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4 Commits
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3eed33f0d8 | ||
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a2ef82d954 | ||
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2198aed3c2 | ||
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965342bafe |
79
main.py
79
main.py
@@ -47,7 +47,7 @@ SUPPORTED_SPECIAL_ARCHS = ['Eagle3Speculator', 'LlamaForCausalLMEagle3']
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MODELHUB_API = "https://modelhub.org.cn/api"
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# 搜索关键词
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SEARCH_KEYWORDS = ['qwen', 'Qwen2', 'Qwen3', 'Qwen3.5', 'Llama-3', 'Llama-3.1', 'Mistral', 'DeepSeek']
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SEARCH_KEYWORDS = ['Llama-3', 'Llama-3.1', 'Llama-3.2', 'Meta-Llama', 'Llama-4']
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# ============================================================
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# 全局状态
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@@ -157,7 +157,7 @@ def search_models(keyword: str) -> list:
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def check_architecture(model_id: str) -> tuple:
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"""检查模型架构"""
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"""检查模型是否有有效的 config.json(不限架构类型)"""
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try:
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cfg_url = f"{MODELSCOPE_API}/models/{model_id}/repo?Revision=master&FilePath=config.json"
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resp = requests.get(cfg_url, timeout=10)
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@@ -165,20 +165,13 @@ def check_architecture(model_id: str) -> tuple:
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cfg = resp.json()
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archs = cfg.get('architectures', [])
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mtype = cfg.get('model_type', '')
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arch_str = str(archs)
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for special in SUPPORTED_SPECIAL_ARCHS:
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if special in arch_str:
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return True, special
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for kw in SUPPORTED_ARCH_KEYWORDS:
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if kw in arch_str:
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return True, kw
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if mtype in SUPPORTED_MODEL_TYPES:
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return True, mtype
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return False, f"arch={archs} type={mtype}"
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if archs or mtype:
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return True, f"arch={archs} type={mtype}"
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return False, "empty config"
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mid_upper = model_id.upper()
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if 'QWEN3' in mid_upper or 'QWEN2' in mid_upper:
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return True, "GGUF"
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return False, "no config, not Qwen"
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if 'LLAMA' in mid_upper:
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return True, "Llama(GGUF)"
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return False, "no config"
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except Exception as e:
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return True, f"check error: {e}"
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@@ -254,26 +247,25 @@ def check_queue_available() -> int:
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def build_config_params() -> str:
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"""构建 YAML 配置 - llama.cpp (支持 GGUF)"""
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"""构建 YAML 配置 - vllm"""
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params = {
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'framework': 'llama.cpp',
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'nv_framework': 'llama.cpp',
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'framework': 'vllm',
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'nv_framework': 'vllm',
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'api': 'completion',
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'max_tokens': 1024,
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'temperature': 0.7,
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'repetition_penalty': 1.2,
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'top_p': 0.9,
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'lang': 'zh',
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'max_model_len': 4096,
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'max_model_len': 2048,
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'sut_config': {
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'gpu_num': 1,
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'values': {
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'command': [
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'llama-server', '--model', '/model', '--alias', 'llm',
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'--threads', '20', '--n-gpu-layers', '999', '--prio', '3',
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'--min_p', '0.01', '--ctx-size', '4096',
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'--host', '0.0.0.0', '--port', '8000',
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'--jinja', '--flash-attn', 'off',
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'vllm', 'serve', '/model', '--port', '8000',
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'--served-model-name', 'llm', '--max-model-len', '2048',
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'--dtype', 'auto', '--gpu-memory-utilization', '0.95',
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'-tp', '1', '--enforce-eager', '--trust-remote-code',
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]
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}
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},
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@@ -281,9 +273,9 @@ def build_config_params() -> str:
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'gpu_num': 1,
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'values': {
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'command': [
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'llama-server', '--model', '/model', '--alias', 'llm',
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'--threads', '20', '--n-gpu-layers', '999',
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'--ctx-size', '4096', '--host', '0.0.0.0', '--port', '8000',
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'vllm', 'serve', '/model', '--port', '80',
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'--served-model-name', 'llm', '--max-model-len', '4096',
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'--enforce-eager', '--trust-remote-code', '-tp', '1',
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]
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}
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},
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@@ -303,7 +295,7 @@ def submit_model(model_url: str) -> tuple:
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'modelAddress': normalize_model_url(model_url),
