- Fix container registry region nl-ams → fr-par across all CI jobs
- Add sccache build-args to training image build (no-op fallback for local)
- Add rclone to training Docker runtime for S3 output sync
- Update train.sh: S3 sync on Job completion via rclone env-var config,
--run-id tracking, evaluate preset for walk-forward evaluation
- Add s3-credentials Secret template (.example, apply via kubectl)
- Add design doc and implementation plan
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Split single H100 GPU pool into two purpose-specific pools:
- gpu-training: H100-1-80G (€2.73/hr) for 10-model ensemble training
- gpu-inference: L4-1-24G (€0.75/hr) for cost-effective trading inference
- Add GPU taint controller DaemonSet that auto-taints new GPU nodes
with nvidia.com/gpu=true:NoSchedule to prevent non-GPU workloads
- Fix service log directory permissions with emptyDir volumes
(observability init fails creating /app/logs as non-root user)
- Increase postgres max_connections from 25 to 100
(7 services each requesting connection pools exhausted the limit)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Fix DB pool connect/acquire timeouts (50ms→5s) for cluster networking
(pool-level timeouts, not query timeouts — HFT query timeout stays at 800μs)
- Fix secret key references (DATABASE_PASSWORD→db-password) in all manifests
- Fix api-gateway port (50050→50051) to match actual gRPC listen port
- Fix web-gateway health probe path (/api/health→/health)
- Fix S3 endpoint region (nl-ams→fr-par) in ml-training-service
- Add TLS cert volume mount for ml-training-service
- Add BENZINGA_API_KEY placeholder for backtesting-service startup
- Remove always-on nodeSelector from services (let autoscaler handle)
- Add serve subcommand to ml-training-service container
All 10 pods (3 databases + 7 services) now 1/1 Running.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
