grpc_pass only handles native gRPC (HTTP/2 frames). The web dashboard
uses gRPC-web via Connect protocol (HTTP/1.1 POST requests). Switch
api.fxhnt.ai nginx block to proxy_pass so tonic_web::GrpcWebLayer on
the API service can handle the protocol conversion.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
containerd on Kapsule nodes uses host DNS which can't resolve
.svc.cluster.local names. Switch all image references from
gitlab-registry.foxhunt.svc.cluster.local:5000 to localhost:30500
(NodePort on the GitLab registry). Also make training runtime image
configurable via TRAINING_RUNTIME_IMAGE env var.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- nginx pod serves Vite-built static assets from MinIO S3
- initContainer fetches dist/ from s3://foxhunt-binaries/web-dashboard/
- SPA routing via try_files, aggressive caching for hashed assets
- dashboard.fxhnt.ai proxied to web-dashboard:80 via tailscale nginx
- CI pipeline: build-web-dashboard task (npm ci + build + rclone upload)
- detect-changes: needs-dashboard output for web-dashboard/ paths
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Split the combined grafana+dashboard nginx block so dashboard.fxhnt.ai
proxies to web-gateway:3000 (the actual trading dashboard) instead of
Grafana.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add nginx basic auth (htpasswd) to ci.fxhnt.ai proxy block and
switch Argo to server auth mode. Login: jgrusewski / Welcome01.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Rename DNS record from argo→ci and update nginx proxy server_name
to match. Applied via terragrunt and kubectl.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The GPU runners used runtime_class_name=nvidia for CUDA access but
didn't request nvidia.com/gpu as a K8s resource, allowing multiple
training pods to share a GPU without K8s awareness. Add pod_spec
patches to request GPU resources properly so K8s enforces mutual
exclusion between GitLab CI and Argo training jobs.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Helm values (controller + server on platform node, MinIO artifact repo)
- WorkflowTemplate: parameterized 5-step DAG (fetch→hyperopt→train→eval→upload)
- Nginx proxy for argo.fxhnt.ai → Argo Server :2746
- DNS A record for argo.fxhnt.ai
- MinIO bucket foxhunt-training-results for Argo artifacts
- Kustomization for kubectl apply -k
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
On Ampere+ GPUs the training dtype is BF16. Network forward passes
with BF16 inputs return BF16 tensors, but all loss-path arithmetic
(rewards, dones, gamma, PER weights, Huber constants, atoms, clip
epsilon) was created as F32 from Tensor::from_vec. Candle forbids
mixed-dtype binary ops, causing "dtype mismatch in {mul,sub,add}"
on every training step.
DQN fixes (7 mismatch sites):
- Cast current_q_values to F32 immediately after forward pass
- Cast all target network outputs (standard, dueling, C51, IQN) to F32
- Keep rewards/dones/gamma/weights/atoms/half/delta as F32 (remove
.to_dtype(training_dtype) casts — now use DType::F32 sentinel)
- Entropy regularization and CQL paths now match (F32 + F32)
PPO fixes (3 mismatch sites):
- Use DType::F32 for clip epsilon one_tensor (was training_dtype BF16)
- Cast critic.forward() output to F32 in both MLP and LSTM value loss
- Cast target_returns to F32 for symlog/normalization path
Infra: revert runner-h100 from SXM2 (zero Scaleway quota) back to
ci-training-h100 PCIe pool.
2704 ml lib tests pass, 260 PPO tests pass, 0 clippy warnings.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Eliminate foxhunt-binaries and training-binaries PVCs — services and
training jobs now fetch binaries from MinIO via rclone initContainers.
This removes L40S GPU autoscale-for-PVC-writes, removes RWO node
affinity constraints, and requires zero image rebuilds.
Changes:
- .gitlab-ci.yml: replace ~115 lines of binary-writer pod logic with
aws s3 cp to MinIO (~25 lines)
- 8 service YAMLs + 2 GPU overlays: add rclone initContainer + emptyDir
- Training job template: MinIO rclone fetch replaces PVC copy
- Delete foxhunt-binaries-pvc.yaml and training-binaries-pvc.yaml
- Add s3.fxhnt.ai DNS record (Terraform) and nginx proxy block
- Replace pod-writer-deploy skill with MinIO-based deploy skill
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
BUG #41 kept forward pass in F32 for autograd, but the target-side
tensors (reward, gamma, done, next_q) were cast to BF16 via `dtype`.
The `state_action_values.sub(&target_q_values)` then hit F32-vs-BF16
mismatch on Ampere+ GPUs, causing every training step to fail silently.
Fix: `.to_dtype(state_action_values.dtype())` on the detached target.
Safe because target is detached (no autograd graph to break).
Also: H100 runner → SXM2 pool, GPU availability checker script.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Pool ci-training-h100x2 (H100-2-80G, 160GB VRAM total).
MultiGpuConfig::detect() in ml crate auto-enables data parallelism.
Tags: kapsule,h100x2. Autoscale 0→1.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Clear separation: l40s tag → L40S pool, h100 tag → H100 pool.
