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Build an Accelerator Chassis Thermal Incident Root-Cause Diagnoser — implementation
Linked challenge: Build an Accelerator Chassis Thermal Incident Root-Cause Diagnoser
Prompt source
implementation88 lines11 sectionsNo variables1 code block
## Build
Analyze multi-sensor thermal logs during server shutdown events to reconstruct thermal runaway sequences. Generate hardware engineer post-mortem summaries and facility air-balance tasks using hosted Claude.
## User
Datacenter Hardware Reliability Specialist
## Workflow
1. Ingest high-frequency sensor streams tracking intake temperature, exhaust delta, and per-card junction temperatures.
2. Detect abnormal temperature gradient rates and identify the initial failing component.
3. Prompt hosted Claude to synthesize a formal hardware failure post-mortem and inspection steps.
4. Package diagnostic conclusions into a review dossier for facility HVAC and technician teams.
## Interface
Interactive timeline viewer displaying thermal gradient curves, component failure markers, and an audit report preview.
## Data
Create synthetic CSV logs simulating an 8-GPU chassis thermal runaway scenario with exhaust and junction metrics.
Starter examples below are synthetic. They are not downloaded records.
## Source
[NVIDIA DCGM exporter](https://raw.githubusercontent.com/NVIDIA/dcgm-exporter/main/README.md)
Adopts DCGM junction temperature and memory temperature counters to trace localized thermal spikes.
## Tools
anthropic, pydantic-ai, pandas.
anthropic-claude excels at correlating disparate multi-sensor anomalies into concise post-mortem findings.
Hosted Claude API for structured report compilation; pandas locally for metric gradients.
## 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
- Pinpoint the exact timestamp and GPU index of the initial thermal trip.
- Produce actionable inspection recommendations targeting physical heatsinks and fans.
- Ensure all output dossiers clearly delineate confirmed observations from suggested root causes.
## Starter Examples
```json
[
{
"input": {
"chassis_id": "rack-12-u3",
"incident_time": "2025-02-10T14:30:00Z",
"sensor_readings": [
{
"gpu_id": 0,
"junction_temp": 81,
"memory_temp": 75
},
{
"gpu_id": 1,
"junction_temp": 105,
"memory_temp": 99
},
{
"gpu_id": 2,
"junction_temp": 82,
"memory_temp": 76
}
]
},
"expected_output": {
"primary_fault_device": "GPU-1",
"critical_threshold_exceeded": true,
"hypothesized_cause": "Localized thermal paste degradation or liquid cooling plate blockage on GPU-1",
"immediate_task": "Reseat GPU-1 cold plate and verify thermal interface material integrity."
}
},
{
"input": {
"chassis_id": "rack-12-u4",
"incident_time": "2025-02-10T15:00:00Z",
"sensor_readings": [
{
"gpu_id": 0,
"junction_temp": 70,
"memory_temp": 68
},
{
"gpu_id": 1,
"junction_temp": 71,
"memory_temp": 69
}
]
},
"expected_output": {
"primary_fault_device": null,
"critical_threshold_exceeded": false,
"hypothesized_cause": "Nominal operations, no thermal runaway detected",
"immediate_task": "Continue standard telemetry monitoring."
}
}
]
```
## Safety
Read-only telemetry analysis; does not interact with datacenter control loops or physical power switches.
Use mock external writes. Require human review before any real deployment.