feat: 新增 compute_gateway、compute_poller、agent 模块，重构前端 dist

- 新增 backend/app/modules/compute_gateway（client/sync）计算网关模块
- 新增 backend/app/workers/compute_poller 计算轮询 worker
- 新增 compute/agent/process_manager 进程管理器
- 新增 scripts/ 脚本目录
- 更新 Docker 部署配置（app/compute/nginx）
- 更新后端平台 API、数据库 SQL、core 配置
- 更新前端多个视图组件及 API 模块
- 重构 frontend/dist 构建产物（新 hash）
- 更新多项文档

Co-Authored-By: Claude <noreply@anthropic.com>

compute/engines/llama_factory/adapter.py

@@ -19,22 +19,57 @@ def validate_config(config: dict[str, Any]) -> list[str]:
         errors.append("base_model or model_name_or_path is required")
     if not config.get("dataset") and not config.get("dataset_dir"):
         errors.append("dataset or dataset_dir is required")
-    learning_rate = float(config.get("learning_rate", 0.0002))
+    try:
+        learning_rate = float(config.get("learning_rate", 0.0002))
+    except (TypeError, ValueError):
+        learning_rate = 0
     if learning_rate <= 0:
         errors.append("learning_rate must be greater than zero")
-    epochs = int(config.get("n_epochs", config.get("num_train_epochs", 1)))
+    try:
+        epochs = int(config.get("n_epochs", config.get("num_train_epochs", 1)))
+    except (TypeError, ValueError):
+        epochs = 0
     if epochs <= 0:
         errors.append("n_epochs must be greater than zero")
     return errors
 
 
+def _optional_arg(config: dict[str, Any], command: list[str], option: str, *keys: str) -> None:
+    for key in keys:
+        value = config.get(key)
+        if value is not None and value != "":
+            command.extend([option, str(value)])
+            return
+
+
 def build_command(config: dict[str, Any], llama_factory_home: str = "/app/LLaMA-Factory") -> LlamaFactoryCommand:
     errors = validate_config(config)
     if errors:
         raise ValueError("; ".join(errors))
 
+    engine = str(config.get("engine") or config.get("training_engine") or "llama_factory")
+    if engine == "smoke":
+        output_dir = config.get("output_dir") or f"/data/yg-ft/outputs/{config.get('name', 'training-smoke')}"
+        script = (
+            "import json, os, time; "
+            f"out={str(output_dir)!r}; "
+            "os.makedirs(out, exist_ok=True); "
+            "print('[INFO] smoke training started', flush=True); "
+            "\nfor step in range(1, 7):\n"
+            "    loss=round(1.8/(step+1), 4)\n"
+            "    lr=round(0.0002*(1-step/10), 8)\n"
+            "    print({'loss': loss, 'grad_norm': round(0.4 + step*0.03, 4), 'learning_rate': lr, 'epoch': round(step/6, 4)}, flush=True)\n"
+            "    time.sleep(0.4)\n"
+            "\nopen(os.path.join(out, 'adapter_config.json'), 'w', encoding='utf-8').write(json.dumps({'engine':'smoke','status':'completed'})); "
+            "print('***** train metrics *****', flush=True); "
+            "print('train_loss = 0.12', flush=True); "
+            "print('***** train metrics end *****', flush=True)"
+        )
+        return LlamaFactoryCommand(command=["python", "-u", "-c", script], work_dir="/app", env={})
+
     model_path = config.get("base_model") or config.get("model_name_or_path")
-    dataset = config.get("dataset") or config.get("dataset_dir")
+    dataset = config.get("dataset") or config.get("dataset_name")
+    dataset_dir = config.get("dataset_dir")
     output_dir = config.get("output_dir") or f"/data/yg-ft/outputs/{config.get('name', 'training-job')}"
     command = [
         "llamafactory-cli",
@@ -46,7 +81,7 @@ def build_command(config: dict[str, Any], llama_factory_home: str = "/app/LLaMA-
         "--model_name_or_path",
         str(model_path),
         "--dataset",
-        str(dataset),
+        str(dataset or "default"),
         "--template",
         str(config.get("template", "qwen")),
         "--finetuning_type",
@@ -61,7 +96,22 @@ def build_command(config: dict[str, Any], llama_factory_home: str = "/app/LLaMA-
         str(config.get("n_epochs", 3)),
         "--save_steps",
         str(config.get("save_steps", 50)),
+        "--logging_steps",
+        str(config.get("logging_steps", 10)),
+        "--overwrite_output_dir",
+        "true",
+        "--plot_loss",
+        "true",
     ]
+    if dataset_dir:
+        command.extend(["--dataset_dir", str(dataset_dir)])
+    _optional_arg(config, command, "--cutoff_len", "max_length", "cutoff_len")
+    _optional_arg(config, command, "--lr_scheduler_type", "lr_scheduler_type")
+    _optional_arg(config, command, "--warmup_ratio", "warmup_ratio")
+    _optional_arg(config, command, "--weight_decay", "weight_decay")
+    _optional_arg(config, command, "--lora_rank", "lora_rank", "rank")
+    _optional_arg(config, command, "--lora_alpha", "lora_alpha")
+    _optional_arg(config, command, "--lora_dropout", "lora_dropout")
     quantization_bit = int(config.get("quantization_bit", 0) or 0)
     if quantization_bit in {4, 8}:
         command.extend(["--quantization_bit", str(quantization_bit)])