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'taskType': 'text-generation',
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'targetGpu': TARGET_GPU,
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'framework': 'llama.cpp',
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'framework': 'vllm',
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'strategyId': STRATEGY_ID,
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'configParams': build_config_params(),
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}
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@@ -322,7 +314,7 @@ def submit_model(model_url: str) -> tuple:
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# 主流程
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# ============================================================
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def run_pipeline(submit_limit: int = 2):
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def run_pipeline(submit_limit: int = 5):
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"""完整流程:搜索→筛选→提交(只针对目标GPU)"""
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init_db()
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log("=" * 50)
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@@ -366,14 +358,22 @@ def run_pipeline(submit_limit: int = 2):
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hf_models.append(m)
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log(f"格式筛选: {len(hf_models)} 通过, {format_skipped} 跳过 (GPTQ/AWQ)")
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# 3. 架构筛选(参考检查,不做严格过滤,让平台决定兼容性)
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# 3. 架构筛选(只保留有标准config.json的模型,排除无效格式)
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log("\n--- 阶段3: 架构检查 ---")
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arch_passed = []
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arch_rejected = 0
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for m in hf_models:
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ok, reason = check_architecture(m['model_id'])
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if not ok:
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log(f" ! {m['model_id']}: {reason} (仍保留)")
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arch_rejected += 1
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log(f" x {m['model_id']}: {reason}")
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elif reason == 'GGUF':
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arch_rejected += 1
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log(f" x {m['model_id']}: GGUF(无config)")
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else:
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arch_passed.append(m)
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time.sleep(0.1)
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log(f"架构检查: {len(hf_models)} 个模型进入下一阶段")
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log(f"架构检查: {len(arch_passed)} 通过, {arch_rejected} 拒绝")
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# 4. 筛选并提交(只针对目标GPU)
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log(f"\n--- 阶段4: 筛选并提交 [{TARGET_GPU}] ---")
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@@ -387,7 +387,7 @@ def run_pipeline(submit_limit: int = 2):
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# 筛选
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to_submit = []
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for m in hf_models:
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for m in arch_passed:
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model_id = m['model_id']
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# 检查全平台验证状态
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@@ -418,8 +418,9 @@ def run_pipeline(submit_limit: int = 2):
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submitted += 1
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log(f" ✅ {m['model_id']}")
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db_conn.execute(
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'INSERT OR REPLACE INTO submitted VALUES (?,?,?,?)',
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(m['model_id'], TARGET_GPU, str(task_id), datetime.now().isoformat())
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'INSERT OR REPLACE INTO submitted VALUES (?,?,?,?,?,?)',
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(m['model_id'], TARGET_GPU, str(task_id), 'submitted',
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datetime.now().isoformat(), None)
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)
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else:
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log(f" ❌ {m['model_id']}: {msg}")
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@@ -486,7 +487,7 @@ class AgentHandler(BaseHTTPRequestHandler):
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if content_len > 0:
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body = json.loads(self.rfile.read(content_len))
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limit = body.get('limit', 2)
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limit = body.get('limit', 5)
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self._json({'status': 'started', 'gpu': TARGET_GPU, 'limit': limit})
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@@ -567,7 +568,7 @@ class AgentHandler(BaseHTTPRequestHandler):
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'modelAddress': 'https://www.modelscope.cn/models/Qwen/Qwen3-8B',
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'taskType': 'text-generation',
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'targetGpu': TARGET_GPU,
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'framework': 'llama.cpp',
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'framework': 'vllm',
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'strategyId': STRATEGY_ID,
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'configParams': build_config_params(),
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},
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@@ -685,7 +686,7 @@ def main():
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json={
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'modelAddress': 'https://www.modelscope.cn/models/Qwen/Qwen3-8B',
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'taskType': 'text-generation', 'targetGpu': TARGET_GPU,
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'framework': 'llama.cpp', 'strategyId': STRATEGY_ID,
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'framework': 'vllm', 'strategyId': STRATEGY_ID,
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'configParams': build_config_params(),
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}, timeout=10
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)
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@@ -703,7 +704,7 @@ def main():
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try:
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state['running'] = True
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state['last_run'] = datetime.now().isoformat()
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count = run_pipeline(submit_limit=2)
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count = run_pipeline(submit_limit=5)
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state['last_result'] = {'submitted': count, 'success': True}
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except Exception as e:
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log(f"流程异常: {traceback.format_exc()}")
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