Remove prometheus_fqdn and monitor_fqdn outputs that referenced
deleted DNS records. Add chat_fqdn and mail_fqdn for Mattermost
and Stalwart services.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
CRITICAL BUG: When use_branching=true, the optimizer trains ONLY the
branching network, but q_values_for_batch() (used by walk-forward
evaluator) was reading from dist_dueling_q_network — which was never
trained. Walk-forward was evaluating random/untrained weights.
Fix: q_values_for_batch() now uses the exposure branch Q-values
[batch, 5] from the trained branching network when use_branching=true.
Also adds device/dtype migration to match forward() contract.
Fixed IQN test that implicitly had use_branching=true (default) with
num_actions=3 — incompatible with 5-action branching exposure head.
1331 tests pass (416 ml-dqn + 915 ml), 0 failures.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The single global epsilon coin flip caused 90% of actions to use greedy
argmax across ALL 3 branches simultaneously, collapsing to 1 composite
action. Only 6/45 factored actions were used (13.3% diversity).
Fix: Each branch (exposure, order, urgency) now flips its own epsilon
coin independently:
- P(all greedy) = (1-ε)³ ≈ 72.9% at ε=0.10
- P(at least one random) ≈ 27.1% vs previous 10%
- Expected unique actions per epoch: significantly higher
Applied to both select_action() and select_action_with_confidence().
The forward pass is skipped when all 3 branches happen to be random
(0.1% chance), preserving the optimization.
1331 tests pass (416 ml-dqn + 915 ml), 0 failures.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The GPU backtest evaluator computed state_dim = feature_dim + 3 = 53
(unaligned), but the model was trained with 56 (8-aligned for H100
tensor cores). This caused matmul shape mismatch [5,53] vs [56,512]
and forced CPU fallback on every walk-forward evaluation.
Fix: align state_dim at construction with (dim + 7) & !7, matching
the training pipeline. The CUDA gather_states kernel already zero-pads
extra positions, so no kernel changes needed.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
serialize_model() saves only the trending (primary) head into a single safetensors
blob. The restore code only handled Standard(DQN) and skipped RegimeConditional
with a warning. This caused walk-forward to always evaluate the final epoch model
instead of the best per-epoch Sharpe checkpoint.
Fix: load the checkpoint directly into primary_head_mut() for the RegimeConditional
variant, matching the serialization path.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Walk-forward evaluation was using the final epoch model, which may have
overfit. Now loads the best per-epoch Sharpe checkpoint (saved during
training) back into the agent before running the walk-forward backtest.
Trial 0 showed Sharpe +3.30 at epoch 2 vs +1.84 at epoch 8 (final).
The walk-forward should evaluate the peak model, not the final one.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
NoisyNets provide learned exploration but their noise magnitude shrinks
during training. When Q-value gaps exceed noise (~0.016 vs ~0.01), the
agent converges to a single action (Short100 only). The 10% epsilon
floor guarantees 2% random selection per exposure level across all
action selection paths (select_action, select_action_with_confidence,
get_effective_epsilon).
Updated 6 test assertions and 5 comments to match the new floor.
1331 tests pass (916 ml + 416 ml-dqn), 0 failures.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The circuit breaker (>20% drawdown) carried forward across epochs,
permanently locking out all trades once triggered. With compounding
portfolios, early drawdown in one epoch could produce zero rewards
for all subsequent epochs (constant rewards bug).
Fix: reset peak_value (high-water mark) at each epoch start. Capital
still compounds (Bug #15 preserved), but drawdown is measured fresh
per epoch.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
pnl_history stores raw dollar rewards but VaR calculation treated them
as percentage returns, producing nonsensical -500% VaR values. Now
divides by initial_capital before calculating percentiles.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Image renamed from stalwartlabs/mail-server to stalwartlabs/stalwart.
Volume mounts updated from /opt/stalwart-mail/ to /opt/stalwart/
to match the actual container filesystem layout.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Merge feature/ci-comms-stack:
- Add clippy + cargo test quality gate to CI pipeline
- Clean up dead detect-changes outputs (-205 lines)
- Add onExit notification handlers (Mattermost webhook) to all workflows
- Add auto-compile ConfigMap toggle (disabled by default)
- Deploy Mattermost Team Edition on platform nodes
- Deploy Stalwart mail server (internal-only, Tailnet IMAP/admin)
- Add Mattermost egress to all Argo workflow NetworkPolicies
- Fix sensor nodeSelectors for decommissioned POP2-32C-128G
- Add DNS records for chat.fxhnt.ai and mail.fxhnt.ai
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add Stalwart all-in-one mail server: SMTP (cluster-internal relay for
Mattermost), IMAP + HTTPS admin via Tailscale proxy. RocksDB storage,
self-signed TLS (stalwart-tls secret). No public MX, no outbound SMTP.
