feat: training pipeline for all 10 ML ensemble models

- Add standalone training binaries: TGGN, KAN, xLSTM, Diffusion (DBN data)
- Update Dockerfile.training: 6 → 16 binaries (all 10 models + hyperopt + baseline)
- Expand train.sh: 4 → 10 models, fix registry URL and GPU pool nodeSelector
- Add GPU overlay manifests for trading-service and ml-training-service
- Create training data PVC and upload pod manifests
- Expand web-gateway model validation: 4 → 10 types (training + tune routes)
- Extend dashboard: 10 model cards grouped by category (RL/Temporal/Graph/Generative)
- Add training image build job to Gitea CI workflow
- Update GPU taint controller to exclude inference pool from tainting
- Fix job-template nodeSelector: gpu → gpu-training

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

web-dashboard/src/components/ml/EnsemblePanel.tsx

@@ -4,7 +4,7 @@ interface Props {
   predictions?: MlPrediction[];
 }
 
-const MODELS = ['DQN', 'PPO', 'TFT', 'Mamba2'];
+const MODELS = ['DQN', 'PPO', 'TFT', 'Mamba2', 'xLSTM', 'TGGN', 'TLOB', 'LNN', 'KAN', 'Diffusion'];
 
 export function EnsemblePanel({ predictions = [] }: Props) {
   // Count votes