feat: 评测任务元数据增强与前端展示优化

后端:
- 新增评测指标标签生成(_build_eval_metric_label/_basic_metric_labels)
- eval 任务 payload 富化(_enrich_eval_payload): 解析模型名/数据集名/维度配置, 生成 metric_label
- 任务创建时解析 trained_models/模型名, metric 回退为 metric_label
- 运行时状态刷新增加 2s 节流, 避免频繁触发

前端:
- EvalTask 类型增加 dataset_id/metric_label
- 评测列表与详情展示 metric_label 指标标签, 模型名/指标超长 tooltip 省略
- 创建页改用 getComputeGpus 过滤空闲 GPU, 移除 getSystemInfo 依赖

frontend/src/views/eval/EvalCreateView.vue

@@ -1,4 +1,4 @@
-<script setup lang="ts">
+<script setup lang="ts">
 import { onMounted, ref, watch } from 'vue'
 import { useRouter } from 'vue-router'
 import { ElMessage } from 'element-plus'
@@ -10,8 +10,7 @@ import StartEvalStep from './create/StartEvalStep.vue'
 import { createDimension, startEval } from '@/api/modules/eval'
 import { getTrainedModels, getModelList } from '@/api/modules/model'
 import { getDatasetList } from '@/api/modules/dataset'
-import { getSystemInfo } from '@/api/modules/system'
-import { getComputeNodes, type ComputeNode } from '@/api/modules/compute'
+import { getComputeGpus } from '@/api/modules/compute'
 import type { DatasetItem, Dimension, GpuInfo, ModelItem, TrainedModel } from '@/types'
 
 type StepExposed = { validate: () => Promise<boolean> }
@@ -83,9 +82,8 @@ async function loadData() {
   const results = await Promise.allSettled([
     getTrainedModels(),
     getDatasetList(),
-    getSystemInfo(),
     getModelList(),
-    getComputeNodes(),
+    getComputeGpus(),
   ])
 
   if (results[0].status === 'fulfilled') trainedModels.value = results[0].value?.models || []
@@ -93,18 +91,12 @@ async function loadData() {
     evalDatasets.value = (results[1].value || []).filter((dataset) => dataset.type === 'eval')
   }
   if (results[2].status === 'fulfilled') {
-    const allGpus: GpuInfo[] = results[2].value?.gpu || []
-    const nodes: ComputeNode[] = (results[4].status === 'fulfilled' ? results[4].value : []) || []
-    const onlineIds = new Set(nodes.filter((n) => n.enabled && n.scheduler_status === 'online').map((n) => n.id))
-    // Only show idle GPUs from online compute nodes
-    gpus.value = allGpus.filter(
-      (g) => g.status === 'idle' && (!g.node_id || onlineIds.has(g.node_id)),
+    evalModels.value = (results[2].value || []).filter(
+      (model) => model.purpose === 'evaluation' || (model.model_source === 'api' && !!model.api_url),
     )
   }
   if (results[3].status === 'fulfilled') {
-    evalModels.value = (results[3].value || []).filter(
-      (model) => model.purpose === 'evaluation' || (model.model_source === 'api' && !!model.api_url),
-    )
+    gpus.value = ((results[3].value || []) as unknown as GpuInfo[]).filter((g) => g.status === 'idle')
   }
 
   const failedCount = results.filter((result) => result.status === 'rejected').length