- Update COPY paths in all 3 Dockerfiles for crates/bin/services/testing layout
- Migrate service image builds from internal GitLab registry to SCW CR
- Update Kaniko auth to use SCW credentials (nologin + SCW_SECRET_KEY)
- Remove --insecure-registry flags (SCW CR is HTTPS)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The backtesting crate links libcuda.so.1 at runtime (via candle CUDA).
GPU-less CI nodes need the stub library so binaries can load, but
model_loader tests that actually use CUDA must be skipped. These will
run on gpu-training nodes separately.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The backtesting crate dynamically links to libcuda.so.1 via candle.
NVIDIA CUDA dev images include stubs at /usr/local/cuda/lib64/stubs/
but only as libcuda.so (not .so.1). Create the symlink both in the
Dockerfile (for future builds) and inline in the test script (for
the current image).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- 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>
- 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>
Remove deleted foxhunt-deploy COPY from all Dockerfiles, update sccache
default region to fr-par, add build-and-push.sh for building all service
images and pushing to rg.fr-par.scw.cloud/foxhunt/.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Dockerfile.service: unified multi-stage with sccache S3 support
- Dockerfile.web-gateway: Node dashboard + Rust gateway combined
- Dockerfile.training: CUDA 12.4 with H100 target (compute cap 9.0)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
