submmit/README.md

# ModelHub Adaptation Agent

This repository is packaged for the ModelHub XC agent platform.

## Platform Contract

- Root-level `Dockerfile`
- Listens on port `8080`
- Exposes `GET /health`
- Handles `SIGTERM`
- Reads platform-provided `STRATEGY_ID` and attaches it to task submissions as `strategyId`

The root `main.py` starts a lightweight health server and runs the existing
submission poller in a child process.

## Runtime Environment

The image includes multi-account ModelHub and ModelScope token fallbacks for
the agent platform. Environment variables can override them without rebuilding
the image.

- `MODELHUB_XC_TOKEN`, `XC_TOKEN`, `XC_TOKEN2...`, or `MODELHUB_XC_TOKENS` for ModelHub API authentication
- `MODELHUB_JWT_TOKEN` or `JWT_TOKEN` can be used instead when the platform provides a JWT
- `MODELSCOPE_API_TOKEN` or `MODELSCOPE_TOKEN` optional override for the embedded ModelScope fallback token
- `STRATEGY_ID` is expected to be injected by the ModelHub agent platform and is attached to submissions for strategy attribution; it is not an API authentication token

Optional tuning:

- `MODELHUB_AGENT_POLL_INTERVAL_SECONDS` default `15`
- `MODELHUB_AGENT_IDLE_INTERVAL_SECONDS` default `60`
- `MODELHUB_AGENT_POST_CYCLE_COOLDOWN_SECONDS` default `2`
- The hosted entrypoint always uses `--max-submits-per-run 0` so stale deployment
  settings cannot restrict a refill cycle to five submissions.
- `MODELHUB_AGENT_ACTIVE_TASK_CAP` default `100` per account
- `MODELHUB_CAPACITY_PROBE_INTERVAL_CYCLES` default `3`
- `MODELHUB_CAPACITY_STATE_PATH` default `.modelhub_state/account_capacity.json`
- `MODELHUB_AGENT_ACTIVE_COUNTS_TTL_SECONDS` default `15`
- `MODELHUB_AGENT_RESERVATION_TTL_SECONDS` default `120`
- `MODELHUB_AGENT_INSTANCE_ID` optional stable worker identity used to spread concurrent agents across accounts and candidates
- `MODELHUB_AGENT_CLAIMS_PATH` default `.modelhub_state/submission_claims.jsonl`
- `MODELHUB_SUBMISSION_EXCLUSIONS_PATH` default `.modelhub_state/submission_exclusions.jsonl`
- `MODELHUB_AGENT_DAILY_TARGET`
- `MODELHUB_AGENT_MIN_DOWNLOADS`
- `MODELHUB_AGENT_GPUS`
- `MODELHUB_AGENT_EXTRA_ARGS`
- `MODELHUB_GPU_STRATEGY_STATE_PATH` default `.modelhub_state/gpu_strategy.json`
- `MODELHUB_MARKET_INTELLIGENCE_PATH` default `.modelhub_state/market_intelligence.json`
- `MODELHUB_MARKET_QUEUE_REFRESH_SECONDS` default `600`
- `MODELHUB_MARKET_FRAMEWORK_REFRESH_SECONDS` default `21600`
- `MODELHUB_MARKET_THROUGHPUT_WINDOW_HOURS` default `6`
- `MODELHUB_MARKET_FRAMEWORK_MIN_SAMPLES` default `100`
- `MODELSCOPE_PAGE_INTERVAL_SECONDS` default `0.25`
- `MODELSCOPE_PAGE_CACHE_TTL_SECONDS` default `900`
- `MODELHUB_AGENT_VERIFY_CACHE_TTL_SECONDS` default `900`

## Adaptive GPU Strategy

When no explicit GPU override is supplied, the worker uses a queue-aware 50/30/20
strategy generation:

- 50%: the three long-term GPUs with the best Wilson lower confidence score and at least 100 terminal samples
- 30%: exploration across every currently supported GPU
- 20%: the top recent GPUs among the latest 1,000 terminal tasks

The 50/30/20 category ratio remains exact across accepted tasks. Inside each
category, weighted fair scheduling combines the category's historical rank with
live public market data:

- estimated backlog hours (`waiting / recent completions per hour`) instead of raw queue length
- recent public success quality, scored with a Wilson lower confidence bound
- machine availability, running workers, and advertised concurrency
- a circuit breaker for unavailable or apparently stalled GPU pools

This optimizes expected successful completions rather than blindly selecting the
smallest queue. Queue/throughput data is refreshed every 10 minutes and persisted
in `.modelhub_state/market_intelligence.json`. A failed refresh keeps the last good
snapshot, uses a retry backoff, and never blocks normal submissions.

