- 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>
The GPU experience collector gate checked only `dqn.dueling_q_network`
(plain dueling), but with both `use_dueling: true` AND `use_distributional: true`
(the defaults), DQN creates hybrid `dist_dueling_q_network` instead, leaving
the plain dueling fields as None. This meant the GPU collector never initialized
despite curiosity being enabled.
Fix: Add else-if fallback to check `dist_dueling_q_network`/`dist_dueling_target_network`
when plain dueling fields are None. Same fix applied to the weight sync site.
Also fix test_train_with_empty_data_completes_gracefully: reduce to 5 epochs with
early stopping disabled. The debug-mode async state machine is large enough that
empty-data epochs run ~180ms each (vs ~3ms in release), triggering both plateau
and patience-based early stopping. The test purpose is crash-freedom, not timing.
2497 tests pass, 0 clippy warnings.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Wire monitoring_service gRPC into web-gateway: proto compilation,
config/state/client plumbing, REST endpoint at /api/monitoring/live-metrics,
and a 3s polling bridge that broadcasts training_progress over WebSocket.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Enable curiosity module by default (weight 0.0→0.1) to satisfy the
three-way gate (dueling + target + curiosity) that was blocking the
GPU experience collector CUDA kernel. This eliminates the 30-40% CPU
experience collection phase that was the main GPU idle bottleneck.
Additional changes:
- BatchSample API: train_step/compute_gradients now accept
Option<BatchSample> instead of Option<Vec<Experience>>, preserving
PER importance-sampling weights and indices through pre-sampling.
- Async PER pre-sampling: train_step_single_batch and
train_step_with_accumulation now pre-sample from the replay buffer
using a READ lock before acquiring the WRITE lock for GPU training.
This separates CPU sampling (~250μs) from GPU forward/backward (~3ms).
- Delete dead EpochPrefetcher: the binary (train_baseline_rl.rs) already
implements fold prefetching with background thread + mpsc channel +
GPU double-buffering, making the trainer's EpochPrefetcher redundant.
- Hyperopt DQN bounds: curiosity_weight min 0.0→0.01 so PSO can never
fully disable curiosity (which would re-gate the GPU collector).
10 files changed, -195 net lines. 2497 tests pass, 0 clippy warnings.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The auto-detect heuristic used cpus/2 ("smart cap"), designed for
CPU-bound workloads. DQN/PPO trials are GPU-bound — each rayon
thread submits CUDA kernels and waits on cudaDeviceSynchronize(),
using minimal CPU. cpus-1 is the correct cap.
On L40S-1-48G (8 vCPU): 3 threads → 7 threads (2.3× more trials).
Also bumps CI CPU limit 7500m→8000m to expose all 8 cores.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The batch span processor needs a tokio runtime for gRPC transport and
periodic flush. Async services already have one via #[tokio::main], but
sync training binaries (hyperopt, train, evaluate) don't.
Previous approach (making binaries async with #[tokio::main]) caused
"Cannot start a runtime from within a runtime" panics because the ML
crate's internal code creates its own tokio runtimes for block_on().
New approach: build_otel_tracer() detects runtime context via
Handle::try_current(). If absent, it creates a dedicated 1-worker
multi-thread runtime stored in a process-lifetime OnceLock. The worker
thread actively polls the OTLP batch export task.
Reverts training binaries to sync fn main() so internal runtime creation
(hyperopt adapters, DQN/PPO trainers) continues working as before.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
All 6 training binaries (hyperopt_baseline_rl, hyperopt_baseline_supervised,
train_baseline_rl, train_baseline_supervised, evaluate_baseline,
evaluate_supervised) used sync fn main() but the OTLP batch exporter
requires a tokio runtime (tonic/hyper-util gRPC transport). This caused
an immediate panic on CI when OTEL_EXPORTER_OTLP_ENDPOINT was set.
Fix: #[tokio::main(flavor = "current_thread")] on all 6 binaries.
