Argo Events labels workflows with events.argoproj.io/trigger, not
workflows.argoproj.io/workflow-template. Fixed find_ci_workflow() so
ci-train preset can actually find CI pipeline runs.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
New `ci-train` preset monitors the Argo CI workflow for a commit,
polls until completion, then auto-submits the training job (defaults
to hyperopt). Prints Prometheus/log links during monitoring.
Usage: ./infra/scripts/train.sh ci-train --model dqn [--commit SHA]
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Three root causes for "no metrics" on the training dashboard:
1. Dashboard template variables ($model, $fold) sourced from
foxhunt_training_current_epoch which isn't emitted until the first
epoch completes. Switch to foxhunt_training_step which fires from
step 500 onward.
2. train.sh pod template missing Prometheus annotations
(prometheus.io/scrape, port, path). Also add the
app.kubernetes.io/component label to the eval manifest so
evaluation pods are discoverable too.
3. DQN and PPO trainers only called set_epoch() at the END of each
epoch. Move the call to the TOP of the epoch loop so the gauge
exists from the first training iteration.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The train_baseline_rl binary expects --output-dir, not --output.
Also adds prometheus.io scrape annotations to job pod templates
so training metrics appear in Grafana.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Bring Branching Dueling Q-Network (Tavakoli 2018) to full Rainbow parity
with the existing GPU hotpath. 3 independent advantage heads (exposure=5,
order=3, urgency=3) decompose the 45-action space into learnable branches.
H1 - CUDA fallback: gate GpuExperienceCollector when use_branching=true
(fused kernel hardcodes NUM_ACTIONS=5, incompatible with 45 factored)
H2 - Per-branch C51 distributional: each branch outputs [batch, n_d, atoms]
log-softmax, loss = avg of D cross-entropies vs projected Bellman target
M1 - NoisyNet: MaybeNoisyLinear enum in branch heads, reset_noise/disable_noise
wired through select_action, compute_loss, and set_eval_mode
M2 - Regime-conditional IS weights: Trending=1.2, Ranging=0.8, Volatile=0.6
applied to branching loss via ADX/CUSUM features at state[40:41]
M3 - State dim alignment: align_dim_for_tensor_cores() in from_dqn_params()
for H100 HMMA dispatch (8-byte alignment)
L1 - Fill simulator: splitmix64 replaces golden ratio hash (chi-squared tested)
L2 - Hyperopt 29D: branch_hidden_dim [64,256] added to PSO search space
Config plumbing: branch_hidden_dim, v_min/v_max/num_atoms, use_distributional,
use_noisy, noisy_sigma_init all flow from DQNConfig → BranchingConfig.
10 files, +3207/-125 lines, 33 branching tests + 387 ml-dqn + 284 ml-core pass.
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>
Move all compiled binary uploads/downloads from public Scaleway S3 to
MinIO running inside Kapsule. Keeps proprietary binaries and trained
models off the public internet.
- CI compile/upload jobs: Scaleway → MinIO endpoint + credentials
- train.sh: all 3 rclone blocks → RCLONE_CONFIG_MINIO_*
- Deploy stage: apply MinIO manifests + wait for readiness before rollout
- Add infra/k8s/minio/ (Deployment, Service, Secret, PVC, init-buckets Job)
- Update s3-credentials.secret.example → minio-credentials format
IaC Terragrunt jobs and SCW registry pulls intentionally unchanged.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
All K8s nodeSelectors, CI config, runner config, monitoring docs, and
smoke tests now reference the new pool names provisioned by terragrunt.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Mass cleanup of crates/ml/:
- Delete 121 dead/broken/superseded example files (-44,098 lines)
- Delete 6 QAT test files (-4,201 lines) — QAT is disabled at runtime
(trainer.rs falls back to FP32 with warning)
- Delete 2 stale markdown files in examples/
- Delete orphaned src/bin/train_tft.rs (unimplemented stub)
- Clean up Cargo.toml: remove stale [[example]] entries, add missing ones
- Fix stale binary references in log_size_test.rs
- Add infra consistency test (35 checks across Dockerfile/train.sh/Cargo.toml)
Remaining examples (7): train_baseline_rl, train_baseline_supervised,
evaluate_baseline, hyperopt_baseline_rl, hyperopt_baseline_supervised,
download_baseline, cuda_test
All 2390 lib tests pass. All examples compile. 35/35 infra checks pass.
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>
- 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>
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>
- 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>
- train.sh: submit GPU training K8s Jobs with presets
(quick-test, single-model, full-ensemble, hyperopt)
- smoke-test.sh: end-to-end verification of cluster,
networking, databases, services, and node pools
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
