add self-looping quota retry: retry quota-blocked models every 30min in-process, no redeploy needed

main.py

@@ -6,7 +6,14 @@ xc_validation_strategy — 主入口
 Kunlunxin_p-800 的 config_content 模板和模型列表仍保留在代码中，未列入本次 GPU_JOBS）
 （/adminApi/async/task/create-contest-task，
 Bearer Token 认证），之后保持 HTTP 服务存活。
-同时暴露 /health（K8s 探活）和 /status（运行状态）。
+
+账号额度自动重试：如果某个模型提交时命中"当前等待中或运行中的异步模型验证
+任务数量已达上限"（账号额度已满），不算永久失败，会被留到下一轮；额度耗尽后
+本进程会原地等待 30 分钟，再自动重试所有因额度问题未提交成功的模型，如此循环，
+直至全部提交成功或进程被平台关闭——不需要重新部署新策略，循环逻辑在本进程内完成。
+非额度原因的失败（如模型已在验证中等）不会重试。
+
+同时暴露 /health（K8s 探活）和 /status（运行状态，含当前轮次/待重试数/下次重试时间）。
 """
 
 import json
@@ -6086,13 +6093,16 @@ TOTAL_MODELS = sum(len(models) for _, models in GPU_JOBS)
 # ══════════════════════════════════════════════════════════
 _state = {
     "strategy_id":   STRATEGY_ID,
-    "phase":         "starting",   # starting | submitting | done | error
+    "phase":         "starting",   # starting | submitting | waiting_retry | done | error
     "total":         TOTAL_MODELS,
     "submitted":     0,
     "failed":        0,
     "per_gpu":       {gpu: 0 for gpu, _ in GPU_JOBS},
     "started_at":    None,
     "finished_at":   None,
+    "round":               0,     # 当前是第几轮提交
+    "quota_blocked_remaining": 0, # 因额度上限暂未提交成功、等待下一轮重试的模型数
+    "next_retry_at":       None,  # 下一轮重试的预计时间（额度耗尽等待期间）
 }
 _shutdown = threading.Event()
 
@@ -6290,7 +6300,14 @@ ref_config:
 # ══════════════════════════════════════════════════════════
 #  业务逻辑
 # ══════════════════════════════════════════════════════════
-def _submit_task(token: str, gpu_type: str, model_id: str) -> Tuple[bool, str]:
+# 账号"等待中/运行中"任务数已达上限时平台返回的业务错误信息（子串匹配）；
+# 命中这个的模型不算永久失败，会在额度腾出空位后自动重试，不会被记作 failed
+QUOTA_FULL_MSG = "当前等待中或运行中的异步模型验证任务数量已达上限"
+# 额度耗尽后，隔多久自动重试一次剩余（因额度问题未提交成功）的模型
+RETRY_INTERVAL_SECONDS = 30 * 60  # 30 分钟
+
+
+def _submit_task(token: str, gpu_type: str, model_id: str) -> Tuple[bool, str, str]:
     headers = {
         "Content-Type": "application/json",
         "Authorization": f"Bearer {token}",
@@ -6323,13 +6340,14 @@ def _submit_task(token: str, gpu_type: str, model_id: str) -> Tuple[bool, str]:
         if result.get("code") == 0:
             task_id = result.get("data", {}).get("id", "")
             print(f"[worker] OK  {model_id} (GPU={gpu_type})  task_id={task_id}", flush=True)
-            return True, task_id
+            return True, task_id, ""
         else:
-            print(f"[worker] FAIL {model_id} (GPU={gpu_type}): {result.get('message')}", flush=True)
+            message = result.get("message") or ""
-            return False, ""
+            print(f"[worker] FAIL {model_id} (GPU={gpu_type}): {message}", flush=True)
+            return False, "", message
     except Exception as e:
         print(f"[worker] ERROR {model_id} (GPU={gpu_type}): {e}", flush=True)
-        return False, ""
+        return False, "", str(e)
 
 
 def _run_worker():
@@ -6340,23 +6358,44 @@ def _run_worker():
     token = AUTH_TOKEN
     print("[worker] 使用预设 Token，跳过登录", flush=True)
 
-    for gpu_type, model_list in GPU_JOBS:
+    # 待提交队列：保持 GPU_JOBS 里原有的 (gpu_type, model_id) 顺序
-        if _shutdown.is_set():
+    pending: List[Tuple[str, str]] = [
-            break
+        (gpu_type, model_id)
-        print(f"\n{'='*60}\n🚀 开始处理 GPU={gpu_type}，共 {len(model_list)} 个模型\n{'='*60}", flush=True)
+        for gpu_type, model_list in GPU_JOBS
+        for model_id in model_list
+    ]
 
-        for model_id in model_list:
+    round_num = 0
+    while pending and not _shutdown.is_set():
+        round_num += 1
+        _state["round"] = round_num
+        _state["phase"] = "submitting"
+        _state["next_retry_at"] = None
+        print(
+            f"\n{'='*60}\n🚀 第 {round_num} 轮，待提交 {len(pending)} 个模型\n{'='*60}",
+            flush=True,
+        )
+
+        quota_blocked: List[Tuple[str, str]] = []
+        for gpu_type, model_id in pending:
             if _shutdown.is_set():
                 break
-            ok, task_id = _submit_task(token, gpu_type, model_id)
+            ok, task_id, message = _submit_task(token, gpu_type, model_id)
             if ok:
                 _state["submitted"] += 1
                 _state["per_gpu"][gpu_type] += 1
                 successful.append((task_id, gpu_type, model_id))
+            elif QUOTA_FULL_MSG in message:
+                # 账号额度暂时满了，不算永久失败，留到下一轮重试
+                quota_blocked.append((gpu_type, model_id))
             else:
+                # 非额度原因失败（如重复提交等），不再重试
                 _state["failed"] += 1
 
-    # 写入结果文件
+        pending = quota_blocked
+        _state["quota_blocked_remaining"] = len(pending)
+
+        # 每轮结束都把已成功的结果落盘一次，避免中途重启丢失记录
         try:
             with open("submitted_validation_tasks.txt", "w", encoding="utf-8") as f:
                 for tid, gpu, mid in successful:
@@ -6364,11 +6403,24 @@ def _run_worker():
         except Exception:
             pass
 
+        if pending and not _shutdown.is_set():
+            next_retry = datetime.utcnow().timestamp() + RETRY_INTERVAL_SECONDS
+            _state["next_retry_at"] = datetime.utcfromtimestamp(next_retry).isoformat()
+            _state["phase"] = "waiting_retry"
+            print(
+                f"[worker] 第 {round_num} 轮结束：{len(pending)} 个模型因账号额度上限暂未提交，"
+                f"{RETRY_INTERVAL_SECONDS // 60} 分钟后自动重试（不部署新策略，本进程内循环）...",
+                flush=True,
+            )
+            _shutdown.wait(RETRY_INTERVAL_SECONDS)
+
     _state["finished_at"] = datetime.utcnow().isoformat()
     _state["phase"] = "done"
+    _state["quota_blocked_remaining"] = len(pending)
     print(
         f"[worker] 完成  submitted={_state['submitted']}  failed={_state['failed']}  "
-        f"total={_state['total']}  per_gpu={_state['per_gpu']}",
+        f"total={_state['total']}  per_gpu={_state['per_gpu']}  "
+        f"仍因额度未提交(如遇shutdown中断)={len(pending)}",
         flush=True,
     )
     # 提交完成后继续保持进程存活，等待平台停止