add automatic model task discovery and verification

README.md

@@ -4,6 +4,9 @@
 
 ## 能做什么
 
+- 后台定时读取信创模盒公开热门模型，自动发现文本生成候选模型。
+- 选择较小且与官方 EngineX 支持家族相符的候选，按模型与卡型查询历史任务去重。
+- 当运行环境同时提供私密令牌、策略 ID、合法卡型枚举与验证配置时，自动提交一条验证任务。
 - 校验用户指定的目标卡是否与本智能体的官方卡型一致。
 - 收集模型、框架、后端、驱动和 SDK 版本，明确缺失信息。
 - 根据多卡、量化、长上下文、自定义算子和动态形状提示验证风险。
@@ -13,9 +16,26 @@
 
 - 卡型名称来自信创模盒“模型 X 算力”页面。
 - 不声明页面未提供的显存、算力、SDK 版本或算子支持结论。
-- 不访问外部网络，不执行命令，不创建或修改适配任务。
+- 只访问信创模盒公开候选接口和官方任务接口；不执行系统命令。
+- 无私密 `MODELHUB_XC_TOKEN` 时只扫描、记录候选，不创建任务。
+- 令牌只从运行环境读取，不写入仓库、HTTP 响应或 stdout。
 - 最终兼容性必须以目标设备上的真实日志和测试结果为准。
 
+## 自动工作模式
+
+- `AUTO_SCAN_ENABLED`：默认 `true`，启动后立即扫描，之后每小时扫描一次。
+- `SCAN_INTERVAL_SECONDS`：扫描间隔，最小 60 秒，默认 3600 秒。
+- `AUTO_SUBMIT_ENABLED`：默认 `true`；缺少任一必要配置时自动降级为只读扫描。
+- `MODELHUB_XC_TOKEN`：调用任务查询/创建接口的私密 `Xc-Token`，不得写进公开代码。
+- `MODELHUB_TARGET_GPU`：平台内部卡型枚举。只有已由官方任务记录核验的枚举才可配置。
+- `MODELHUB_CONFIG_PARAMS`：验证任务 YAML；未内置官方配置的卡型必须通过私密部署配置提供。
+- 每次扫描最多提交一个候选；提交前用 `modelId + gpuType` 精确查询历史，已有任务则跳过。
+
+## 日志
+
+- stdout 使用单行 JSON，记录服务启动、扫描、候选、去重、提交结果、健康检查、分析结论和优雅停机。
+- 仅记录经过清洗的模型标识、结论和必要状态，不输出整个请求体、凭据、URL 查询参数或环境变量值。
+
 ## 平台约束
 
 - 仓库根目录包含 `Dockerfile`，容器开放 `8080`。
@@ -28,6 +48,7 @@
 
 - `GET /health`：存活检查。
 - `GET /`：卡型和智能体元信息。
+- `GET /scanner`：后台扫描器最近状态，不包含令牌或策略 ID。
 - `POST /analyze`：部署前预检。
 - `POST /task`：与 `/analyze` 相同的兼容入口。
 

main.py

@@ -1,6 +1,7 @@
 """Card-specific ModelHub XC deployment preflight agent.
 
-This service is read-only and only makes claims supported by submitted facts.
+It scans public candidates and only writes a deduplicated task when private runtime
+credentials and verified platform configuration are present.
 """
 
 from __future__ import annotations
@@ -10,23 +11,279 @@ import os
 import re
 import signal
 import threading
+from datetime import datetime, timezone
 from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
 from typing import Any
+from urllib.parse import quote, urlencode, urlsplit
+from urllib.request import ProxyHandler, Request, build_opener
 
