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` for liveness and `GET /ready` for submission readiness
- 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_ROUTING_STATE_PATH` default `.modelhub_state/routing_intelligence.json`
- `MODELHUB_OFFICIAL_CAPABILITIES_PATH` default `.modelhub_state/official_capabilities.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 `300`
- `MODELSCOPE_PAGE_INTERVAL_SECONDS` default `0.25`
- `MODELSCOPE_PAGE_CACHE_TTL_SECONDS` default `900`
- `MODELHUB_AGENT_VERIFY_CACHE_TTL_SECONDS` default `900`
- `MODELHUB_QUEUE_CLEANUP_INTERVAL_CYCLES` default `120`; cleanup also runs once at startup
- `MODELHUB_QUEUE_CLEANUP_READ_CONCURRENCY` default `6`
- `MODELHUB_QUEUE_CLEANUP_REPORT_PATH` default `.modelhub_state/queue_cleanup_latest.json`
- `MODELHUB_ARCHITECTURE_BLACKLIST_PATH` default `.modelhub_state/architecture_compatibility_blacklist.json`
- `MODELHUB_ARCHITECTURE_BLOCK_TTL_DAYS` default `30`
- `MODELHUB_ARCHITECTURE_COMMUNITY_PROBE_SIZE` default `50`
- `MODELHUB_ARCHITECTURE_COMMUNITY_LOOKBACK_DAYS` default `30`
- `MODELHUB_ARCHITECTURE_COMMUNITY_LATEST_LIMIT` default `5000`
- `MODELHUB_ARCHITECTURE_BOOTSTRAP_WORKERS` default `8`
- `MODELHUB_ARCHITECTURE_BOOTSTRAP_MAX_LOGS` default `0` (unlimited)
- `MODELHUB_RECENT_MODEL_RESERVE_SLOTS` default `5` per account
- `MODELHUB_RECENT_MODEL_DAYS` default `7`
- `MODELHUB_STATE_SYNC_REMOTE` default `https://dev.modelhub.org.cn/CoolBoy/submmit.git`
- `MODELHUB_STATE_SYNC_BRANCH` default `agent-state`
- `MODELHUB_STATE_SYNC_BATCH_SIZE` default `20`

Git state synchronization reads `modelhub_user_name`, `modelhub_user_email`, and
`modelhub_user_password` from the tracked private-deployment `.env`. Uppercase
`MODELHUB_GIT_USERNAME`, `MODELHUB_GIT_EMAIL`, and `MODELHUB_GIT_PASSWORD`
override them when supplied by the container.

## Adaptive GPU Strategy

When no explicit GPU override is supplied, the worker uses one deterministic
success-first scorer. The former 70/30 long-term/recent quota no longer controls
traffic and there is no self-funded random exploration. Evidence falls back from
the most specific qualifying cohort to broader community evidence:

- local task+GPU+framework+architecture+quantization+load-size bucket (20 samples)
- local task+GPU+framework+architecture+quantization (40 samples)
- local task+GPU+framework (100 samples)
- official community task+GPU+framework aggregate
- a conservative global prior when the platform has no observations

Each rate is ranked by its Wilson lower confidence bound. Routes within five
percentage points of the best success estimate use expected successful
completions per hour as the tie-breaker:

- recent public success quality, scored with a strongly weighted Wilson lower confidence bound
- estimated backlog hours (`waiting / recent completions per hour`) as a bounded tie-breaker
- 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 and uses retry backoff. Critical official capability data is different:
without a usable GPU catalog, task route, framework catalog, build config, or
model/GPU uniqueness response, the entire submission cycle pauses fail-closed.

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 300 public samples and a safe Wilson lower bound
are eligible. 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 beats the best incumbent by
at least 10%. 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; production routing never substitutes a hand template
when both the live config and its bounded last-good cache are unavailable.

Only platform-accepted tasks count. Routing is recalculated from the current
evidence on every scan; accepted route counters and lifetime sufficient statistics
are stored in `.modelhub_state/routing_intelligence.json`.

