feat: 推理与评测支持多卡 GPU 选择，训练任务实时 GPU 监控

- 新增 GPU 选择归一化 helper，统一前端各形态的选择（gpu_indices/gpus/gpu_id）
- 评测与推理支持同一节点内多卡选择，校验所选 GPU 空闲后再派发
- 新增 /fine-tune/{id}/gpu-status 接口，训练日志页展示实时 GPU 指标
- 算力节点推理加载支持 CUDA_VISIBLE_DEVICES 多卡可见，nvidia-smi 进程级监控
- GPU 占用跟踪细化为按卡记录，覆盖评测任务、推理模型与对比任务

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

backend/app/api/v1/endpoints/platform.py

@@ -57,6 +57,33 @@ def _select_first_online_node(store: Any) -> dict[str, Any] | None:
     return None
 
 
+def _normalize_gpu_indices(payload: dict[str, Any], *, allow_primary: bool = True) -> list[int]:
+    """Normalize all frontend GPU selection shapes to sorted integer indexes."""
+    raw = payload.get("gpu_indices")
+    if raw is None:
+        raw = payload.get("gpus")
+    if raw is None and allow_primary and payload.get("gpu_id") is not None:
+        raw = [payload.get("gpu_id")]
+    if raw is None or raw == "":
+        return []
+    if isinstance(raw, str):
+        raw = [item.strip() for item in raw.split(",") if item.strip()]
+    if not isinstance(raw, (list, tuple, set)):
+        raw = [raw]
+    result: set[int] = set()
+    for item in raw:
+        if isinstance(item, str) and ":" in item:
+            item = item.rsplit(":", 1)[-1]
+        try:
+            index = int(item)
+        except (TypeError, ValueError) as exc:
+            raise ValueError(f"invalid GPU index: {item}") from exc
+        if index < 0:
+            raise ValueError("GPU index must be non-negative")
+        result.add(index)
+    return sorted(result)
+
+
 async def _wait_for_object_storage() -> None:
     """Wait for MinIO before starting a resource task."""
     settings = get_settings()
@@ -1527,10 +1554,10 @@ async def start_fine_tune(
         if node_id and gpu_indices:
             if not store.check_gpu_access(current_user["id"], node_id, gpu_indices):
                 raise fail(403, "无权使用所选 GPU，请联系管理员分配")
-    # 记录创建者
         if node_id and not gpu_indices:
             payload["allowed_gpu_indices"] = store.assigned_gpu_indexes(current_user["id"], node_id)
-        payload["strict_node_selection"] = bool(node_id)
+    # 页面明确选择节点时，调度器必须保持节点约束；否则可能落到其它节点。
+    payload["strict_node_selection"] = bool(payload.get("compute_node_id") or payload.get("node_id"))
     payload.setdefault("created_by", current_user.get("id"))
     try:
         return ok(await _submit_fine_tune_task(store, payload))
@@ -1661,6 +1688,42 @@ async def fine_tune_diagnostics(task_id: str) -> dict[str, Any]:
     )
 
 
+@router.get("/fine-tune/{task_id}/gpu-status")
+async def fine_tune_gpu_status(task_id: str, current_user: dict[str, Any] = Depends(get_current_user)) -> dict[str, Any]:
+    """Return live GPU metrics for the task's selected node and cards."""
+    store = get_platform_store()
+    try:
+        task = store.task(task_id)
+    except KeyError:
+        raise fail(404, "fine tune task not found")
+    if not has_resource_access("fine-tune", task_id, current_user, "read"):
+        raise fail(403, "no permission to access this task")
+    node = _node_for_task(task)
+    selected = set(_normalize_gpu_indices({"gpus": task.get("gpus") or []}, allow_primary=False))
+    if not node:
+        return ok({"source": "unavailable", "items": [], "selected_gpus": sorted(selected)})
+    try:
+        if get_settings().compute_mode == "simulator":
+            live_items = store.gpus()
+        else:
+            live_items = await ComputeNodeClient(node["api_base_url"]).gpu_resources()
+        items = []
+        for item in live_items:
+            index = int(item.get("gpu_index", item.get("id", -1)))
+            if selected and index not in selected:
+                continue
+            items.append({
+                **item,
+                "id": index,
+                "node_id": node["id"],
+                "node_code": node.get("code"),
+                "node_name": node.get("name"),
+            })
+        return ok({"source": "compute", "items": items, "selected_gpus": sorted(selected)})
+    except Exception as exc:  # noqa: BLE001 - let UI retain last good snapshot
+        return ok({"source": "unavailable", "items": [], "selected_gpus": sorted(selected), "error": str(exc)})
+
+
 @router.put("/fine-tune/{task_id}")
 async def update_fine_tune(task_id: str, payload: dict[str, Any] = Body(...), current_user: dict = Depends(get_current_user)) -> dict[str, Any]:
     if not has_resource_access("fine-tune", task_id, current_user, "write"):
@@ -1808,6 +1871,12 @@ async def model_eval_detail(task_id: str, current_user: dict = Depends(get_curre
 async def model_eval_start(payload: dict[str, Any] = Body(...), current_user: dict = Depends(get_current_user)) -> dict[str, Any]:
     """Start an evaluation task: submit eval job to compute node."""
     store = get_platform_store()
+    try:
+        gpu_indices = _normalize_gpu_indices(payload)
+    except ValueError as exc:
+        raise fail(400, str(exc))
+    if not gpu_indices:
+        raise fail(400, "请选择至少一张 GPU")
     # 1. Create eval task record
     payload.setdefault("created_by", current_user.get("id"))
     task = store.create_eval_task({**payload, "status": "pending"})
@@ -1910,6 +1979,17 @@ async def model_eval_start(payload: dict[str, Any] = Body(...), current_user: di
         store.update_eval_task(task["id"], {"status": "failed", "error": message})
         return ok({"task_id": task["id"], "status": "failed", "error": message})
 
