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Challenge

Build an Accelerator Chassis Power Budget and Throttling Reviewer

Correlate server chassis electrical power caps with per-device DCGM utilization logs. Formulate dynamic power balancing schedules and throttling prevention plans using hosted Claude.

EngineeringHosted by Vera
Challenge brief

What you are building

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

Correlate server chassis electrical power caps with per-device DCGM utilization logs. Formulate dynamic power balancing schedules and throttling prevention plans using hosted Claude.

Datacenter Power and Facilities Engineer

Ingest rack PDU power limits and per-accelerator DCGM power consumption telemetry using pandas.

Detect electrical headroom deficits where total peak draw risks tripping rack-level circuit breakers.

Invoke hosted Claude via Anthropic API to synthesize power cap balancing proposals across non-critical workers.

Generate a structured power allocation manifest detailing per-GPU watt limits validated via pydantic.

Dashboard visualizing rack power envelope utilization and Claude-recommended power capping profiles.

Simplified synthetic app records representing power telemetry logs from server PDUs and DCGM metrics across 8-accelerator chassis. Starter examples below are synthetic. They are not downloaded records.

NVIDIA DCGM exporter Extracts DCGM power metrics (DCGM_FI_DEV_POWER_USAGE) and hardware power limit registers.

dspy, anthropic, pandas, pydantic. DSPy compiles robust prompt pipelines to guarantee hosted Claude produces schema-compliant power allocation limits. Hosted a currently available hosted Claude model accessed via official SDK; does not emit live electrical breaker or firmware commands.

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.

Identifies potential PDU overload conditions when peak sum exceeds rack threshold.

Calculates balanced power allocation across active server chassis.

Produces hosted Claude explanations for selected power capping setpoints.

```json [ { "input": { "pdu_max_kw": 12, "chassis_count": 4, "unconstrained_peak_kw": 14.5, "priority_chassis_index": 0 }, "expected_output": { "breaker_trip_risk": true, "recommended_chassis_caps_kw": [ 4, 2.6, 2.6, 2.6 ], "total_budget_kw": 11.8, "allocation_rationale": "Chassis 0 maintained at peak demand for SLA compliance; remaining chassis capped to prevent PDU overload." } }, { "input": { "pdu_max_kw": 12, "chassis_count": 4, "unconstrained_peak_kw": 9.2, "priority_chassis_index": 0 }, "expected_output": { "breaker_trip_risk": false, "recommended_chassis_caps_kw": [ 3.5, 2.5, 2, 1.2 ], "total_budget_kw": 9.2, "allocation_rationale": "Operating load is well within the 12 kW PDU envelope. No power capping required." } } ] ```

Provides advisory power capping allocations; does not issue live IPMI power cap updates. Use mock external writes. Require human review before any real deployment.

Rules

Participation guide

Provides advisory power capping allocations; does not issue live IPMI power cap updates. Use mock external writes and human review.

Delivery guide

How work is evaluated

Evaluation

Identifies potential PDU overload conditions when peak sum exceeds rack threshold. Calculates balanced power allocation across active server chassis. Produces hosted Claude explanations for selected power capping setpoints.

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 rack PDU power limits and per-accelerator DCGM power consumption telemetry using pandas. Detect electrical headroom deficits where total peak draw risks tripping rack-level circuit breakers. Invoke hosted Claude via Anthropic API to synthesize power cap balancing proposals across non-critical workers. Generate a structured power allocation manifest detailing per-GPU watt limits validated via pydantic.

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

Identifies potential PDU overload conditions when peak sum exceeds rack threshold. Calculates balanced power allocation across active server chassis. Produces hosted Claude explanations for selected power capping setpoints.

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

Dashboard visualizing rack power envelope utilization and Claude-recommended power capping profiles.

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

Provides advisory power capping allocations; does not issue live IPMI power cap updates.

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 rack PDU power limits and per-accelerator DCGM power consumption telemetry using pandas.

  • Detect electrical headroom deficits where total peak draw risks tripping rack-level circuit breakers.

  • Invoke hosted Claude via Anthropic API to synthesize power cap balancing proposals across non-critical workers.

  • Generate a structured power allocation manifest detailing per-GPU watt limits validated via pydantic.

Resources and assets

Reference links and supporting material

Dataset notes

Simplified synthetic app records representing power telemetry logs from server PDUs and DCGM metrics across 8-accelerator chassis. Supplied starter examples are synthetic, not source extracts.

Challenge setup

Configuration summary for this challenge.

Challenge input

Input details are not configured.

DSPy

Prompt and LM program optimization framework.

Evaluated output

No output checks are configured.

Evaluation checks
  • Ingest rack PDU power limits and per-accelerator DCGM power consumption telemetry using pandas. Detect elec...
  • Identifies potential PDU overload conditions when peak sum exceeds rack threshold. Calculates balanced powe...
  • Dashboard visualizing rack power envelope utilization and Claude-recommended power capping profiles.
Configured evaluation evidence
  • 2 public reference cases
Configured runtime
  • 3 configured tools

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
  • DSPy · Optional
Training Infra
  • DSPy · Optional
Orchestration
  • DSPy · 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 an Accelerator Chassis Power Budget and Throttling Reviewer