Updated runner-rl-values.yaml and CI base templates to match.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Create ci-training-h100 runner (runner-h100-values.yaml)
- Route hyperopt-dqn and hyperopt-ppo to H100 via kapsule,h100 tags
- H100 is ~2x faster and cheaper per run vs L40S for hyperopt workloads
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Remove from workspace members, delete K8s deployment, remove nginx routing.
Training metrics and system health now served directly by API Gateway.
Changes:
- Cargo.toml: remove services/monitoring_service from workspace members
- services/monitoring_service/: deleted entirely (6 files)
- infra/k8s/services/monitoring-service.yaml: deleted
- infra/k8s/network-policies/monitoring-service.yaml: deleted
- infra/k8s/network-policies/api-gateway.yaml: remove egress rule to monitoring-service
- infra/k8s/network-policies/web-gateway.yaml: remove egress rule to monitoring-service
- infra/k8s/services/api-gateway.yaml: remove stale MONITORING_SERVICE_URL env var
- infra/k8s/gitlab/tailscale-proxy.yaml: redirect monitor.fxhnt.ai to api-gateway:50051
- .gitlab-ci.yml: remove monitoring_service from compile, copy, and deploy loops
- services/trading_service/src/services/monitoring.rs: update stale comments to
reference api_gateway (not monitoring_service) as training metrics host
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- DQN/PPO trainers now record epoch, loss, val_loss, batch/s every epoch
- CI training jobs get Prometheus scrape annotations via runner overrides
- Allow Prometheus (foxhunt namespace) to scrape foxhunt-ci pods
- Skip no-model metrics in monitoring service session grouping
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add DNS record, nginx gRPC proxy block, network policy for Tailscale
ingress, and auto-detect fxhnt.ai in fxt monitor URL derivation.
Show GPU telemetry even when no training sessions are active.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Runner #2 already has tags [kapsule, gpu] but CI jobs still used
kapsule-rl. Also removes stale minio-ca-cert volume from runner values.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The remoteWrite block had placeholder values (COCKPIT_METRICS_PUSH_URL,
COCKPIT_PUSH_TOKEN) that were never replaced, causing Prometheus to
spam "Failed to send batch" warnings every minute. Cockpit was replaced
by self-hosted Grafana+Prometheus+Loki+Tempo.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Grafana now stores users, preferences, and state in our existing
PostgreSQL instance (dedicated 'grafana' database). This eliminates
the 2Gi PVC dependency — all persistent state lives in Postgres,
dashboards in ConfigMaps, datasources in Helm values.
Also adds Grafana to Postgres NetworkPolicy ingress allowlist.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The Grafana PVC was lost during cleanup (orphaned PV deletion).
Dashboards survived (ConfigMap-based), but user accounts and
datasources were lost.
This commit captures all Helm values previously set via --set:
- Sidecar config with foldersFromFilesStructure for folder organization
- Loki + Tempo datasources alongside Prometheus
- Platform node toleration for gitlab taint
Also fixed CI/CD folder annotation (slash caused nested directory).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
db-credentials, jwt-secret, redis-credentials, s3-credentials.
Each service only references the secrets it needs.
Values are REPLACE_IN_DEPLOY markers — real values
injected by scripts/deploy-secrets.sh.
Also updated references in postgres.yaml, migrate.yaml,
and postgres-init.yaml to use db-credentials.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
RL runner only handles GPU training jobs, so global nvidia runtime
is safe and required (KUBERNETES_RUNTIME_CLASS_NAME override wasn't
being applied). Main runner keeps overwrite-allowed pattern.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Pool mapping:
- ci-compile-cpu (POP2-32C-128G): Rust compile, web dashboard, manifest
- ci-compile (L4): CUDA compile with stubs only
- ci-training (L40S): ALL training (RL + supervised)
Main runner default → ci-compile-cpu
RL runner default → ci-training
.train-rl-base → explicit ci-training node selector
Replaced all stale ci-rl references with correct pool names.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Tempo was OOMKilled every ~30 min at 512Mi, causing web-gateway OTLP
export errors. Increased to 1Gi.
RL runner had cpu_request_overwrite_max_allowed=4000m but hyperopt RL
jobs need 6000m for parallel PSO trials. Increased to 8000m.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Remove runtime_class_name="nvidia" from runner configs (applied to ALL
pods, broke compile on CPU-only ci-compile-cpu nodes). Instead use
runtime_class_name_overwrite_allowed + KUBERNETES_RUNTIME_CLASS_NAME
per-job on GPU templates (.train-rl-base, .train-validate-base).