+    node_gpus = {
+        int(item.get("id", item.get("gpu_index", -1))): item
+        for item in store.gpus()
+        if item.get("node_id") == node["id"]
+    }
+    unavailable = [index for index in gpu_indices if node_gpus.get(index, {}).get("status") != "idle"]
+    if unavailable:
+        message = f"selected GPU is not idle on compute node {node.get('code')}: {unavailable}"
+        store.update_eval_task(task["id"], {"status": "failed", "error": message})
+        return ok({"task_id": task["id"], "status": "failed", "error": message})
+
     # 6. Build eval job payload
     output_dir = f"/data/yg-ft/outputs/{task['id']}"
     job_payload = {
@@ -1923,7 +2003,9 @@ async def model_eval_start(payload: dict[str, Any] = Body(...), current_user: di
         "output_dir": output_dir,
         "basic_metrics": payload.get("basic_metrics", {}),
         "dimension": dimension_cfg,
-        "gpus": [int(payload.get("gpu_id", 0))],
+        "gpu_id": gpu_indices[0],
+        "gpu_indices": gpu_indices,
+        "gpus": gpu_indices,
         "temperature": payload.get("temperature", 0.1),
         "max_new_tokens": payload.get("max_new_tokens", 512),
         "compute_node_id": node["id"],
@@ -2222,6 +2304,15 @@ async def model_compare_load(task_id: str, current_user: dict = Depends(get_curr
                 "model_name_or_path": model_path,
                 "template": item.get("template", "qwen"),
             }
+            try:
+                item_gpu_indices = _normalize_gpu_indices(item)
+            except ValueError as exc:
+                loaded_models.append({**item, "status": "error", "error": str(exc)})
+                continue
+            if not item_gpu_indices:
+                loaded_models.append({**item, "status": "error", "error": "no GPU selected"})
+                continue
+            load_payload["gpu_indices"] = item_gpu_indices
             if item.get("adapter_path"):
                 load_payload["adapter_name_or_path"] = item["adapter_path"]
             if get_settings().compute_mode == "simulator":
@@ -2230,14 +2321,28 @@ async def model_compare_load(task_id: str, current_user: dict = Depends(get_curr
             # 只派发：HTTP 响应成功即视为已接受（节点会异步加载），loaded 字段忽略
             item_dispatched = False
             errors = []
-            for node in _candidate_online_nodes(store, preferred_node_id):
+            candidate_nodes = _candidate_online_nodes(store, preferred_node_id)
+            if preferred_node_id:
+                candidate_nodes = candidate_nodes[:1]
+            for node in candidate_nodes:
                 try:
+                    node_gpu_map = {
+                        int(gpu.get("id", gpu.get("gpu_index", -1))): gpu
+                        for gpu in store.gpus()
+                        if gpu.get("node_id") == node["id"]
+                    }
+                    unavailable = [
+                        index for index in item_gpu_indices
+                        if node_gpu_map.get(index, {}).get("status") != "idle"
+                    ]
+                    if unavailable:
+                        raise RuntimeError(f"selected GPU is not idle on compute node {node.get('code')}: {unavailable}")
                     if get_settings().minio_enabled:
                         await _wait_for_object_storage()
                     client = ComputeNodeClient(node["api_base_url"])
                     await client.inference_load(load_payload)
-                    store.mark_inference_loaded(node["id"])
+                    store.mark_inference_loaded(node["id"], item_gpu_indices)
-                    loaded_models.append({**item, "status": "starting", "node_id": node["id"], "node_name": node.get("name")})
+                    loaded_models.append({**item, "gpu_indices": item_gpu_indices, "gpus": item_gpu_indices, "status": "starting", "node_id": node["id"], "node_name": node.get("name")})
                     item_dispatched = True
                     break
                 except Exception as exc:  # noqa: BLE001 - try next candidate node
@@ -2340,7 +2445,7 @@ async def model_chat_local_preload(payload: dict[str, Any] = Body(...)) -> dict[
         # 计算节点现在异步加载：HTTP 接受（loading/ready）即视为派发成功
         result = await client.inference_load(payload)
         if result.get("loaded") or result.get("status") in {"loading", "ready"}:
-            store.mark_inference_loaded(node["id"])
+            store.mark_inference_loaded(node["id"], _normalize_gpu_indices(payload))
         return ok(result)
     except Exception as exc:
         return ok({"loaded": False, "error": str(exc)})
@@ -2420,7 +2525,7 @@ async def model_chat_trained_preload(payload: dict[str, Any] = Body(...), curren
         # 计算节点现在异步加载：HTTP 接受（loading/ready）即视为派发成功
         result = await client.inference_load({**payload, "compute_node_id": node["id"]})
         if result.get("loaded") or result.get("status") in {"loading", "ready"}:
-            store.mark_inference_loaded(node["id"])
+            store.mark_inference_loaded(node["id"], _normalize_gpu_indices(payload))
         return ok(result)
     except Exception as exc:
         return ok({"loaded": False, "error": str(exc)})