Also fix pre-existing clippy warnings (shadow, let_underscore_must_use,
doc_markdown, cognitive_complexity, integer_division, unsafe_code).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace CPU-side argmax (to_vec1 + iter enumerate max_by) with
GPU-native argmax(D). Single u32 scalar extraction instead of
full Q-value vector transfer.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace &Device::Cpu with &self.device for input and target tensor
creation in Mamba2 hyperopt adapter. Avoids unnecessary CPU→GPU
transfer during hyperparameter optimization.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Stream-aware ensemble that runs models on separate CUDA streams
for true GPU-level parallelism. Falls back to rayon on CPU.
Uses CudaStreamPool for synchronization.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
CUDA stream pool with CPU no-op fallback. Foundation for
StreamAwareEnsemble that runs models on separate CUDA streams.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Use predict_raw() to collect raw GPU tensors from adapters. Stack,
sigmoid, weighted-sum on GPU before single extraction. Falls back
to CPU path for adapters without tensor output.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Override predict_raw() in TGGN, TLOB, KAN, xLSTM, Diffusion adapters
to return raw GPU tensors. Enables GPU-side ensemble aggregation
instead of per-model CPU extraction.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace clamped.to_vec1() CPU loop with GPU-native floor/ceil/frac
operations. Removes 3 tensor transfers per forward pass (N floats
down + 3*N up). All index computation now stays on GPU.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Stack var_returns, var_residuals, mean_reward, var_reward into single
tensor before extraction. Uses broadcast_sub for GPU-native mean
centering instead of scalar round-trip.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Backward-compatible trait extension. Default predict_raw() wraps
predict() result with tensor: None. Adapters can override to return
raw GPU tensors for GPU-side ensemble aggregation.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace per-param gradient norm extraction loop with batched
Tensor::stack pattern. Single GPU→CPU sync instead of one per
parameter tensor.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace per-param calculate_gradient_norm() loop with batched
Tensor::stack pattern. Single GPU→CPU sync instead of one per
parameter tensor.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace per-batch loss.to_vec0() in TFT training/validation loops
with GPU tensor accumulation. Single extraction per epoch + NaN guard
every 100 batches.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace per-batch .to_dtype(F64).to_scalar() in Liquid train/validate
with GPU tensor accumulation. Single extraction per epoch + NaN guard
every 100 batches. Removes ~100-500 GPU→CPU syncs per epoch.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace per-batch loss.to_scalar() in TLOB train_epoch/validate_epoch
with GPU tensor accumulation. Single extraction per epoch + NaN guard
every 100 batches.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Both DQN and PPO eval paths used old 3-action index matching (0=Buy,
1=Sell, 2=Hold). Now uses FactoredAction.target_exposure() for
exposure-weighted returns and order-type-specific transaction costs.
PPO path had .to_int() which doesn't exist on FactoredAction.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
compute_reward_pnl() took raw action_idx (0=Buy,1=Sell,2=Hold) — wrong
with 45-action FactoredAction encoding. Now takes &FactoredAction and
uses order-type-specific transaction costs. Position tracking uses
action.exposure instead of old 3-way index match.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Hyperopt adapter and PPO benchmark still had num_actions: 3, which would
produce misconfigured PPO models when used with the 45-action FactoredAction
sampling path. Found by spec compliance review.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Use a purely linear network (no ReLU) for the completeness axiom test.
IG on linear functions is mathematically exact, so the test is
deterministic regardless of random weight initialization. Tighten
tolerance from 20% to 1% (f32 rounding only). Keep ReLU network in
the basic test for non-linear verification.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- trading_service: PPO predict() used TradingAction match but act() now
returns FactoredAction. Use target_exposure() mapped to 0-1 range.
- IG completeness axiom test: relax tolerance from 5% to 20% (random
weights with ReLU non-linearity and trapezoidal rule can exceed 5%).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Was using direction=idx/15 (3 groups of 15: Buy/Sell/Hold) — incompatible
with DQN's FactoredAction encoding. Now uses exposure_idx=idx/9 (5 groups
of 9: Short100/Short50/Flat/Long50/Long100) matching FactoredAction.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
sample_action(), act(), act_with_log_prob(), greedy_action() now return
FactoredAction instead of TradingAction. Fixes the architectural disconnect
where num_actions=45 output neurons were sampled through a 3-action bottleneck.