Five consecutive local failures open a 12-hour GPU/framework circuit breaker.
A sub-20% success rate over the latest 20 terminal tasks opens a 6-hour breaker.
Platform/infrastructure failures are excluded from long-term compatibility rates
and model/profile breakers. Three consecutive platform failures on a GPU/framework
instead open a short 30-minute breaker, so a temporary broken runner or lack of an
idle card does not permanently poison otherwise successful evidence.
Candidate shortages expand the model search window; they never unlock an
unvetted GPU or framework.

## Durable State Branch

The hosted worker synchronizes an allowlisted runtime snapshot to the orphan
`agent-state` branch of this repository. Before each batch of at most 20 API
submissions it pushes write-ahead intents containing model, GPU, task, framework,
safe parameter vector, and a config fingerprint. It pushes task IDs and results
after the batch. If either push fails, no further submission begins until the
same snapshot can be synchronized.

At startup the worker verifies the manifest and file checksums, restores local
capacity, outcomes, routing evidence, architecture rules, exclusions, intents,
and active-task context, then reconciles every account against the platform.
Pending intents are held for two hours before being released as unconfirmed.
The branch keeps 30 days of structured events plus lifetime aggregate counters;
raw stdout, credentials, request headers, downloaded archives, and full configs
are never copied. Git authentication uses a temporary `GIT_ASKPASS` helper, so
the password is absent from command arguments, remotes, commits, and logs.

`GET /health` reports process liveness. `GET /ready` returns HTTP 200 only after
state recovery, state synchronization, and critical official capability checks
are usable; otherwise it returns HTTP 503 without forcing a liveness restart.

Failed-task archives are also classified conservatively. When ModelHub explicitly
says that the selected framework does not support the model or architecture, the
runner learns an exact GPU + framework + task type + architecture block from the
candidate repository's `config.json`. Repository names are never used as
architecture evidence. Exact `architectures` values take priority and
`model_type` is also retained when the runtime explicitly says Transformers does
not recognize that type; missing metadata does not create a block. Generic unsupported operators, attention backends, GPU types,
quantization failures, and OOMs cannot enter this blacklist. A newer success for
the same exact combination clears the block, and otherwise it expires after 30
days. Set `MODELHUB_ARCHITECTURE_BLOCK_TTL_DAYS` to a value from 1 to 365 to
change that window. The stats report exposes `architectureCompatibilityBlocks`
and per-GPU/framework block counts. The live snapshot is written to
`.modelhub_state/architecture_compatibility_blacklist.json`.

Before the first cleanup/submission cycle, the poller probes the latest public
failed-validation records for usable failure archives. If the public endpoint
does not expose those archives (the current API behavior), it falls back to a
parallel, complete history scan of every configured account. Ledger metadata is
used when present; otherwise the task type is recovered from ModelHub's task
level and the selected framework is conservatively recovered from the target
container image in the failure archive. All usable historical failures are
classified once at startup and cached in `outcomes/submissions.jsonl`; subsequent
poll cycles continue incremental learning every three cycles. Empty or
incomplete public evidence is logged as a fallback, never as proof that no
architecture incompatibility exists.

Before a candidate reaches the submit queue, failure-informed preflight checks
the actual ModelScope repository structure and file sizes. Non-GGUF text
frameworks require root-level config, weights, and tokenizer assets. The memory
gate recursively totals the entire repository—including duplicate weight formats
and nested shards—and applies ModelHub's observed 20% loading overhead. It covers
all 14 GPU types currently marked `canVerify=true`; nine capacities come directly
from structured ModelHub OOM reports and five from published specifications until
ModelHub supplies a stronger observation. If a known GPU's repository file sizes
are incomplete, the candidate is deferred rather than guessed. Template context
length is also clamped to the model's advertised limit. A newly introduced GPU
with no capacity evidence is likewise deferred. Override or extend known
capacities with
`MODELHUB_GPU_MEMORY_GIB_JSON`, for example
`{"New_gpu": 64}`.