backend/app/db/platform_store.py

@@ -454,15 +454,21 @@ class PlatformStore:
         self.ensure_seed_data()
         # Track which compute nodes have an active inference model loaded
         self._inference_nodes: set[str] = set()
+        self._inference_gpu_indexes: dict[str, set[int]] = {}
         self._last_runtime_refresh = 0.0
 
     # ── inference node tracking ────────────────────────────────────
 
-    def mark_inference_loaded(self, node_id: str) -> None:
+    def mark_inference_loaded(self, node_id: str, gpu_indexes: list[int] | None = None) -> None:
         self._inference_nodes.add(node_id)
+        if gpu_indexes is not None:
+            self._inference_gpu_indexes[node_id] = {int(item) for item in gpu_indexes}
+        else:
+            self._inference_gpu_indexes.pop(node_id, None)
 
     def mark_inference_unloaded(self, node_id: str) -> None:
         self._inference_nodes.discard(node_id)
+        self._inference_gpu_indexes.pop(node_id, None)
 
     def is_inference_loaded(self, node_id: str) -> bool:
         return node_id in self._inference_nodes
@@ -2766,7 +2772,35 @@ class PlatformStore:
             "SELECT gpu_index FROM gpu_allocations WHERE node_id=? AND status IN ('allocated','running')",
             (node_id,),
         ).fetchall()
-        return {int(row["gpu_index"]) for row in rows}
+        active = {int(row["gpu_index"]) for row in rows}
+        # Evaluation jobs use the same Compute ProcessManager GPU lock but do
+        # not have fine-tune allocation rows; derive their selected cards here
+        # so a training task cannot race onto an evaluation GPU.
+        for row in conn.execute(
+            "SELECT payload FROM eval_tasks WHERE status IN ('syncing','queued','running')"
+        ).fetchall():
+            payload = json_loads(row["payload"], {})
+            if payload.get("compute_node_id") != node_id:
+                continue
+            selected = payload.get("gpu_indices") or payload.get("gpus")
+            if selected is None and payload.get("gpu_id") is not None:
+                selected = [payload.get("gpu_id")]
+            active.update(int(item) for item in selected or [])
+        # Loaded inference models also reserve only their selected cards.
+        active.update(self._inference_gpu_indexes.get(node_id, set()))
+        for row in conn.execute("SELECT payload FROM compare_tasks").fetchall():
+            payload = json_loads(row["payload"], {})
+            load_status = payload.get("load_status") or {}
+            if isinstance(load_status, str):
+                load_status = json_loads(load_status, {})
+            for item in load_status.get("loaded_models") or []:
+                if item.get("node_id") != node_id or item.get("status") not in {"starting", "ready", "running"}:
+                    continue
+                selected = item.get("gpu_indices") or item.get("gpus")
+                if selected is None and item.get("gpu_id") is not None:
+                    selected = [item.get("gpu_id")]
+                active.update(int(gpu) for gpu in selected or [])
+        return active
 