SCW TEM relay noted in NetworkPolicy comments for future use.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add Mattermost Team Edition deployment (PVC, Service, NetworkPolicy),
DNS records (chat.fxhnt.ai, mail.fxhnt.ai), Tailscale nginx proxy
server block with WebSocket upgrade support, and Mattermost egress
rules to all 5 Argo workflow NetworkPolicies for exit handler webhooks.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add auto-compile-config ConfigMap with enabled=false. Document the
manual compile-deploy webhook trigger and toggle pattern in the
ci-pipeline sensor. Auto-compile can be enabled per-push by adding
a second trigger to the sensor or via manual webhook.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add notify-result exit handler to ci-pipeline, compile-and-deploy, and
compile-and-train templates. Posts workflow outcome to Mattermost via
incoming webhook (secret: notification-webhook). Gracefully skips if
webhook URL is placeholder or missing (optional secretKeyRef).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add clippy + cargo test quality gate to CI pipeline DAG. The test-gate
runs on ci-compile-cpu nodes with persistent cargo cache PVC for fast
incremental builds. Also simplifies detect-changes by removing 5 unused
output parameters (service-packages, training-examples, deploy-list,
needs-services, needs-training) and ~170 lines of dead shell logic.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
POP2-32C-128G is decommissioned. Sensor pods (ci-pipeline-trigger,
workflow-trigger) now target the platform node pool.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Without fsGroup, workspace PVC created by root-running compile step
is not writable by other steps. fsGroup=0 ensures all pods in the
workflow can write to the shared workspace volume.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- apply-argo-templates step now also applies argo-workflow-netpol.yaml
so NetworkPolicy changes auto-deploy on push
- Deleted stale compile-training WorkflowTemplate from cluster
(superseded by compile-and-train)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
CUBLAS_WORKSPACE_CONFIG and NVIDIA_TF32_OVERRIDE are set once at
startup before any threads or CUDA work begins. The unsafe block is
correct but triggers -W unsafe-code. Adding #[allow(unsafe_code)] at
the call site silences the warning while keeping the safety comment.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Image builds are handled by ci-pipeline, not by compile-and-train or
compile-and-deploy. Having them here caused network policy failures
(Kaniko can't reach registry) and securityContext conflicts (Kaniko
needs root). Also removed pod-level securityContext from
compile-and-train since ci-builder needs root for cargo on PVC.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Wire build-image endpoint into EventSource + Sensor for manual triggers
- Add rebuild-* DAG tasks to compile-and-deploy and compile-and-train
templates — Kaniko layer cache makes cached builds ~15-30s
- Fix foxhunt-runtime Dockerfile: rename ubuntu user instead of groupadd
(GID 1000 already exists in ubuntu:24.04)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The compile-and-train workflow uses component label 'compile-and-train'
which wasn't covered by existing network policies. Pods were blocked by
default-deny-all from reaching GitLab SSH (git clone) and HTTP (package
upload/download). Combines compile egress (SSH, external HTTPS, DNS) with
training egress (Tempo OTLP, GitLab HTTP).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
PVCs may retain ownership from a previous pod UID (e.g. after
ci-builder image rebuild). Git 2.35.2+ refuses to operate without
safe.directory set. Applied to both persistent-checkout templates.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The cargo-target PVC retains the source checkout between workflow runs.
When the container UID differs from the PVC owner, git refuses to operate
with "dubious ownership" error. Adding safe.directory resolves this.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
No cudnn feature flags remain after 6ba52425 — the devel headers
and libs were dead weight (~1.5 GB).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- gradient_utils: Add TensorId-based fallback when Var identity mismatch
causes 0/N vars to match GradStore (Candle Adam clones Arcs). Fallback
computes norm AND clips via insert_id. Throttled warning (1st + every
1000th). 7 unit tests including mismatch-actually-clips.