For each compatible model/GPU pair, the worker also ranks the GPU's supported
frameworks using ModelHub's public aggregate `modelCount` and `successCount` data,
then blends in the worker's own GPU+framework outcomes with a capped weight.
Only frameworks with at least 100 samples receive statistical priority; the
legacy safe framework order remains the fallback. Framework statistics refresh
every 6 hours, so they do not add per-model API traffic. Newly published
frameworks are discovered automatically, but receive no novelty bonus: they can
win only when their confidence-adjusted success score is better. A new framework
is eligible only after the authenticated official build-config endpoint returns
a complete config that passes local structure, placeholder, framework-name, and
GPU-parallelism validation. Valid official configs are cached and refreshed with
the framework snapshot; local templates remain the fail-safe fallback.

Only platform-accepted tasks count. After exactly 200 accepted tasks, the next
poll cycle reloads all account history, generates a new immutable strategy snapshot,
and resets the generation counters to 100/60/40 targets. The active snapshot and
progress are stored in `.modelhub_state/gpu_strategy.json`.

ModelScope HTTP 429 responses use exponential backoff and `Retry-After`. Successful
pages remain cached, so a later cycle retries the failed page instead of restarting
the whole pagination scan.

## Adaptive Candidate Discovery

The configured recent window remains the fast path. If it contains no usable
model/GPU combinations, the same run progressively expands discovery to the last
7 days, the last 30 days, and finally older history (up to 3,000 models). Scanning
stops as soon as enough replacement candidates have been found.

Model verification results are reused for 15 minutes across poll cycles, and a
locally failed model/GPU pair cools down for 24 hours instead of being excluded
forever. The `[scan]` lines show every expansion stage, while `[daily] wave_done`
includes `skip_reasons` so an empty candidate pool is directly diagnosable.

Community deduplication is scoped to the exact model/GPU combination. A model
adapted on one GPU remains eligible for another GPU. Before each submission the
worker performs an uncached community check; a lookup failure defers the task
instead of failing open. Platform uniqueness rejections are persisted per
model/GPU in `.modelhub_state/submission_exclusions.jsonl` and are not retried.

## Concurrent Agents

The token pool keeps a local reservation for every in-flight submission, so a
lagging platform count cannot send all concurrent requests to the same account.
If another process fills an account first, the submission is retried immediately
against another account with capacity.

Workers that share a filesystem also coordinate model/GPU claims through
`.modelhub_state/submission_claims.jsonl`. Workers in isolated containers use
different candidate ordering (derived from `STRATEGY_ID`, instance ID, or
hostname), which reduces duplicate work while the platform remains the final
authority for account capacity and model/GPU uniqueness.

If the platform reports that a model/GPU is already being validated, the claim
is retained and the runner immediately draws replacement candidates from the
same scan instead of retrying the duplicate every cycle. Startup logs and the
health response expose `agent_version`; version `2026.08.02.3` or newer includes
duplicate replacement behavior, while version `2026.08.02.4` adds adaptive
candidate-window expansion and skip-reason reporting. Version `2026.08.02.5`
adds fail-closed model/GPU prechecks and persistent uniqueness exclusions.
Version `2026.08.04.1` adds queue/throughput intelligence, GPU health circuit
breaking, weighted-fair scheduling, live framework/config discovery, and
confidence-ranked public-plus-local framework selection.

## Deploy

Create a tag and submit the repository URL plus tag in "我的适配智能体".

```bash
git tag agent-v12
git push origin agent-v12
```