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
MODELHUB_AGENT_MAX_SUBMITS_PER_RUN default 0 (fill all currently available slots)
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_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

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.

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 this behavior.

Deploy

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

git tag agent-v6
git push origin agent-v6