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Build an Accelerator Interconnect Diagnosis and Packet Loss Reviewer — implementation
Linked challenge: Build an Accelerator Interconnect Diagnosis and Packet Loss Reviewer
Prompt source
implementation88 lines11 sectionsNo variables1 code block
## Build
Analyze multi-GPU node interconnect counters and fabric error flags. Isolate degraded PCIe and NVLink links causing distributed all-reduce slowdowns and propose hardware repair actions.
## User
Cluster Network Engineer
## Workflow
1. Ingest normalized interconnect counter logs, CRC error rates, and replay counts.
2. Correlate interconnect packet drops with collective all-reduce latency degradation.
3. Prompt hosted Claude to inspect fabric topology anomalies and pinpoint failing lanes.
4. Synthesize a hardware maintenance ticket with port isolation and replacement guidance.
## Interface
Network topology graph visualizer highlighting degraded links with click-to-generate repair tickets.
## Data
Construct synthetic interconnect telemetry tables recording link CRC errors, replay events, and NVLink/PCIe port states across 8-GPU nodes.
Starter examples below are synthetic. They are not downloaded records.
## Source
[NVIDIA DCGM exporter](https://raw.githubusercontent.com/NVIDIA/dcgm-exporter/main/README.md)
Extracts DCGM NVLink error metrics, replay counters, and bandwidth saturation indicators from the DCGM exporter counters.
## Tools
anthropic-claude, pydantic-ai.
Interprets multi-hop interconnect error patterns and drafts clear, actionable physical maintenance steps.
Hosted Claude API for topological reasoning and maintenance ticket drafting. No access to proprietary cloud network routing.
## Deliverables
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.
## Acceptance
- Identifies failing interconnect links based on replay threshold exceedance (>1000/hr).
- Correlates link errors with distributed collective step latency degradation.
- Outputs structured repair tickets specifying switch port, GPU index, and replacement part.
## Starter Examples
```json
[
{
"input": {
"node_id": "hpc-gpu-node-12",
"fabric_errors": [
{
"gpu_src": 2,
"gpu_dst": 3,
"link_id": "nvlink-1",
"crc_errors": 45120,
"replay_count": 8900
},
{
"gpu_src": 0,
"gpu_dst": 1,
"link_id": "nvlink-0",
"crc_errors": 0,
"replay_count": 0
}
],
"step_time_ms": 340.5,
"baseline_step_time_ms": 180
},
"expected_output": {
"fault_detected": true,
"isolated_component": "GPU 2 -> GPU 3 NVLink 1",
"degradation_pct": 89.2,
"repair_action": "Reseat high-speed mezzanine interconnect or replace interposer ribbon cable on slot 2-3",
"ticket_priority": "urgent"
}
},
{
"input": {
"node_id": "hpc-gpu-node-03",
"fabric_errors": [
{
"gpu_src": 0,
"gpu_dst": 1,
"link_id": "nvlink-0",
"crc_errors": 2,
"replay_count": 1
}
],
"step_time_ms": 181.2,
"baseline_step_time_ms": 180
},
"expected_output": {
"fault_detected": false,
"isolated_component": "None",
"degradation_pct": 0.6,
"repair_action": "Maintain routine monitoring; error rate within allowable transient threshold",
"ticket_priority": "low"
}
}
]
```
## Safety
Diagnostic analysis only; does not reset physical PCIe registers or disable cluster switch fabrics.
Use mock external writes. Require human review before any real deployment.