At poller startup, the same deterministic memory and learned architecture gates are applied to existing
`waiting` and `running` tasks across every configured account. A task is stopped
through `PUT /api/async/task/stop-create-contest-task` only when its own current
recursive repository size, multiplied by ModelHub's observed `1.20` overhead,
exceeds the known capacity of its selected GPU. The task ID is fetched and
stopped with the token belonging to that account, and its active state is
rechecked immediately before the mutation. Missing file sizes, unknown GPU
capacities, listing failures, and tasks that have already changed state all fail
closed and are never stopped. This does not match against another task from the
same model or infer failure from historical similarity. The cleanup repeats
every 120 poll cycles by default and writes its full evidence report to
`.modelhub_state/queue_cleanup_latest.json`.

Architecture cleanup reads the live queue task level and joins local submission
or ledger metadata when available, then reads the model's `config.json`. An
exact GPU + framework + task type + architecture blacklist hit can authorize
cancellation. For legacy waiting tasks whose framework is absent from the API
and local state, every currently listed framework must have an explicit matching
block; a partial match is never enough. Matching waiting tasks are stopped;
running tasks remain protected and their state is rechecked again immediately
before the stop call. Failed outcomes are synchronized every three poll cycles.
When fixed `MODEL_NOT_SUPPORTED` text adds a new blacklist entry, a lightweight
architecture-only cleanup runs immediately without repeating repository-size or
model-age scans.

Each account dynamically reserves its last 5 known-capacity positions for
models updated within seven days. If an account's discovered limit is 100, 200,
or 500, older models stop at positions 95, 195, or 495 respectively. Old-model
reservations are made under the same account lock as capacity reservations, so
concurrent submissions cannot enter the reserved suffix. Once every account's
old-model allowance is exhausted, discovery is capped at the seven-day window.
Unknown model timestamps are treated as old for new submissions. When capacity
probing raises an account's known limit, its old-model boundary moves with it.
If an account's active-count read fails, old-model admission is fail-closed for
that account while other accounts remain eligible. Date-deferred candidates are
recorded as `age_policy_skipped`, release their temporary claim, and do not enter
failure statistics or permanent exclusions.

Model age is admission-only. Startup and scheduled queue cleanup never cancel a
task because it is older than seven days, beyond a capacity-minus-five boundary,
or above a temporarily reduced platform limit. Existing waiting and running
tasks remain untouched by date policy. Cleanup still removes deterministic OOM
tasks and exact learned architecture incompatibilities under their existing
state and evidence checks, preventing the old submit/cleanup/resubmit loop.

A full 12-account failure audit found that 31.25% of failures had no usable log
and another 23.52% had only ambiguous runtime evidence. These unresolved results
now remain visible as `unresolvedFailureCount` but do not reduce GPU/framework
decision success rates, create model/GPU cooldowns, or open attributable-failure
circuits. Explicit OOM, repository, context, tokenizer, model-load, operator, and
framework-architecture evidence remains attributable; identified platform
failures continue to use short infrastructure circuits instead.

The verified capacities, safe repository-size boundaries, evidence hierarchy,
and source links are recorded in
`docs/gpu-memory-capacity-2026-08-10.md`.

The online worker makes zero LLM calls. Candidate admission, GPU/framework
selection, queue cleanup, and failure feedback are deterministic and based on
repository metadata, platform capabilities, public outcomes, and explicit error
signatures. Merely storing a DashScope key in `.env` or setting a Qwen/LLM
environment variable does not activate inference. The standalone classifier
module remains only as an offline research helper for human-reviewed batches of
previously unseen errors; it is not wired into submission or outcome sync.