     def _node_gpu_indexes(self, conn: PgConnection, node: dict[str, Any]) -> set[int]:
         rows = conn.execute("SELECT gpu_index FROM gpus WHERE node_id=?", (node["id"],)).fetchall()
@@ -3195,6 +3229,9 @@ class PlatformStore:
             # 推理模型占用算力节点同样计入：优先从 compare_tasks 持久化状态派生
             # （重启后仍准确），并用内存标记兜底（直接 preload 的模型无 compare 记录）
             inference_node_ids = set(self._inference_nodes)
+            inference_gpu_indexes: dict[str, set[int]] = {
+                node_id: set(indexes) for node_id, indexes in self._inference_gpu_indexes.items()
+            }
             for ctr in conn.execute("SELECT payload FROM compare_tasks").fetchall():
                 ls = json_loads(ctr["payload"], {}).get("load_status") or {}
                 if isinstance(ls, str):
@@ -3204,8 +3241,13 @@ class PlatformStore:
                         ls = {}
                 for m in ls.get("loaded_models") or []:
                     if m.get("status") in {"ready", "running"} and m.get("node_id"):
-                        inference_node_ids.add(m["node_id"])
+                        node_id = m["node_id"]
-            for nid in inference_node_ids:
+                        selected = m.get("gpu_indices") or m.get("gpus")
+                        if selected:
+                            inference_gpu_indexes.setdefault(node_id, set()).update(int(item) for item in selected)
+                        else:
+                            inference_node_ids.add(node_id)
+            for nid in set(inference_node_ids) | set(inference_gpu_indexes):
                 running_map[nid] = running_map.get(nid, 0) + 1
             rows = conn.execute("SELECT * FROM compute_nodes ORDER BY scheduler_weight DESC, code").fetchall()
             return [
@@ -3434,6 +3476,9 @@ class PlatformStore:
             # 推理模型占用的节点：优先从 compare_tasks 持久化状态派生（重启后仍准确），
             # 内存标记兜底（直接 preload 的模型无 compare 记录）
             inference_node_ids = set(self._inference_nodes)
+            inference_gpu_indexes: dict[str, set[int]] = {
+                node_id: set(indexes) for node_id, indexes in self._inference_gpu_indexes.items()
+            }
             for ctr in conn.execute("SELECT payload FROM compare_tasks").fetchall():
                 ls = json_loads(ctr["payload"], {}).get("load_status") or {}
                 if isinstance(ls, str):
@@ -3443,7 +3488,12 @@ class PlatformStore:
                         ls = {}
                 for m in ls.get("loaded_models") or []:
                     if m.get("status") in {"ready", "running"} and m.get("node_id"):
-                        inference_node_ids.add(m["node_id"])
+                        node_id = m["node_id"]
+                        selected = m.get("gpu_indices") or m.get("gpus")
+                        if selected:
+                            inference_gpu_indexes.setdefault(node_id, set()).update(int(item) for item in selected)
+                        else:
+                            inference_node_ids.add(node_id)
             items = []
             for row in rows:
                 task = next(
@@ -3459,7 +3509,10 @@ class PlatformStore:
                         t
                         for t in eval_running
                         if t.get("compute_node_id") == row["node_id"]
-                        and row["gpu_index"] == (int(t["gpu_id"]) if t.get("gpu_id") is not None else -1)
+                        and row["gpu_index"] in {
+                            int(item)
+                            for item in (t.get("gpu_indices") or t.get("gpus") or ([t["gpu_id"]] if t.get("gpu_id") is not None else []))
+                        }
                     ),
                     None,
                 )
@@ -3468,7 +3521,8 @@ class PlatformStore:
                     eval_task is not None and eval_task.get("status") in {"syncing", "queued"}
                 )
                 # Also mark GPU as busy if an inference model is loaded on this node
-                if row["node_id"] in inference_node_ids and not busy:
+                inference_on_gpu = row["node_id"] in inference_node_ids or row["gpu_index"] in inference_gpu_indexes.get(row["node_id"], set())
+                if inference_on_gpu and not busy:
                     busy = True
                     reserved = False
                 memory_used = round(row["memory_total_gb"] * (0.72 if busy else 0.18 if reserved else 0.04), 1)

backend/app/modules/compute_gateway/client.py

@@ -240,6 +240,10 @@ class ComputeNodeClient:
     async def inference_status(self) -> dict[str, Any]:
         return await self._request("GET", "/inference/status", timeout=INFERENCE_STATUS_TIMEOUT)
 
+    async def gpu_resources(self) -> list[dict[str, Any]]:
+        """Read live per-GPU metrics from this compute node."""
+        return await self.gpus()
+
     async def inference_unload(self) -> dict[str, Any]:
         return await self._request("POST", "/inference/unload", json_data={}, timeout=INFERENCE_UNLOAD_TIMEOUT)
 

backend/app/modules/compute_gateway/sync.py

@@ -73,7 +73,8 @@ async def reconcile_inference_loads(store: Any) -> list[dict[str, Any]]:
             if node_status == "ready":
                 item["status"] = "ready"
                 item.pop("error", None)
-                store.mark_inference_loaded(node["id"])
+                selected_gpus = item.get("gpu_indices") or item.get("gpus")
+                store.mark_inference_loaded(node["id"], selected_gpus)
             elif node_status == "error":
                 item["status"] = "error"
                 item["error"] = status.get("error") or "model load failed on compute node"

compute/api/main.py

@@ -221,6 +221,35 @@ def create_app() -> FastAPI:
             return fallback_gpu_resources()
 
