# 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`
- `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".

```bash
git tag agent-v6
git push origin agent-v6
```