 
 AGENT_NAME = "xc-tianga100-advisor-agent"
-AGENT_VERSION = "1.0.0"
+AGENT_VERSION = "1.2.0"
 CARD_VENDOR = "天数智芯"
 CARD_MODEL = "天垓100"
+DEFAULT_TARGET_GPU = "Iluvatar_bi-100"
 PORT = int(os.getenv("PORT", "8080"))
 STRATEGY_ID = os.getenv("STRATEGY_ID", "")
+XC_TOKEN = os.getenv("MODELHUB_XC_TOKEN", "")
+TARGET_GPU = os.getenv("MODELHUB_TARGET_GPU", DEFAULT_TARGET_GPU)
+AUTO_SCAN_ENABLED = os.getenv("AUTO_SCAN_ENABLED", "true").lower() not in {
+    "0", "false", "no", "off"
+}
+AUTO_SUBMIT_ENABLED = os.getenv("AUTO_SUBMIT_ENABLED", "true").lower() not in {
+    "0", "false", "no", "off"
+}
+SCAN_INTERVAL_SECONDS = max(int(os.getenv("SCAN_INTERVAL_SECONDS", "3600")), 60)
+CANDIDATE_ENDPOINT = "https://modelhub.org.cn/api/computility/models/top/models"
+TASK_PAGE_ENDPOINT = "https://modelhub.org.cn/api/adapt/task/page"
+TASK_ADD_ENDPOINT = "https://modelhub.org.cn/api/adapt/task/add"
+DEFAULT_CONFIG_PARAMS = "framework: vllm\napi: completion\nlang: zh\nmax_model_len: 2048\nmax_tokens: 256\ntemperature: 0.1\nrepetition_penalty: 1.0\ntop_p: 0.9\nsut_config:\n  gpu_num: 1\n  values:\n    command:\n    - python3\n    - -m\n    - vllm.entrypoints.openai.api_server\n    - --host\n    - 0.0.0.0\n    - --port\n    - '20644'\n    - --served-model-name\n    - llm\n    - --model\n    - /model\n    - --max-model-len\n    - '2048'\n    - --tensor-parallel-size\n    - '1'\n    - --max-num-seqs\n    - '8'\n    - --enforce-eager\n    - --disable-log-requests\n    - --enable-prefix-caching\n    - --trust-remote-code\nref_config:\n  gpu_num: 1\n  values:\n    command:\n    - vllm\n    - serve\n    - /model\n    - --port\n    - '80'\n    - --served-model-name\n    - llm\n    - --max-model-len\n    - '2048'\n    - -tp\n    - '1'\n    - --enforce-eager\n    - --trust-remote-code\n"
+CONFIG_PARAMS = os.getenv("MODELHUB_CONFIG_PARAMS", DEFAULT_CONFIG_PARAMS)
 MAX_BODY_BYTES = 1_000_000
+SECRET_ASSIGNMENT_RE = re.compile(
+    r"(?i)(token|secret|password|api[_-]?key)=([^&\s]+)"
+)
+HTTP_OPENER = build_opener(ProxyHandler({}))
+SCANNER_LOCK = threading.Lock()
+SCANNER_STATE: dict[str, Any] = {
+    "enabled": AUTO_SCAN_ENABLED,
+    "running": False,
+    "last_scan_at": None,
+    "last_candidate": None,
+    "last_action": "not_started",
+    "last_error_type": None,
+}
 
 
 class ValidationError(ValueError):
     """Raised when a request field is invalid."""
 