         items: list[dict[str, Any]] = []
+        processes_by_uuid: dict[str, list[dict[str, Any]]] = {}
+        try:
+            process_result = subprocess.run(
+                [
+                    "nvidia-smi",
+                    "--query-compute-apps=gpu_uuid,pid,process_name,used_memory",
+                    "--format=csv,noheader,nounits",
+                ],
+                check=True,
+                capture_output=True,
+                text=True,
+                timeout=5,
+            )
+            for process_line in process_result.stdout.splitlines():
+                process_parts = [part.strip() for part in process_line.split(",")]
+                if len(process_parts) < 4:
+                    continue
+                process_uuid, pid, process_name, used_memory = process_parts[:4]
+                processes_by_uuid.setdefault(process_uuid, []).append(
+                    {
+                        "pid": int(_safe_float(pid)),
+                        "name": process_name,
+                        "memory_used_gb": round(_safe_float(used_memory) / 1024, 2),
+                    }
+                )
+        except Exception:
+            # Some driver/runtime combinations do not expose compute-apps;
+            # utilization and memory metrics remain useful without processes.
+            pass
         for line in result.stdout.splitlines():
             parts = [part.strip() for part in line.split(",")]
             if len(parts) < 9:
@@ -244,7 +273,7 @@ def create_app() -> FastAPI:
                     "temperature": int(_safe_float(temp)),
                     "power_w": round(_safe_float(power), 1),
                     "power_limit_w": round(_safe_float(power_limit), 1),
-                    "processes": [],
+                    "processes": processes_by_uuid.get(uuid, []),
                 }
             )
         return items
@@ -747,6 +776,23 @@ def create_app() -> FastAPI:
             infer_backend: str (default: "huggingface")
             infer_dtype: str (default: "auto")
         """
+        requested_gpus = payload.get("gpu_indices")
+        if requested_gpus is None:
+            requested_gpus = payload.get("gpus") or []
+        try:
+            requested_gpus = sorted({int(item) for item in requested_gpus})
+        except (TypeError, ValueError) as exc:
+            raise HTTPException(status_code=400, detail=f"invalid GPU selection: {exc}") from exc
+        if any(item < 0 for item in requested_gpus):
+            raise HTTPException(status_code=400, detail="GPU index must be non-negative")
+        if requested_gpus:
+            known_gpus = {int(item.get("gpu_index", item.get("id", -1))) for item in gpu_resources()}
+            missing = sorted(set(requested_gpus) - known_gpus)
+            if missing:
+                raise HTTPException(status_code=409, detail=f"requested GPU not found: {missing}")
+            conflict = sorted(set(requested_gpus).intersection(process_manager.locked_gpus()))
+            if conflict:
+                raise HTTPException(status_code=409, detail=f"GPU already used by another compute job: {conflict}")
         session = get_inference_session()
         result = session.load(
             model_name_or_path=payload.get("model_name_or_path", ""),
@@ -754,6 +800,7 @@ def create_app() -> FastAPI:
             template=payload.get("template", "qwen"),
             infer_backend=payload.get("infer_backend", "huggingface"),
             infer_dtype=payload.get("infer_dtype", "auto"),
+            gpu_indices=requested_gpus,
         )
         return result
 

compute/engines/llama_factory/inference.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import os
 import threading
 import time
 import uuid
@@ -36,6 +37,7 @@ class InferenceSession:
         self._generating_args: dict[str, Any] = {}
         self._model_name: str = ""
         self._adapter_path: str = ""
+        self._gpu_indices: list[int] = []
         self._loaded_at: float = 0.0
 
     @property
@@ -53,6 +55,7 @@ class InferenceSession:
                 "loaded_at": self._loaded_at,
                 "request_id": self._request_id,
                 "error": self._error,
+                "gpu_indices": list(self._gpu_indices),
             }
 
     def wait_until_loaded(self, timeout: float | None = None) -> dict[str, Any]:
@@ -87,8 +90,12 @@ class InferenceSession:
         template="qwen",
         infer_backend="huggingface",
         infer_dtype="auto",
+        gpu_indices=None,
         **kwargs,
     ) -> dict[str, Any]:
+        requested_gpus = sorted({int(item) for item in (gpu_indices or [])})
+        if any(item < 0 for item in requested_gpus):
+            return {"loaded": False, "status": "error", "error": "GPU index must be non-negative"}
         with self._state_lock:
             if self._status == "loading":
                 # A model is already loading — dedupe, reuse the same request id.
@@ -97,6 +104,7 @@ class InferenceSession:
             self._status = "loading"
             self._error = ""
             self._request_id = uuid.uuid4().hex[:12]
+            self._gpu_indices = requested_gpus
             self._cancel_requested = False
             self._load_args = {
                 "model_name_or_path": model_name_or_path,
@@ -115,13 +123,21 @@ class InferenceSession:
 
     def _load_worker(self) -> None:
         """Build the ChatModel off the state lock so info() never blocks."""
+        with self._state_lock:
+            requested_gpus = list(self._gpu_indices)
         model = None
         tokenizer = None
         generating_args: dict[str, Any] = {}
         error = ""
+        previous_visible_devices = os.environ.get("CUDA_VISIBLE_DEVICES")
         try:
+            # Set visibility before LLaMA-Factory/PyTorch initializes CUDA.
+            if requested_gpus:
+                os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(str(item) for item in requested_gpus)
             if self._teardown_old:
                 self._release_model()
+                with self._state_lock:
+                    self._gpu_indices = requested_gpus
             from llamafactory.chat import ChatModel
             from llamafactory.hparams import get_infer_args
 
