2 Commits

Author	SHA1	Message	Date
jgrusewski	1ad79493dc	feat(infra): training data cache architecture — MinIO → PVC sync ... - Expand training-data-pvc from 10Gi → 200Gi (OHLCV + MBP-10 + headroom) - Add data-sync-job: rclone sync from MinIO → PVC (delta-only, fast repeats) - Add sync-training-data init container to training job template (auto-syncs data from MinIO before each training run) - Add training-job + data-sync-job NetworkPolicies (MinIO, Tempo, Pushgateway) - Add app.kubernetes.io/part-of: foxhunt to training pod template - download_baseline: add --parallel N flag for concurrent Databento downloads - download_baseline: delete local files after MinIO upload (saves scratch space) - Bump download job scratch volume to 100Gi Architecture: Databento → MinIO (source of truth) → PVC (local cache). First sync pulls everything; subsequent runs only sync deltas (seconds). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-07 14:45:30 +01:00
jgrusewski	5fb84a02f0	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>	2026-02-24 20:47:50 +01:00