 
+def _safe_model_name(value: Any) -> str:
+    """Return a bounded model identifier without URL credentials or query data."""
+
+    text = str(value or "未指定模型").strip()
+    if "://" in text:
+        try:
+            parsed = urlsplit(text)
+            text = f"{parsed.scheme}://{parsed.hostname or ''}{parsed.path}"
+        except ValueError:
+            text = "无效模型地址"
+    text = SECRET_ASSIGNMENT_RE.sub(r"\1=[REDACTED]", text)
+    return text[:200] or "未指定模型"
+
+
+def _log_event(event: str, **fields: Any) -> None:
+    """Write one structured, unbuffered log record to stdout."""
+
+    record = {
+        "timestamp": datetime.now(timezone.utc).isoformat(),
+        "event": event,
+        "agent": AGENT_NAME,
+        "version": AGENT_VERSION,
+        **fields,
+    }
+    print(json.dumps(record, ensure_ascii=False, separators=(",", ":")), flush=True)
+
+
+def _scanner_state_update(**fields: Any) -> None:
+    with SCANNER_LOCK:
+        SCANNER_STATE.update(fields)
+
+
+def scanner_snapshot() -> dict[str, Any]:
+    with SCANNER_LOCK:
+        return dict(SCANNER_STATE)
+
+
+def _http_json(
+    method: str,
+    url: str,
+    *,
+    payload: dict[str, Any] | None = None,
+    token: str = "",
+) -> dict[str, Any]:
+    """Call one ModelHub JSON endpoint without logging headers or response bodies."""
+
+    body = None
+    headers = {"Accept": "application/json"}
+    if payload is not None:
+        body = json.dumps(payload, ensure_ascii=False).encode("utf-8")
+        headers["Content-Type"] = "application/json"
+    if token:
+        headers["Xc-Token"] = token
+    request = Request(url, data=body, headers=headers, method=method)
+    with HTTP_OPENER.open(request, timeout=15) as response:
+        result = json.loads(response.read().decode("utf-8"))
+    if not isinstance(result, dict):
+        raise RuntimeError("unexpected_response_shape")
+    return result
+
+
+def _candidate_records(payload: dict[str, Any]) -> list[dict[str, str]]:
+    """Extract text-generation candidates from the public ModelHub response."""
+
+    data = payload.get("data")
+    if not isinstance(data, list):
+        return []
+    candidates: list[dict[str, str]] = []
+    for item in data:
+        if not isinstance(item, dict):
+            continue
+        model_id = str(item.get("modelId") or "").strip()
+        info = item.get("taskLevelsInfo")
+        info = info if isinstance(info, dict) else {}
+        raw_type = str(
+            info.get("taskLevelName")
+            or info.get("taskLevelCode")
+            or info.get("taskLevelEnglishName")
+            or ""
+        ).lower()
+        chinese_type = str(info.get("taskLevelChineseName") or "")
+        if not model_id or not (
+            "text-generation" in raw_type
+            or "text_generation" in raw_type
+            or chinese_type.strip() == "文本生成"
+        ):
+            continue
+        candidates.append({"model_id": model_id, "task_type": "text-generation"})
+    return candidates
+
+
+def _model_size_score(model_id: str) -> float:
+    match = re.search(r"(?i)(\d+(?:\.\d+)?)\s*([bm])(?:\b|[_-])", model_id)
+    if not match:
+        return 1_000_000.0
+    size = float(match.group(1))
+    return size * (1_000 if match.group(2).lower() == "b" else 1)
+
+
+def select_candidate(payload: dict[str, Any]) -> dict[str, str] | None:
+    """Prefer a small official-EngineX text model from the public hot list."""
+
+    candidates = _candidate_records(payload)
+    if not candidates:
+        return None
+
+    def score(item: dict[str, str]) -> tuple[int, float, str]:
+        model_id = item["model_id"].lower()
+        supported_family = any(
+            name in model_id for name in ("qwen3", "llama3", "deepseek-r1-distill")
+        )
+        return (0 if supported_family else 1, _model_size_score(model_id), model_id)
+
+    return min(candidates, key=score)
+
+
+def _model_address(model_id: str) -> str:
+    safe_path = "/".join(quote(part, safe="") for part in model_id.split("/"))
+    return f"https://www.modelscope.cn/models/{safe_path}"
+
+
+def scan_once(request_json: Any = None) -> dict[str, Any]:
+    """Discover one candidate, deduplicate it, and optionally submit one task."""
+
+    call = request_json or _http_json
+    scan_at = datetime.now(timezone.utc).isoformat()
+    _scanner_state_update(running=True, last_scan_at=scan_at, last_error_type=None)
+    _log_event("candidate_scan_started", source="modelhub_hot_models")
+    candidate_payload = call("GET", CANDIDATE_ENDPOINT)
+    candidate = select_candidate(candidate_payload)
+    if candidate is None:
+        result = {"action": "no_candidate"}
+        _scanner_state_update(running=False, last_candidate=None, last_action=result["action"])
+        _log_event("candidate_scan_completed", candidate_count=0, action=result["action"])
+        return result
+
+    model_id = _safe_model_name(candidate["model_id"])
+    _scanner_state_update(last_candidate=model_id)
+    _log_event(
+        "candidate_discovered",
+        model=model_id,
+        task_type=candidate["task_type"],
+        target_gpu_configured=bool(TARGET_GPU),
+    )
+
+    missing = []
+    if not AUTO_SUBMIT_ENABLED:
+        missing.append("auto_submit_disabled")
+    if not XC_TOKEN:
+        missing.append("xc_token_missing")
+    if not STRATEGY_ID:
+        missing.append("strategy_id_missing")
+    if not TARGET_GPU:
+        missing.append("target_gpu_missing")
+    if not CONFIG_PARAMS:
+        missing.append("task_config_missing")
+    if missing:
+        result = {"action": "submission_skipped", "reasons": missing, "model": model_id}
+        _scanner_state_update(running=False, last_action=result["action"])
+        _log_event("task_submission_skipped", model=model_id, reasons=missing)
+        return result
+
+    query = urlencode(
+        {
+            "current": 1,
+            "pageSize": 1,
+            "onlyMine": "true",
+            "modelId": candidate["model_id"],
+            "gpuType": TARGET_GPU,
+        }
+    )
+    history = call("GET", f"{TASK_PAGE_ENDPOINT}?{query}", token=XC_TOKEN)
+    history_data = history.get("data") if isinstance(history, dict) else None
+    records = history_data.get("records") if isinstance(history_data, dict) else []
+    if records:
+        result = {"action": "duplicate_skipped", "model": model_id}
+        _scanner_state_update(running=False, last_action=result["action"])
+        _log_event("task_duplicate_skipped", model=model_id, target_gpu=TARGET_GPU)
+        return result
+
+    task_payload = {
+        "modelAddress": _model_address(candidate["model_id"]),
+        "taskType": candidate["task_type"],
+        "targetGpu": TARGET_GPU,
+        "framework": "vllm",
+        "strategyId": STRATEGY_ID,
+        "configParams": CONFIG_PARAMS,
+    }
+    response = call("POST", TASK_ADD_ENDPOINT, payload=task_payload, token=XC_TOKEN)
+    response_data = response.get("data") if isinstance(response, dict) else None
+    task_id = response_data.get("taskId") if isinstance(response_data, dict) else response_data
+    result = {"action": "submitted", "model": model_id, "task_id": task_id}
+    _scanner_state_update(running=False, last_action=result["action"])
+    _log_event(
+        "task_submitted",
+        model=model_id,
+        target_gpu=TARGET_GPU,
+        task_id=task_id,
+    )
+    return result
+
+
+def _scanner_loop() -> None:
+    _log_event(
+        "scanner_started",
+        interval_seconds=SCAN_INTERVAL_SECONDS,
+        auto_submit=AUTO_SUBMIT_ENABLED,
+        token_present=bool(XC_TOKEN),
+        target_gpu_configured=bool(TARGET_GPU),
+    )
+    while not STOP.is_set():
+        try:
+            scan_once()
+        except Exception as exc:
+            error_type = type(exc).__name__
+            _scanner_state_update(
+                running=False,
+                last_action="scan_error",
+                last_error_type=error_type,
+            )
+            _log_event("scanner_error", error_type=error_type)
+        if STOP.wait(SCAN_INTERVAL_SECONDS):
+            break
+
+
 def _first(payload: dict[str, Any], *names: str) -> Any:
     for name in names:
         if name in payload and payload[name] not in (None, ""):
@@ -160,7 +417,7 @@ def analyze(payload: dict[str, Any]) -> dict[str, Any]:
 