@@ -138,6 +154,12 @@ class InferenceSession:
                 generating_args = dict(generating_args)
         except Exception as exc:  # noqa: BLE001 - surface load failure via status
             error = str(exc)
+        finally:
+            if requested_gpus:
+                if previous_visible_devices is None:
+                    os.environ.pop("CUDA_VISIBLE_DEVICES", None)
+                else:
+                    os.environ["CUDA_VISIBLE_DEVICES"] = previous_visible_devices
         with self._state_lock:
             if error:
                 self._model = None
@@ -152,6 +174,7 @@ class InferenceSession:
                 self._model = None
                 self._tokenizer = None
                 self._status = "idle"
+                self._gpu_indices = []
                 return
             self._model = model
             self._tokenizer = tokenizer
@@ -189,6 +212,7 @@ class InferenceSession:
                 self._adapter_path = ""
                 self._loaded_at = 0.0
                 self._error = ""
+                self._gpu_indices = []
 
     def unload(self) -> dict[str, Any]:
         with self._state_lock:
@@ -208,6 +232,7 @@ class InferenceSession:
                 self._adapter_path = ""
                 self._loaded_at = 0.0
                 self._error = ""
+                self._gpu_indices = []
         return {"unloaded": True, "status": "idle"}
 
     def chat(self, messages, temperature=0.95, top_p=0.7, max_new_tokens=1024, do_sample=True, **kwargs) -> dict[str, Any]:

frontend/src/api/modules/fineTune.ts

@@ -42,12 +42,23 @@ export interface FineTunePreflightResult {
   sync_results?: Array<Record<string, unknown>>
 }
 
+export interface FineTuneGpuStatus {
+  source: string
+  items: Array<Record<string, unknown>>
+  selected_gpus: number[]
+  error?: string
+}
+
 /** 训练任务列表 */
 export const getFineTuneList = () => get<FineTuneTask[]>('/fine-tune')
 
 /** 训练任务详情 */
 export const getFineTune = (id: string | number) => get<FineTuneTask>(`/fine-tune/${id}`)
 
+/** 获取任务所在 Compute 节点的实时 GPU 指标 */
+export const getFineTuneGpuStatus = (id: string | number) =>
+  get<FineTuneGpuStatus>(`/fine-tune/${id}/gpu-status`)
+
 /** 任务名查重 */
 export const checkFineTuneName = (name: string) =>
   get<{ exists: boolean }>('/fine-tune/check-name', { name })

frontend/src/types/index.ts

@@ -218,6 +218,8 @@ export interface LoadedModel {
   port?: number
   node_id?: string
   node_name?: string
+  gpu_indices?: number[]
+  gpus?: number[]
   error?: string
 }
 
@@ -239,6 +241,8 @@ export interface CompareModelRef {
   gpu_id: number
   node_id?: string
   node_name?: string
+  gpu_indices?: number[]
+  gpus?: number[]
   source?: string
   port?: number
 }
@@ -282,7 +286,9 @@ export interface StartEvalPayload {
   eval_task_name: string
   eval_type: EvalType
   model_id: string | number
-  gpu_id: string | number
+  gpu_id: string | number | string[]
+  gpu_indices?: number[]
+  gpus?: number[]
   compute_node_id?: string
   dataset_id: string | number
   dimension_id: string | number

frontend/src/views/eval/EvalCreateView.vue

@@ -39,7 +39,7 @@ const createdDimensionId = ref<string | number>('')
 const taskForm = ref<EvalTaskSetupDraft>({
   eval_task_name: '',
   model_id: '',
-  gpu_id: '',
+  gpu_id: [],
   data_source: 'dataset',
   dataset_id: '',
   leaderboard: false,
@@ -145,12 +145,24 @@ async function handleSubmit() {
     const dimensionId = await resolveDimensionId()
     // GPU 选择为「节点:GPU序号」复合值，解析出节点与 GPU 序号，
     // 多算力节点时必须把节点信息传给后端，否则会派发到错误的算力节点
-    const [gpuNodeId, gpuIndex] = String(taskForm.value.gpu_id).split(':')
+    const selectedGpuKeys = Array.isArray(taskForm.value.gpu_id)
+      ? taskForm.value.gpu_id
+      : [String(taskForm.value.gpu_id)]
+    const gpuSelections = selectedGpuKeys
+      .map((key) => {
+        const [nodeId, gpuIndex] = String(key).split(':')
+        return { nodeId, gpuIndex: Number(gpuIndex) }
+      })
+      .filter((item) => item.nodeId && Number.isInteger(item.gpuIndex) && item.gpuIndex >= 0)
+    const gpuNodeId = gpuSelections[0]?.nodeId || ''
+    const gpuIndices = gpuSelections.map((item) => item.gpuIndex)
     const evalResult: any = await startEval({
       eval_task_name: taskForm.value.eval_task_name,
       eval_type: 'custom',
       model_id: taskForm.value.model_id,
-      gpu_id: Number(gpuIndex) || 0,
+      gpu_id: gpuIndices[0] ?? 0,
+      gpu_indices: gpuIndices,
+      gpus: gpuIndices,
       compute_node_id: gpuNodeId || '',
       dataset_id: taskForm.value.data_source === 'dataset' ? taskForm.value.dataset_id : '',
       dimension_id: dimensionId,

frontend/src/views/eval/create/EvalTaskSetupStep.vue

@@ -6,7 +6,7 @@ import type { DatasetItem, GpuInfo, TrainedModel } from '@/types'
 export interface EvalTaskSetupDraft {
   eval_task_name: string
   model_id: string | number
-  gpu_id: string | number
+  gpu_id: string | number | string[]
   data_source: 'dataset' | 'inference'
   dataset_id: string | number
   leaderboard: boolean
@@ -23,6 +23,13 @@ defineProps<{
 const form = defineModel<EvalTaskSetupDraft>({ required: true })
 const formRef = ref<FormInstance>()
 
