Completes the RunPod backend for testing.

Pods pool (runpod_worker.py):
- RunpodPodPool: provision via _select_gpu (cheaper=lowest $/hr, faster=SECURE+
  throughput, across allowed pools, spot-aware) → create_pod → wait_ready →
  vLLM health-check; least-loaded per-request acquire, provision-on-demand up to
  max_pods; background scaler thread tears down idle pods to min after
  idle_timeout_s, keeps min warm, reaps. get_pod_pool registry + pods_status().
- Budget enforcement at provision: global $/hr rate cap + per-model & global
  cumulative spend caps (rolling period) via the ledger; refuses over budget.

Cost ledger (runpod_ledger.py): persistent per-model + global spend, trailing
rolling windows (hour/day/week/month), totals() for stats. Final pod cost booked
on teardown; live pod cost added on top for enforcement/stats (no double count).

Backend (backends/runpod.py): pods mode wired to the pool (per-request acquire/
release; async stream acquires off the event loop); serverless unchanged. Config
adds global_cost_limit_usd + global_cost_period.

Global-gate bypass (app.py _model_capacity): runpod models get an effectively
unlimited LOCAL queue cap — remote GPUs aren't bound by the local concurrency
gate; RunPod's max_pods/workers are the real limit.

Spillover (app.py): a local model can burst to a RunPod SERVERLESS target when
local can't serve it — front reverse-proxies to the serverless OpenAI URL, guarded
to fall back to local on any error. Wired at the direct path for on_no_gpu
(no engine) + on_concurrency_full (queue full). NOTE partial: broker path,
streaming-concurrency, pods-target and on_local_error triggers are follow-ups.

UI: dedicated /admin/runpod stats page (pods, live $/hr vs caps, spend by period +
model) + nav link; Tasks page RunPod box; Settings split into category tabs
(JS-only, IDs/save untouched) + global spend cap fields.

Verified: imports in-image (8b16914ee24b), ledger + GPU selection (cheaper/faster)
unit-tested, all templates Jinja-parse. Live RunPod calls (provision/serverless)
still need an end-to-end test with the user's account.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01R6iugoNeEgt9PkajKrFyq4