Remove orphaned `ci` pool (GP1-XS, 4 vCPU wasted — nothing scheduled to it).
Remove `ci-build` pool (GP1-M) — builds now run on gpu-training (H100-1-80G:
24 vCPU, 240GB, real CUDA). This eliminates the need for CUDA stubs,
separate test-gpu jobs, and ml crate exclusions.
Pool layout after:
always-on DEV1-M (core services, always on)
gitlab GP1-XS (GitLab CE + runner manager, always on)
gpu-training H100-1-80G (CI builds + ML training, scale-to-zero)
gpu-inference L4-1-24G (trading inference, scale-to-zero)
Build pod limits bumped to 16 vCPU / 64GB (from 6/12GB) to use H100 capacity.
Runner now has `gpu` tag — all tests including ml crate run in single job.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- K8s API ACL restricted to Tailscale CGNAT (100.64.0.0/10) + admin IPs
- CI and GPU pools: min_size=0 with lifecycle ignore_changes on size
so autoscaler manages actual node count without Terraform drift
- Add harden.sh script for post-provisioning security lockdown
(ACL setup, exposed service check, bucket ACL verify, registry visibility)
- Update smoke-test.sh default region to fr-par
Security audit results:
- All services ClusterIP only (no LoadBalancer/NodePort)
- No Ingress resources
- Container registry: private
- S3 buckets: HTTP 403 on anonymous access
- Gitea: all ports closed on public IP (Tailscale-only via security group)
- Kapsule nodes: no open ports on external IPs
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