+function handleGpuChange(value: string | number | string[]) {
+  const keys = Array.isArray(value) ? value.map(String) : [String(value || '')]
+  const nodeId = keys[0]?.split(':', 1)[0]
+  if (!nodeId || !Array.isArray(form.value.gpu_id)) return
+  form.value.gpu_id = keys.filter((key) => key.split(':', 1)[0] === nodeId)
+}
+
 const rules: FormRules<EvalTaskSetupDraft> = {
   eval_task_name: [
     { required: true, message: '请输入任务名称', trigger: 'blur' },
@@ -101,7 +108,16 @@ defineExpose({ validate })
     </el-form-item>
 
     <el-form-item label="选择 GPU" prop="gpu_id">
-      <el-select v-model="form.gpu_id" placeholder="请选择 GPU" style="width: 100%" :loading="loading">
+      <el-select
+        v-model="form.gpu_id"
+        multiple
+        collapse-tags
+        collapse-tags-tooltip
+        placeholder="请选择同一算力节点内的一张或多张 GPU"
+        style="width: 100%"
+        :loading="loading"
+        @change="handleGpuChange"
+      >
         <el-option
           v-for="gpu in gpus"
           :key="`${gpu.node_id || ''}:${gpu.id ?? 0}`"

frontend/src/views/eval/create/StartEvalStep.vue

@@ -21,7 +21,7 @@ function nameOf<T extends { id: string | number; name?: string }>(items: T[], id
 
 /** GPU 选择为「节点:GPU序号」复合值，解析并展示为可读标签 */
 const gpuLabel = computed(() => {
-  const key = String(props.task.gpu_id || '')
+  const key = Array.isArray(props.task.gpu_id) ? String(props.task.gpu_id[0] || '') : String(props.task.gpu_id || '')
   const gpu = props.gpus.find((g) => `${g.node_id || ''}:${g.id ?? 0}` === key)
   if (gpu) return `${gpu.node_name || gpu.node_code || '算力节点'} / GPU ${gpu.id ?? 0}`
   const [nodeId, idx] = key.split(':')

frontend/src/views/inference/InferenceCreateView.vue

@@ -4,8 +4,7 @@ import { useRouter } from 'vue-router'
 import { ElMessage, type FormInstance, type FormRules } from 'element-plus'
 import PageCard from '@/components/PageCard.vue'
 import { getModelList, getTrainedModels } from '@/api/modules/model'
-import { getSystemInfo } from '@/api/modules/system'
+import { getComputeGpus, getComputeNodes, type ComputeNode } from '@/api/modules/compute'
-import { getComputeNodes, type ComputeNode } from '@/api/modules/compute'
 import { createCompare, loadCompare } from '@/api/modules/compare'
 import type { ModelItem, TrainedModel, GpuInfo } from '@/types'
 
@@ -83,8 +82,8 @@ const form = reactive({
   description: '',
   /** 选中的模型 key（单选） */
   model_key: '',
-  /** 使用的 GPU */
+  /** 使用的 GPU（同一节点内可多选） */
-  gpu_key: '',
+  gpu_keys: [] as string[],
 })
 
 const rules: FormRules = {
@@ -94,12 +93,26 @@ const rules: FormRules = {
 
 /** 当前选中的模型对象 */
 const selectedModel = computed(() => modelMap.value[form.model_key])
-const selectedGpu = computed(() => idleGpus.value.find((g) => `${g.node_id || ''}:${g.id ?? 0}` === form.gpu_key))
+const selectedGpus = computed(() => idleGpus.value.filter((gpu) => form.gpu_keys.includes(gpuKey(gpu))))
+
+function gpuKey(gpu: GpuInfo) {
+  return `${gpu.node_id || ''}:${gpu.id ?? 0}`
+}
+
+function handleGpuChange(keys: string[]) {
+  const nodeId = keys[0]?.split(':', 1)[0]
+  if (!nodeId) return
+  const filtered = keys.filter((key) => key.split(':', 1)[0] === nodeId)
+  if (filtered.length !== keys.length) {
+    ElMessage.info('一次推理只能使用同一算力节点内的 GPU，已忽略其它节点的选择')
+  }
+  form.gpu_keys = filtered
+}
 
 watch(selectedModel, (model) => {
   if (!model?.compute_node_id) return
   const gpu = idleGpus.value.find((item) => item.node_id === model.compute_node_id)
-  if (gpu) form.gpu_key = `${gpu.node_id || ''}:${gpu.id ?? 0}`
+  if (gpu) form.gpu_keys = [gpuKey(gpu)]
 })
 