- monitoring: Track full 45-action factored space (5 exposure × 3 order
× 3 urgency). Fix validate_rewards false alarm on GPU path where single
aggregated mean_reward per epoch gives N=1 → std=0.
- trainer: GPU experience collection routes exposure actions through
route_action() for factored tracking instead of exposure-only counts.
Applied in both per-step and epoch-summary paths.
- train_baseline_rl: Auto-detect VRAM <8GB → disable GPU replay buffer
to prevent OOM on RTX 3050 Ti class GPUs.
- smoke_test_real_data: E2E DQN training test with 6 assertions (epoch
completion, loss decrease, finite losses, Q-value divergence, 45-action
space, finite gradient norms).
Validated: 1642 tests pass (ml=915, ml-core=311, ml-dqn=416), 0 clippy
warnings, baseline RL trains 10 epochs on CUDA with Sharpe +5.45.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Move // gpu-ok: annotations to same line as violation patterns so the
guard script's grep -v filter actually suppresses them. Fixes 6 false
positives in ensemble adapters (dqn, ppo, liquid, kan, tggn, diffusion).
Replace hardcoded 48KB shmem limit in compile_forward_kernel() with
GPU-aware query (max_shared_memory_kb) — matches gpu_experience_collector
pattern. H100 now gets 128-row tiles (was 64), eliminating tile loops
for ≤128-dim layers in the backtest forward kernel.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Capture the full DQN backtest step loop (gather → forward → DtoD →
env_step × max_len) as a replayable CUDA Graph:
- evaluate_dqn_graphed(): captures on first call via
CudaStream::begin_capture/end_capture, replays cached graph on
subsequent calls. Falls back to evaluate_dqn() on any failure.
- invalidate_dqn_graph(): discards cached graph when weights change.
- SendSyncGraph: newtype wrapper for CudaGraph (single-threaded use).
- Full unrolled capture: each step's step_i32 argument is baked in at
capture time, avoiding GPU-resident step counter complexity.
Eliminates ~5-10μs per kernel launch × 4 kernels × max_len steps
of CUDA driver overhead per evaluation.
evaluate_baseline.rs: added --cuda-graphs CLI flag to opt in.
14 backtest evaluator tests pass, 0 clippy errors.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Multi-stream pipeline (evaluate() closure path):
- Secondary env_stream with CudaEvent sync allows env_step to overlap
with gather/forward of the next iteration on H100's 132 SMs
Pure-CUDA DQN forward (evaluate_dqn):
- backtest_forward_kernel.cu: warp-cooperative dueling Q forward via NVRTC
- Eliminates candle per-op dispatch overhead, enables future CUDA Graph capture
- evaluate_baseline.rs: auto-detects dueling network and uses pure-CUDA path
Pure-CUDA PPO forward (evaluate_ppo):
- backtest_forward_ppo_kernel.cu: actor MLP → softmax → 45→5 exposure collapse → argmax
- One thread per window, bypasses candle entirely
CUDA supervised signal→action (evaluate_supervised):
- backtest_forward_supervised_kernel.cu: threshold bucketing kernel
- Candle still used for model forward (TFT/Mamba/etc), but argmax is GPU-native
Shared helpers: launch_gather, launch_env_step_on, launch_metrics_and_download
deduplicate code across all four evaluation paths.
914 tests pass, 0 clippy errors.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Lower warp kernel SM threshold from sm_90 to sm_70 (covers all modern GPUs)
- Add q_forward_branching_warp_shmem: warp-cooperative branching DQN forward
with 3 independent advantage heads + shared memory tiling
- Fix online/target dispatch to use warp branching variant when available
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Restore 45-action factored space via Branching DQN (Tavakoli 2018),
outputting 11 Q-values (5+3+3) instead of 45. This was reduced to 5
exposure-only actions during debugging and was never intended as permanent.
- Enable use_branching: true by default in DQNConfig and DQNHyperparameters
- Add branching paths to select_action_with_confidence and select_action_inference
- Update agent.rs select_action_factored for branching-aware selection
- Expand CountBonus to per-branch tracking with bonuses_branched()
- Add order_type + urgency distribution tracking in monitoring
- Add DQN_ORDER_ACTIONS=3, DQN_URGENCY_ACTIONS=3, DQN_TOTAL_ACTIONS=45 to CUDA header
- Fix 7 pre-existing clippy doc_markdown errors in regime_conditional.rs
- Fix pre-existing cognitive_complexity in replay_buffer_type.rs (extract helpers)
- Fix flaky GPU test OOM under parallel execution (CPU fallback + test VRAM safety)
- Delete unused flash_attention submodules (block_sparse, causal_masking, etc.)