Also routes build-web-dashboard and write-manifest to ci-compile-cpu.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Switch all MinIO communication from HTTP to HTTPS across the entire stack:
- MinIO deployment: mount TLS secret, tcpSocket probes, HTTPS init-buckets
- 11 service YAMLs: HTTPS rclone endpoint + CA cert volume mount
- Training job template + train.sh: HTTPS for fetch-binaries and uploader
- CI pipeline (.gitlab-ci.yml): all 7 rclone exports use HTTPS + CA cert
- GitLab runner values: minio-ca-cert ConfigMap volume for CI pods
- Rust: CA cert loading via MINIO_CA_CERT_PATH in training_uploader,
storage backend, data_acquisition uploader, and k8s_dispatcher
- Cert generation script (infra/scripts/generate-minio-tls.sh)
Fixes training uploader S3 upload failures caused by with_allow_http(false)
connecting to an HTTP endpoint.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The existing `global.appConfig.gitlab_kas.enabled: false` only affected
the KAS subchart config but was ignored by the chart template that
generates gitlab.yml. Adding `global.kas.enabled: false` properly sets
`gitlab_kas.enabled: false` in gitlab.yml and removes the orphaned
gitlab-kas Service, stopping ~5 GRPC::Unavailable errors per git push.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Web-gateway routing:
- Point TRADING_SERVICE_URL at api-gateway (proto mismatch fix)
Web-gateway uses foxhunt.tli.TradingService proto but was connecting
directly to trading-service which implements trading.TradingService.
api-gateway already proxies Subscribe* → Stream* correctly.
GitLab KAS:
- Disable gitlab_kas in appConfig to stop sidekiq NotifyGitPushWorker
errors (KAS pod was already disabled but Rails still tried to connect)
Trading service monitoring (3 stubs → real):
- AcknowledgeAlert: real alert lookup + state mutation in shared store
- GetActiveAlerts: returns actual active alerts from in-memory store
- StreamAlerts: now persists generated alerts (capped at 1000 entries)
Trading service ML streams (2 stubs → real):
- StreamModelMetrics: emits real inference_count, error_count, latency
per model every N seconds from the RuntimeModelInfo registry
- StreamSignalStrength: emits per-symbol signal aggregation from model
ensemble weights and latency confidence
Backtesting service:
- stop_backtest: real CancellationToken cancellation (was no-op)
Tokens stored per-backtest, execute_backtest wraps strategy call
in tokio::select! for immediate cancellation
Deleted 7 empty placeholder files:
- 4 Wave D regime stubs (dynamic_stops, ensemble, performance_tracker,
position_sizer) — comment-only files, never wired
- 2 Wave 3 feature stubs (microstructure, statistical)
- 1 PPO stub (unified_ppo.rs — empty struct definitions)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add nginx server block for api.fxhnt.ai with grpc_pass to the
api-gateway ClusterIP service. Add Scaleway DNS A record via Terraform
module. Fix fxt gRPC client to enable TLS when connecting to https://
endpoints (tonic requires explicit ClientTlsConfig).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
RL models (DQN/PPO) only need basic CUDA runtime (~2GB), while supervised
models need cuDNN (~4GB). Splitting saves ~2GB pull time per RL job.
Rename the L4 GPU pool from ci-compile to ci-rl to reflect its actual use.
Add DaemonSet image pre-puller to cache training images on GPU nodes.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
RL env simulation is single-threaded (~1 core actual). Lowering from
7000m to 2000m request allows DQN + PPO hyperopt to run simultaneously
on one L4 node.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Fix ServiceAccount: use serviceAccount.name (not deprecated serviceAccountName)
- concurrent=1: one RL job gets full L4 node (7000m/7800m CPU, 16Gi/40Gi RAM)
- Faster iteration: ~2.3x more CPU per job vs splitting across 2 concurrent
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Set runtime_class_name=nvidia at runner level (all GPU pools have GPUs)
- Fix KUBERNETES_NODE_SELECTOR format: "key=value" not just value
- Replace wrong *_overwrite_allowed (regex) with correct
*_overwrite_max_allowed (max value) for CPU/memory overrides
- Remove per-job KUBERNETES_RUNTIME_CLASS_NAME (runner-level handles it)
- Simplify GPU detection in test/compile (GPU always present now)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
compile-services OOMKilled at 8Gi — ml crate with CUDA needs ~20Gi.
CI variable overrides (KUBERNETES_MEMORY_LIMIT) are silently dropped
by runner config merger for non-auto-generated fields. Increase
defaults to 3500m/12Gi request, 7800m/28Gi limit to fit L4 nodes.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Setting runtime_class_name = "" causes Kubernetes to reject pod creation
with "resource name may not be empty". Omitting it entirely means no
runtimeClassName on pod spec by default; GPU jobs override via
KUBERNETES_RUNTIME_CLASS_NAME CI variable.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
L4 ci-compile node was failing with 'no runtime for nvidia' because
fresh nodes take time to install nvidia drivers. Kaniko builds don't
need GPU at all. Now only Rust compile/test and training jobs request
nvidia RuntimeClass via KUBERNETES_RUNTIME_CLASS_NAME CI variable.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- compile-services now depends on check (not test), running in parallel
with test stage — saves ~10 min from critical path
- Runner concurrent bumped 4→10 so all 9 Kaniko builds run simultaneously
- Runner default resources lowered (500m/1Gi) for lightweight Kaniko jobs;
Rust compile/test/training override via KUBERNETES_*_REQUEST CI vars
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