 
 class Handler(BaseHTTPRequestHandler):
-    server_version = "ModelHubCardAdvisor/1.0"
+    server_version = "ModelHubCardAdvisor/1.2"
 
     def _json(self, payload: dict[str, Any], status: int = 200) -> None:
         body = json.dumps(payload, ensure_ascii=False).encode("utf-8")
@@ -172,7 +429,17 @@ class Handler(BaseHTTPRequestHandler):
 
     def do_GET(self) -> None:  # noqa: N802 - BaseHTTPRequestHandler contract
         if self.path == "/health":
-            self._json({"status": "ok", "agent": AGENT_NAME, "version": AGENT_VERSION})
+            self._json(
+                {
+                    "status": "ok",
+                    "agent": AGENT_NAME,
+                    "version": AGENT_VERSION,
+                    "scanner": scanner_snapshot(),
+                }
+            )
+            return
+        if self.path == "/scanner":
+            self._json(scanner_snapshot())
             return
         if self.path == "/":
             self._json(
@@ -182,8 +449,20 @@ class Handler(BaseHTTPRequestHandler):
                     "description": f"{CARD_VENDOR} {CARD_MODEL} 专属部署前预检",
                     "official_target": {"vendor": CARD_VENDOR, "model": CARD_MODEL},
                     "strategy_id_present": bool(STRATEGY_ID),
-                    "endpoints": ["GET /health", "POST /analyze", "POST /task"],
-                    "external_writes": False,
+                    "target_gpu": TARGET_GPU or None,
+                    "endpoints": [
+                        "GET /health",
+                        "GET /scanner",
+                        "POST /analyze",
+                        "POST /task",
+                    ],
+                    "external_writes_enabled": bool(
+                        AUTO_SUBMIT_ENABLED
+                        and XC_TOKEN
+                        and STRATEGY_ID
+                        and TARGET_GPU
+                        and CONFIG_PARAMS
+                    ),
                 }
             )
             return
@@ -198,24 +477,52 @@ class Handler(BaseHTTPRequestHandler):
             if length <= 0:
                 raise ValidationError("请求正文不能为空")
             if length > MAX_BODY_BYTES:
+                _log_event("request_rejected", path=self.path, reason="payload_too_large")
                 self._json({"error": "payload_too_large"}, 413)
                 return
             payload = json.loads(self.rfile.read(length))
-            self._json(analyze(payload))
+            model_name = _safe_model_name(
+                _first(payload, "model_name", "model", "model_address")
+                if isinstance(payload, dict)
+                else None
+            )
+            _log_event("analysis_started", path=self.path, model=model_name)
+            result = analyze(payload)
+            _log_event(
+                "analysis_completed",
+                path=self.path,
+                model=model_name,
+                verdict=result["verdict"],
+                target_matches=result["target_matches"],
+            )
+            self._json(result)
         except (json.JSONDecodeError, UnicodeDecodeError):
+            _log_event("request_rejected", path=self.path, reason="invalid_json")
             self._json({"error": "invalid_json", "message": "请求正文必须是有效 JSON"}, 400)
         except ValidationError as exc:
+            _log_event(
+                "request_rejected",
+                path=self.path,
+                reason="validation_error",
+                message=str(exc),
+            )
             self._json({"error": "validation_error", "message": str(exc)}, 422)
 
-    def log_message(self, _format: str, *_args: Any) -> None:
-        return
+    def log_message(self, _format: str, *args: Any) -> None:
+        status = str(args[1]) if len(args) > 1 else "unknown"
+        _log_event(
+            "http_access",
+            method=self.command,
+            path=urlsplit(self.path).path,
+            status=status,
+        )
 
 
 STOP = threading.Event()
 
 
 def _handle_signal(signum: int, _frame: Any) -> None:
-    print(f"received signal {signum}; shutting down", flush=True)
+    _log_event("shutdown_requested", signal=signum)
     STOP.set()
 
 
@@ -224,10 +531,29 @@ def main() -> None:
     signal.signal(signal.SIGINT, _handle_signal)
     server = ThreadingHTTPServer(("0.0.0.0", PORT), Handler)
     server.timeout = 1
-    print(f"{AGENT_NAME} {AGENT_VERSION} listening on 0.0.0.0:{PORT}", flush=True)
+    _log_event(
+        "service_started",
+        host="0.0.0.0",
+        port=PORT,
+        target_vendor=CARD_VENDOR,
+        target_model=CARD_MODEL,
+    )
+    scanner_thread = None
+    if AUTO_SCAN_ENABLED:
+        scanner_thread = threading.Thread(
+            target=_scanner_loop,
+            name="modelhub-candidate-scanner",
+            daemon=True,
+        )
+        scanner_thread.start()
+    else:
+        _scanner_state_update(last_action="disabled")
+        _log_event("scanner_disabled", reason="auto_scan_disabled")
     while not STOP.is_set():
         server.handle_request()
     server.server_close()
+    if scanner_thread is not None:
+        scanner_thread.join(timeout=5)
 
 
 if __name__ == "__main__":

test_main.py

@@ -1,6 +1,22 @@
+import io
+import json
 import unittest
+from contextlib import redirect_stdout
+from unittest.mock import patch
 
-from main import CARD_MODEL, CARD_VENDOR, ValidationError, analyze
+import main as agent_main
+
+from main import (
+    AGENT_NAME,
+    CARD_MODEL,
+    CARD_VENDOR,
+    ValidationError,
+    _log_event,
+    _safe_model_name,
+    analyze,
+    scan_once,
+    select_candidate,
+)
 
 
 class AnalyzeTests(unittest.TestCase):
@@ -45,6 +61,103 @@ class AnalyzeTests(unittest.TestCase):
         with self.assertRaises(ValidationError):
             analyze({"cards": 0})
 