TrajectoryStep.action and TrajectoryBatch.actions now use FactoredAction.
Added FactoredAction::from_legacy() for backward compatibility in tests.
Updated all PPO consumers: trainers/ppo.rs, hyperopt/adapters/ppo.rs,
validation/ppo_adapter.rs, benchmark/ppo_benchmark.rs.
2487 tests pass, 0 clippy warnings.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Consolidates ExposureLevel, Urgency, FactoredAction from dqn/action_space.rs
and ppo/factored_action.rs into common/action.rs. Both dqn:: and ppo::
re-export for backward compatibility. Deletes ppo/factored_action.rs.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
calculate_feature_importance() returned hardcoded fabricated scores with
wrong feature names on every inference. Replaced with honest empty map.
Real importance is computed on-demand via integrated gradients.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Implements IntegratedGradients using Candle autograd (Var::from_tensor +
backward). Computes attributions by integrating input gradients along
interpolation path from baseline to input.
Verified via completeness axiom test: sum(attributions) ≈ F(x) - F(baseline).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The real_ prefix was misleading — there is no fake data loader.
Mechanical rename across 18 source files, no logic changes.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- DQN PER: defer td_errors to_vec1() after loss.to_scalar() — piggyback on
existing pipeline flush instead of forcing premature GPU→CPU stall
- PPO trajectories: capacity-hint Vec allocations, extend_flat_states methods,
states_flat field on TrajectoryBatch for zero-copy GPU upload
- TGGN validate(): batch N per-sample losses on GPU → single to_scalar() sync
(was N GPU→CPU syncs)
- Liquid backward(): batch grad-norm per-param sqr().sum_all() on GPU → single
to_scalar() sync (was N GPU→CPU syncs per optimizer step)
- Liquid validate(): same N→1 GPU sync reduction as TGGN
- DQN trainer: restore EpochPrefetcher/DoubleBufferedLoader API (wrongly deleted)
- train_baseline_rl: wire DoubleBuffer GPU pre-upload — after CPU prefetch
completes, immediately upload next fold to GPU via DqnGpuData::upload() so
next fold starts with data already resident on GPU
2478 tests pass, 0 clippy warnings, 0 compile errors.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
DQN and PPO trainers now resolve hidden_dim_base from GPU VRAM when no
explicit override is given, so L4/L40S/H100 GPUs use proportionally
larger networks instead of being stuck at RTX 3050 Ti defaults (256).
- Add resolve_hidden_dim_base() tiered lookup (256/512/768/1024 by VRAM)
- Add network_param_count() for accurate model size estimation
- DQN: pre-compute hidden dims, use real param count for batch sizing
- PPO: add hidden_dim_base field, VRAM resolution in PpoTrainer::new()
- PPO hyperopt: raise hidden_dim_base ceiling from 2048 to 4096
- TFT hyperopt: expand hidden_sizes from [128,256,512] to 5 tiers
- Update stale estimates (DQN 50K→200K, PPO 100K→400K params)
- Fix pre-existing clippy lints in prefetch.rs and ppo.rs
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Emit set_eval_metrics (directional_accuracy, sharpe, profit_factor, return)
per fold for supervised model evaluation. Start metrics server on :9094.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Emit set_eval_metrics (win_rate, sharpe, profit_factor, return) per fold
for DQN/PPO evaluation. Start metrics server on :9094. Feeds training
cockpit Grafana dashboard eval panels.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace tracing_subscriber::fmt() with init_observability() which adds
JSON structured logging + optional OTLP export to Tempo. When
OTEL_EXPORTER_OTLP_ENDPOINT env var is unset, falls back to fmt-only.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Emit set_epoch, set_epoch_loss, set_validation_loss, set_iteration_seconds
after each DQN/PPO fold completes. Feeds training cockpit Grafana dashboard.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Remove unnecessary async from init_observability -- body was fully sync.
Change otlp_endpoint from &str to Option<&str> -- when None, OTLP layer
is skipped (fmt-only mode). Update all 8 service callers.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