- Add GPU hot-path guard scripts and ensemble/hyperopt adapter improvements
Tests: ml-dqn 416/0, ml 905/0, clippy 0 errors on both crates
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Continuous vectors and expected counts updated from 12 to 14 params
after adding signal_high_bps and signal_low_bps to Mamba2Params.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- evaluate_supervised_fold_gpu(): loads any of 8 supervised models via
UnifiedTrainable, runs GPU backtest with signal threshold mapping
- TFT uses tft_quantile_to_signal() to extract median quantile before
threshold comparison; scalar models use signal_to_action_scores() directly
- create_supervised_model() factory + find_supervised_checkpoint() helper
- Main loop dispatches GPU-first for all supervised models when --gpu-eval
- RefCell wrapper bridges &mut self forward() into Fn(&Tensor) closure
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
All supervised hyperopt adapters (TFT, Mamba2, Liquid, KAN, xLSTM, TGGN,
TLOB, Diffusion) now include:
- signal_high_bps and signal_low_bps in ParameterSpace (tunable thresholds)
- backtest_sharpe and backtest_trades fields in Metrics structs
- extract_objective prefers GPU backtest fitness over val_loss when available
- All tests updated (101 passing)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- evaluate_ppo_fold_gpu(): loads PPO checkpoint, runs GPU backtest with
ppo_to_exposure_scores (45→5 collapse), uses GpuBacktestEvaluator
- PPO main loop: tries GPU path first when --gpu-eval (default), falls
back to CPU if CUDA unavailable or error
- PPOMetrics: backtest_sharpe/backtest_trades fields for GPU walk-forward
- PPOTrainer::run_gpu_backtest(): runs backtest on validation 20% split
- extract_objective(): prefers backtest_fitness when GPU results available,
falls back to -avg_episode_reward on non-CUDA builds
- 2 new tests: backtest fitness priority + few-trades penalty
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Wire GpuBacktestEvaluator into PPO hyperopt trials so the optimizer
ranks candidates by walk-forward Sharpe ratio instead of raw episode
reward. The PPO actor's 45-action softmax probabilities are collapsed
to 5 exposure scores via ppo_to_exposure_scores before the backtest
loop. When CUDA is unavailable or the backtest fails, the adapter
falls back to the original -avg_episode_reward objective.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Flip --gpu-eval default to true (--no-gpu-eval to opt out)
- Move actions_history scatter-write into env kernel (zero CPU accumulation)
- Remove done-flag periodic download (env kernel handles per-thread)
- Only GPU→CPU transfer is final metrics readback (n_windows × 40 bytes)
- Add spread cost discrepancy warning when GPU path is active
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Document known benign race on reset_flags (cross-block __threadfence
not being a grid-wide barrier) and writeback picking an arbitrary
episode's DSR/EMA as the representative seed. Remove dead reads for
epoch_port_value/pos/cash in both standard and warp-cooperative kernel
variants.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add --gpu-eval flag that routes DQN fold evaluation through
GpuBacktestEvaluator instead of the per-bar CPU path. The GPU path
uploads the full test fold as a single window, runs the env kernel
loop on-device, and downloads only the final WindowMetrics (10 floats).
Key design choices:
- GPU path uses greedy argmax (not hierarchical softmax); results differ
slightly from CPU path — this is documented and intentional
- Falls back to CPU path on any GPU error (no silent failures)
- Also adds --initial-capital flag needed by GpuBacktestConfig
- Fix pre-existing clippy::wildcard_enum_match_arm in
GpuBacktestEvaluator::new (Device::_ → explicit variants)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Adds crates/ml/tests/gpu_backtest_validation.rs with 6 deterministic
tests that validate GpuBacktestEvaluator produces reasonable metrics:
always-long on uptrend (positive PnL), always-long on downtrend
(negative PnL), always-flat (~zero PnL), multi-window ordering,
extended metrics finiteness/self-consistency, and trade count.
All tests are #[ignore] gated and skip gracefully on CPU-only machines
(CI passes with 6 ignored; intended for GPU development machines).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