 async function handleSubmit() {
@@ -111,6 +124,10 @@ async function handleSubmit() {
       ElMessage.warning('请选择模型')
       return
     }
+    if (!selectedGpus.value.length) {
+      ElMessage.warning('请至少选择一张空闲 GPU')
+      return
+    }
     submitting.value = true
     startupStatus.value = '正在创建推理任务...'
     try {
@@ -130,9 +147,11 @@ async function handleSubmit() {
             model_name: m.name,
             model_path: m.model_path,
             source: m.source,
-            gpu_id: selectedGpu.value?.id ?? 0,
+            gpu_id: selectedGpus.value[0]?.id ?? 0,
-            node_id: selectedGpu.value?.node_id || m.compute_node_id,
+            gpu_indices: selectedGpus.value.map((gpu) => Number(gpu.id ?? 0)),
-            node_name: selectedGpu.value?.node_name || m.compute_node_name,
+            gpus: selectedGpus.value.map((gpu) => Number(gpu.id ?? 0)),
+            node_id: selectedGpus.value[0]?.node_id || m.compute_node_id,
+            node_name: selectedGpus.value[0]?.node_name || m.compute_node_name,
           },
         ],
       })
@@ -169,17 +188,17 @@ async function loadData() {
     const [db, trained, sys, nodes] = await Promise.all([
       getModelList(),
       getTrainedModels(),
-      getSystemInfo(),
+      getComputeGpus(),
       getComputeNodes(),
     ])
     dbModels.value = db || []
     trainedModels.value = trained?.models || []
-    gpus.value = sys?.gpu || []
+    gpus.value = (sys || []) as unknown as GpuInfo[]
     computeNodes.value = nodes || []
     // 默认选中第一个空闲 GPU
     if (idleGpus.value.length > 0) {
       const firstGpu = idleGpus.value[0]
-      form.gpu_key = `${firstGpu.node_id || ''}:${firstGpu.id ?? 0}`
+      form.gpu_keys = [gpuKey(firstGpu)]
     }
   } catch {
     // ignore
@@ -228,12 +247,20 @@ onMounted(loadData)
       </el-form-item>
 
       <el-form-item label="GPU">
-        <el-select v-model="form.gpu_key" style="width: 400px">
+        <el-select
+          v-model="form.gpu_keys"
+          multiple
+          collapse-tags
+          collapse-tags-tooltip
+          style="width: 400px"
+          placeholder="请选择同一算力节点内的一张或多张 GPU"
+          @change="handleGpuChange"
+        >
           <el-option
             v-for="g in idleGpus"
-            :key="`${g.node_id || ''}:${g.id ?? 0}`"
+            :key="gpuKey(g)"
             :label="`${g.node_name || g.node_code || '算力节点'} / ${g.name} (GPU${g.id ?? 0}) [空闲]`"
-            :value="`${g.node_id || ''}:${g.id ?? 0}`"
+            :value="gpuKey(g)"
           />
         </el-select>
       </el-form-item>

frontend/src/views/system/TrainingLogView.vue

@@ -8,10 +8,9 @@ import TrainingTaskOverview from './training-log/TrainingTaskOverview.vue'
 import { usePolling } from '@/composables/usePolling'
 import '@/plugins/echarts-training-log'
 import { useModelsStore } from '@/stores/models'
-import { getFineTune, getFineTuneDiagnostics, getFineTuneLogs, getFineTuneMetrics, type TrainingDiagnostic } from '@/api/modules/fineTune'
+import { getFineTune, getFineTuneDiagnostics, getFineTuneGpuStatus, getFineTuneLogs, getFineTuneMetrics, type TrainingDiagnostic } from '@/api/modules/fineTune'
 import { getTrainingLogFiles, getTrainingLogContent } from '@/api/modules/log'
 import { getDataset } from '@/api/modules/dataset'
-import { getSystemInfo } from '@/api/modules/system'
 import { TRAIN_TYPE_MAP, TRAIN_METHOD_MAP } from '@/constants'
 import {
   buildMetricChartOption,
@@ -205,15 +204,20 @@ async function loadDataset(datasetId: string | number) {
   }
 }
 
-async function loadGpuStatus() {
+async function loadGpuStatus(currentTask: FineTuneTask) {
   try {
-    const systemInfo = await getSystemInfo()
+    const live = await getFineTuneGpuStatus(currentTask.id)
-    gpuPool.value = systemInfo.gpu ?? []
+    if (live.source === 'compute' && live.items.length) {
+      gpuPool.value = live.items as unknown as GpuInfo[]
       gpuUpdatedAt.value = new Date()
       gpuLoadError.value = ''
+      return
+    }
+    gpuPool.value = []
+    gpuLoadError.value = live.error || 'Compute 节点暂未返回实时 GPU 指标'
   } catch {
     gpuLoadError.value = 'GPU 监控数据暂时不可用'
-    if (!gpuUpdatedAt.value) gpuPool.value = []
+    gpuPool.value = []
   }
 }
 
@@ -306,7 +310,7 @@ async function refreshAll() {
     const datasetPromise = currentTask.train_dataset_id
       ? loadDataset(currentTask.train_dataset_id)
       : Promise.resolve()
-    await Promise.all([datasetPromise, loadLog(currentTask), loadGpuStatus(), loadDiagnostics(currentTask)])
+    await Promise.all([datasetPromise, loadLog(currentTask), loadGpuStatus(currentTask), loadDiagnostics(currentTask)])
     await loadMetrics(currentTask)
   } finally {
     loading.value = false