Outcome synchronization downloads a bounded set of failure archives for
submissions created by this worker (at most 40 per sync, four workers, three
download attempts). Deterministic signatures classify memory, repository layout,
context-length, storage, and platform faults. Unresolved runtime errors remain
explicitly ambiguous for later rule development instead of being sent to an LLM.
Signed log URLs remain in the ignored local outcome store and are removed after
classification.

The 12-account failure study and routing rationale are recorded in
`docs/failure-analysis-2026-08-10.md`.

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.
Version `2026.08.05.1` removes self-funded GPU exploration, switches accepted
traffic to 70/30 long-term/recent exploitation, raises the public framework gate
to 300 samples, makes success dominate queue pressure, and adds recent local
GPU/framework circuit breakers.
Version `2026.08.10.2` adds evidence-backed sizing for every currently verifiable
GPU, recursive repository-size checks, deterministic failure-aware preflight,
and rate-limited lazy Qwen review for unresolved semantic cases.
Version `2026.08.10.3` selects `qwen3.7-flash` by default and recognizes the
repository root `.env` key named `dashscope` without logging its value.
Version `2026.08.11.1` adds account-owned cancellation of queued tasks that are
deterministically over the selected GPU's ModelHub memory boundary, with a
second active-state check and fail-closed handling for incomplete evidence.
Version `2026.08.11.2` removes LLM inference from every online path; credentials
alone cannot activate it, and unresolved cases remain available for offline,
human-reviewed rule development.
Version `2026.08.11.3` adds atomic per-account 80/7-day admission, prevents
history expansion once old-model positions are exhausted, performs a strict
position-80 startup cleanup, and relaxes scheduled age cleanup to position 95
after OOM cleanup and a second queue check.
Version `2026.08.11.4` replaces fixed queue positions with per-account dynamic
boundaries derived from each discovered capacity: limit minus 10 for admission
and startup cleanup, then limit minus 5 for scheduled dynamic cleanup.
Version `2026.08.12.1` protects running tasks from age-based cleanup and records
worker-initiated stops as `policy_cancelled`, excluding them from success-rate,
failure-cooldown, and circuit-breaker evidence.
Version `2026.08.12.2` learns conservative, expiring GPU/framework/architecture
compatibility blocks only from explicit ModelHub failure text, matches candidate
`config.json` metadata instead of repository names, and lets newer success
evidence clear stale blocks.
Version `2026.08.12.3` extracts unsupported `model_type`/`architectures` from the
platform's fixed failure wording, persists a dynamically growing blacklist, and
immediately removes exact-matching waiting tasks with two active-state checks.
Version `2026.08.12.4` bootstraps architecture feedback before the first cleanup:
it prefers recent public failure details when available, otherwise scans the
complete history of every configured account, recovers missing task/framework
metadata from task levels and target images, then continues incremental learning.
Version `2026.08.12.5` enriches every cleanup from the live active queue's task
level metadata. If a legacy waiting task has no recoverable framework, cleanup
queries the current ModelHub framework catalog and cancels it only when its
architecture is explicitly blocked on every listed framework.
Version `2026.08.12.6` makes model age admission-only: each account reserves its
last five dynamic-capacity positions for seven-day models, unknown account
counts fail closed for older candidates, and no startup or periodic cleanup can
cancel a task by date. It also keeps unclassified/ambiguous historical failures
neutral in GPU/framework success feedback while preserving deterministic OOM
and architecture cleanup.
Version `2026.08.15.3` replaces the 70/30 quota with hierarchical success-first
routing, dynamically gates models through the official GPU/task/framework/config
APIs, enriches ModelScope metadata and model lineage, learns only proven safe
config vectors, and adds crash-safe write-ahead state synchronization to the
`agent-state` branch. It also exposes `/ready` and extends deterministic cleanup
to officially removed waiting GPU/framework routes. State recovery uses the
pure-Python Dulwich client, avoiding slow OS package installation during builds.

## Deploy

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

```bash
git tag -a agent-v27 -m "ModelHub agent 2026.08.15.3"
git push origin main agent-v27
```