# 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` - `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 local 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%: round-robin exploration across every currently supported GPU - 20%: the best GPU among the latest 1,000 terminal tasks 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. ## Deploy Create a tag and submit the repository URL plus tag in "我的适配智能体". ```bash git tag agent-v6 git push origin agent-v6 ```