- Mount training-data-pvc at /data/training in build pods via runner config
- Update real_data_loader to search per-symbol subdirectories (Databento layout)
- Support .dbn.zst (zstd-compressed) files alongside raw .dbn
- Add FOXHUNT_DATA_DIR env var override for CI PVC path
- Extract decode_ohlcv_bars helper (generic over reader type)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace pod_spec strategic merge patch (silently not applied) with
runtime_class_name="nvidia". The nvidia RuntimeClass uses the
nvidia-container-runtime which injects GPU drivers, nvidia-smi, and
/dev/nvidia* devices into all containers automatically.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Strategic merge patches are applied to the Pod object, not PodSpec.
The patch needs {"spec":{"containers":[...]}} not {"containers":[...]}.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- poll_timeout 180s→600s: H100 provisioning takes ~3-5min from scale-to-zero
- build-ci-builder: only auto-run on push with Dockerfile changes (API
pipelines evaluate changes=true, causing unnecessary kaniko builds)
- check: add needs:[] to decouple from prepare stage
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
bindgen_cuda calls nvidia-smi to detect compute capability, which fails
without GPU device access. Setting CUDA_COMPUTE_CAP=90 bypasses this for
compilation. Pod spec GPU request ensures NVIDIA device plugin injects
/dev/nvidia* for test-time CUDA execution.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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>
- Add .gitignore entries for .dbn, .safetensors, .onnx and other large binaries
- Point GitLab Runner at internal cluster URL (not external Tailscale)
- Disable nginx proxy_buffering for git clone/push through Tailscale proxy
- Increase socat SSH proxy buffer sizes to 1MB
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Enable Prometheus server (GitLab chart bundled) on gitlab node pool
- Add socat SSH proxy sidecar (port 2222 → gitlab-shell) to Tailscale proxy
- Remove nginx stream module (not available in alpine) in favor of socat
- Set unlimited nginx client_max_body_size for large git pushes
- Add workhorse extraArgs for API limits
- Explicit always_on_type = DEV1-M in kapsule terragrunt
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Keep existing git.fxhnt.ai hostname for GitLab CE (repoint DNS
to new Tailscale IP after deploy). Add Terraform DNS module to
manage the A record via Terragrunt instead of manual scw CLI.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
