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".

git tag agent-v12
git push origin agent-v12