+    def test_model_log_identifier_removes_url_credentials(self):
+        value = "https://user:pass@example.com/org/model?token=secret#fragment"
+        self.assertEqual(_safe_model_name(value), "https://example.com/org/model")
+
+    def test_structured_log_is_valid_json(self):
+        stream = io.StringIO()
+        with redirect_stdout(stream):
+            _log_event("analysis_completed", model="example/model", verdict="ready")
+        record = json.loads(stream.getvalue())
+        self.assertEqual(record["agent"], AGENT_NAME)
+        self.assertEqual(record["event"], "analysis_completed")
+        self.assertEqual(record["model"], "example/model")
+
+    @staticmethod
+    def candidate_payload():
+        return {
+            "code": 0,
+            "data": [
+                {
+                    "modelId": "Qwen/Qwen3-30B-Instruct",
+                    "taskLevelsInfo": {"taskLevelChineseName": "文本生成"},
+                },
+                {
+                    "modelId": "Qwen/Qwen3-4B-Instruct-2507",
+                    "taskLevelsInfo": {"taskLevelChineseName": "文本生成"},
+                },
+                {
+                    "modelId": "example/image-model",
+                    "taskLevelsInfo": {"taskLevelChineseName": "文本生成图片"},
+                },
+            ],
+        }
+
+    def test_candidate_selection_prefers_smaller_supported_text_model(self):
+        candidate = select_candidate(self.candidate_payload())
+        self.assertIsNotNone(candidate)
+        self.assertEqual(candidate["model_id"], "Qwen/Qwen3-4B-Instruct-2507")
+
+    def test_scan_skips_write_without_private_token(self):
+        calls = []
+
+        def fake_request(method, url, **kwargs):
+            calls.append((method, url, kwargs))
+            return self.candidate_payload()
+
+        with patch.object(agent_main, "XC_TOKEN", ""):
+            result = scan_once(fake_request)
+        self.assertEqual(result["action"], "submission_skipped")
+        self.assertIn("xc_token_missing", result["reasons"])
+        self.assertEqual([call[0] for call in calls], ["GET"])
+
+    def test_scan_deduplicates_before_submission(self):
+        calls = []
+
+        def fake_request(method, url, **kwargs):
+            calls.append((method, url, kwargs))
+            if "top/models" in url:
+                return self.candidate_payload()
+            return {"code": 0, "data": {"records": [{"taskId": "existing"}]}}
+
+        with (
+            patch.object(agent_main, "XC_TOKEN", "private-token"),
+            patch.object(agent_main, "STRATEGY_ID", "strategy-id"),
+            patch.object(agent_main, "TARGET_GPU", "verified-gpu"),
+            patch.object(agent_main, "CONFIG_PARAMS", "framework: vllm"),
+            patch.object(agent_main, "AUTO_SUBMIT_ENABLED", True),
+        ):
+            result = scan_once(fake_request)
+        self.assertEqual(result["action"], "duplicate_skipped")
+        self.assertEqual([call[0] for call in calls], ["GET", "GET"])
+
+    def test_scan_submits_exactly_one_task_after_empty_history(self):
+        calls = []
+
+        def fake_request(method, url, **kwargs):
+            calls.append((method, url, kwargs))
+            if "top/models" in url:
+                return self.candidate_payload()
+            if method == "GET":
+                return {"code": 0, "data": {"records": []}}
+            return {"code": 0, "data": {"taskId": "new-task"}}
+
+        with (
+            patch.object(agent_main, "XC_TOKEN", "private-token"),
+            patch.object(agent_main, "STRATEGY_ID", "strategy-id"),
+            patch.object(agent_main, "TARGET_GPU", "verified-gpu"),
+            patch.object(agent_main, "CONFIG_PARAMS", "framework: vllm"),
+            patch.object(agent_main, "AUTO_SUBMIT_ENABLED", True),
+        ):
+            result = scan_once(fake_request)
+        self.assertEqual(result["action"], "submitted")
+        self.assertEqual(result["task_id"], "new-task")
+        self.assertEqual([call[0] for call in calls], ["GET", "GET", "POST"])
+        submitted = calls[-1][2]["payload"]
+        self.assertEqual(submitted["targetGpu"], "verified-gpu")
+        self.assertEqual(submitted["strategyId"], "strategy-id")
+
 
 if __name__ == "__main__":
     unittest.main()