cable reseating instructions.

Publish an interconnect triage manifest detailing faulted link endpoints and repair steps.

Topology map CLI that highlights faulted interconnects and prints field repair instructions.

Synthetic node interconnect metrics with normalized DCGM error counters across multi-GPU topological meshes. Starter examples below are synthetic. They are not downloaded records.

NVIDIA DCGM exporter Employs DCGM exporter fabric error metric principles (NVLink and PCIe replay counters) to pinpoint link failures.

crewai, anthropic. CrewAI coordinates specialized software agent roles: a telemetry parser agent and a hardware repair synthesis agent. Hosted a currently available hosted Claude model for fabric diagnostic reasoning; local system processes fabric counter logs.

Submit runnable source, pinned dependencies, setup commands, a working interface, example outputs, and a recorded end-to-end demo. Include tests and an action log.

Detects asymmetric link error distributions between GPU pairs.

Isolates damaged lanes and physical socket positions.

Generates hardware technician physical repair checklists via hosted Claude.

```json [ { "input": { "node_id": "compute-dgx-12", "gpu_source": 1, "gpu_target": 2, "link_type": "NVLink", "replay_errors_per_sec": 45000, "uncorrectable_errors": 2 }, "expected_output": { "link_status": "DEGRADED", "impact": "Severe bandwidth throttling on GPU 1-2 communications.", "remediation_action": "Schedule node maintenance, drain workloads, and reseat NVLink bridge connecting GPU 1 and GPU 2." } }, { "input": { "node_id": "compute-dgx-04", "gpu_source": 0, "gpu_target": 1, "link_type": "NVLink", "replay_errors_per_sec": 0, "uncorrectable_errors": 0 }, "expected_output": { "link_status": "HEALTHY", "impact": "Nominal throughput across interconnect mesh.", "remediation_action": "No hardware intervention required." } } ] ```

Read-only fabric counter analysis; does not issue live IPMI chassis commands or unbind driver devices. Use mock external writes. Require human review before any real deployment.

Rules

Participation guide

Read-only fabric counter analysis; does not issue live IPMI chassis commands or unbind driver devices. Use mock external writes and human review.

Delivery guide

How work is evaluated

Evaluation

Detects asymmetric link error distributions between GPU pairs. Isolates damaged lanes and physical socket positions. Generates hardware technician physical repair checklists via hosted Claude.

Submission format

Runnable source, pinned dependencies, setup commands, working interface, example outputs, recorded demo, tests, and action logs.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
4 scoring checks
Binary
1 pass or fail dimensions
Ordinal
3 scaled dimensions
Dimension 1

Complete workflow

Ingest DCGM fabric and link error metrics, including replay counters and uncorrectable error registers. Identify specific PCIe or NVLink lane degradations causing collective communication tail latency. Prompt hosted Claude to interpret error bitmasks and generate precise physical cable reseating instructions. Publish an interconnect triage manifest detailing faulted link endpoints and repair steps.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 2

Correctness and evidence

Detects asymmetric link error distributions between GPU pairs. Isolates damaged lanes and physical socket positions. Generates hardware technician physical repair checklists via hosted Claude.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 3

Review interface

Topology map CLI that highlights faulted interconnects and prints field repair instructions.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 4

Safe execution

Read-only fabric counter analysis; does not issue live IPMI chassis commands or unbind driver devices.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Ingest DCGM fabric and link error metrics, including replay counters and uncorrectable error registers.

  • Identify specific PCIe or NVLink lane degradations causing collective communication tail latency.

  • Prompt hosted Claude to interpret error bitmasks and generate precise physical cable reseating instructions.

  • Publish an interconnect triage manifest detailing faulted link endpoints and repair steps.

Resources and assets

Reference links and supporting material

Dataset notes

Synthetic node interconnect metrics with normalized DCGM error counters across multi-GPU topological meshes. Supplied starter examples are synthetic, not source extracts.

Challenge setup

Configuration summary for this challenge.

Challenge input

Input details are not configured.

crewAI

Multi-agent orchestration framework

Evaluated output

No output checks are configured.

Evaluation checks
  • Ingest DCGM fabric and link error metrics, including replay counters and uncorrectable error registers. Ide...
  • Detects asymmetric link error distributions between GPU pairs. Isolates damaged lanes and physical socket p...
  • Topology map CLI that highlights faulted interconnects and prints field repair instructions.
Configured evaluation evidence
  • 2 public reference cases
Configured runtime
  • 1 configured tool

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

Action Space
  • crewAI · Optional

Evaluation contract

  • Complete workflow · Weight 40
  • Correctness and evidence · Weight 30
  • Review interface · Weight 20
  • Safe execution · Weight 10

Reference cases

  • 2 public reference cases

Workflow tasks

No workflow tasks are configured.

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Build a Multi-GPU Interconnect Fabric Degradation Triage Engine