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Build a GPU Reservation and Team Cost Reconciliation Engine

Cross-reference multi-tenant GPU reservation allocations against DCGM active telemetry to pinpoint unused reserved capacity. Formulate team chargeback adjustments and idle reclamation actions for infrastructure finance teams.

EngineeringHosted by Vera

What you are building

Cross-reference multi-tenant GPU reservation allocations against DCGM active telemetry to pinpoint unused reserved capacity. Formulate team chargeback adjustments and idle reclamation actions for infrastructure finance teams.

Infrastructure Financial Operations Lead

Ingest scheduled GPU instance reservations and hourly team lease cost agreements.

Parse normalized DCGM GPU utilization telemetry records to compute actual hardware occupancy.

Calculate unallocated idle waste and formulate reconciled departmental cost attribution line items.

Invoke hosted Claude via Anthropic Python SDK to synthesize a finance reconciliation memo.

FastAPI service with a Streamlit cost reconciliation audit table and markdown report export.

Generate 20 normalized synthetic hourly GPU reservation records and matched DCGM average utilization export logs. Starter examples below are synthetic. They are not downloaded records.

NVIDIA DCGM exporter Extract DCGM exporter metric concepts like GPU utilization and active tenant profiling from the DCGM exporter overview.

anthropic, pydantic, fastapi. anthropic-claude structures departmental variance explanations into audit-ready reconciliation memos for infrastructure leadership. Hosted Claude API accessed via Anthropic Python SDK with structured JSON parsing capabilities.

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.

Correctly identifies reserved GPU hours where DCGM compute utilization fell below 5 percent.

Applies chargeback recalculations strictly within agreed hourly rate parameters.

Exports structured JSON reconciliation entries accompanied by a Claude-drafted audit memo.

```json [ { "input": { "team_id": "search-ranking", "reserved_gpus": 8, "reserved_hours": 24, "hourly_rate": 3.5, "avg_dcgm_utilization_pct": 2.1 }, "expected_output": { "allocated_cost": 672, "effective_used_cost": 14.11, "idle_waste_reclaimable": 657.89, "status": "underutilized_reclaim_recommended", "action_code": "DOWNSIZE_RESERVATION" } }, { "input": { "team_id": "core-nlp", "reserved_gpus": 4, "reserved_hours": 12, "hourly_rate": 3.5, "avg_dcgm_utilization_pct": 0 }, "expected_output": { "allocated_cost": 168, "effective_used_cost": 0, "idle_waste_reclaimable": 168, "status": "dormant_lease", "action_code": "CANCEL_RESERVATION" } } ] ```

Simulates financial cost chargebacks and resource adjustments without modifying actual cloud billing accounts or instance states. Use mock external writes. Require human review before any real deployment.

Rules

Simulates financial cost chargebacks and resource adjustments without modifying actual cloud billing accounts or instance states. Use mock external writes and human review.

How work is evaluated

Evaluation

Correctly identifies reserved GPU hours where DCGM compute utilization fell below 5 percent. Applies chargeback recalculations strictly within agreed hourly rate parameters. Exports structured JSON reconciliation entries accompanied by a Claude-drafted audit memo.

Submission format

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

Datasets

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

How submissions are scored

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

Complete workflow

Ingest scheduled GPU instance reservations and hourly team lease cost agreements. Parse normalized DCGM GPU utilization telemetry records to compute actual hardware occupancy. Calculate unallocated idle waste and formulate reconciled departmental cost attribution line items. Invoke hosted Claude via Anthropic Python SDK to synthesize a finance reconciliation memo.

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

Correctly identifies reserved GPU hours where DCGM compute utilization fell below 5 percent. Applies chargeback recalculations strictly within agreed hourly rate parameters. Exports structured JSON reconciliation entries accompanied by a Claude-drafted audit memo.

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

FastAPI service with a Streamlit cost reconciliation audit table and markdown report export.

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

Simulates financial cost chargebacks and resource adjustments without modifying actual cloud billing accounts or instance states.

Binary check

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

Learning goals

  • Ingest scheduled GPU instance reservations and hourly team lease cost agreements.

  • Parse normalized DCGM GPU utilization telemetry records to compute actual hardware occupancy.

  • Calculate unallocated idle waste and formulate reconciled departmental cost attribution line items.

  • Invoke hosted Claude via Anthropic Python SDK to synthesize a finance reconciliation memo.

Resources

Dataset notes

Generate 20 normalized synthetic hourly GPU reservation records and matched DCGM average utilization export logs. Supplied starter examples are synthetic, not source extracts.

Challenge setup

Challenge input

Input details are not configured.

Anthropic Claude

LLM for code generation & more

Evaluated output

No output checks are configured.

Evaluation checks
  • Ingest scheduled GPU instance reservations and hourly team lease cost agreements. Parse normalized DCGM GPU...
  • Correctly identifies reserved GPU hours where DCGM compute utilization fell below 5 percent. Applies charge...
  • FastAPI service with a Streamlit cost reconciliation audit table and markdown report export.
Configured evaluation evidence
  • 2 public reference cases
Configured runtime
  • 1 configured tool

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • Anthropic Claude · 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 GPU Reservation and Team Cost Reconciliation Engine