Noisy nets are now always on (mandatory feature since config unification).
Epsilon start always uses the noisy-nets-aware default (0.05).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Remove use_double_dqn, use_dueling, use_per, use_branching,
use_distributional, use_noisy_nets, use_huber_loss, and use_cql
from DQNConfig, DQNHyperparameters, and DqnParams structs.
These features are always enabled (Rainbow DQN standard). The boolean
flags were dead code — every constructor set them to true, and the
only code paths that set them to false were in tests that disabled
features for simplicity. With the fields removed, the features are
unconditionally active, eliminating ~490 lines of dead configuration.
Key changes:
- Struct field declarations removed from 3 core config structs
- Conditional branches (if use_X { ... } else { ... }) simplified:
dueling/branching/PER network creation is now unconditional
- Checkpoint metadata hardcodes "true" for backward compatibility
- Hyperopt search space index 11 (use_branching) fixed at 1.0
- TOML/YAML config files cleaned of removed fields
- Tests that toggled these flags updated or rewritten
45 files changed, -487 net lines. Zero new test failures.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
use_qr_dqn was a hacky toggle that gated the IQN dual-head behind
a num_atoms threshold. IQN is now always enabled when iqn_lambda > 0
(default 0.25). C51 remains the main loss; IQN is the auxiliary head
for CVaR risk quantification.
Removed use_qr_dqn from: DQNParams, DQNHyperparameters, fused_training,
hyperopt adapter, all tests, all examples.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
evaluate_dqn/evaluate_dqn_graphed now take (weights, branching_weights,
DqnBacktestConfig) instead of (weights, network_dims). This was the
compile error breaking all H100 CI runs.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Two phases only: fast (default) and full. No legacy 31D single-phase
search — it's a dead path that was never the right choice.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Created config/gpu/{default,rtx3050,h100,a100}.toml with all GPU-specific
parameters: batch_size, num_atoms, buffer_size, hidden_dim_base,
replay_buffer_vram_fraction, gpu_n_episodes, gpu_timesteps_per_episode,
cuda_stack_bytes.
GpuProfile::load() auto-detects GPU by device name, falls back to
embedded defaults (include_str!). Override via FOXHUNT_GPU_PROFILE env.
Removed dead code:
- detect_vram_mb(), vram_scaled_hidden_dims(), vram_scaled_base_dim(),
resolve_hidden_dim_base() + 18 tests for these functions
All callers updated: train_baseline_rl, DQNTrainer constructor,
PPO trainer, smoke tests, pipeline tests.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Replace scattered VRAM-based if/else chains with a declarative TOML profile
system. GPU profiles (rtx3050, a100, h100, default) are selected by device
name and embedded at compile time via include_str! for zero-filesystem
fallback in CI/containers, with filesystem and env var overrides.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- DQN CLI --max-steps-per-epoch was only wired to PPO (bug: 1394 steps ran instead of 10)
- Added per-substep timing: sample/fused/guard breakdown per epoch
- Phase 1a results: PER sampling now 0.1ms/step (was ~100ms with CPU roundtrips)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- GpuTrainResult::gpu_tensor_to_cuda_slice: implement via CudaSlice::clone()
(was TODO returning hard error, blocking training guard loss readback)
- DQNAgentType::primary_dqn_mut(): new method returning &mut DQN for both
Standard and RegimeConditional variants
- FusedTrainingCtx: replace as_standard_mut() with primary_dqn_mut() at
all 3 call sites (EMA target update, IQN EMA, VarStore sync)
- fused_post_step/fused_post_step_no_ema/fused_post_step_gpu_bookkeeping:
delegate to primary_dqn_mut() instead of returning error for RegimeConditional
- train_baseline_rl: add error chain logging for fold failures
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Query CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN at runtime
instead of name-based heuristics for shared memory tile sizing
- Cap shmem at 48KB on consumer GPUs (< 164KB hardware max) — A100/H100
with 164KB+ use full opt-in, RTX 3050/4090 use safe 48KB default
- Dynamic C51 atom scaling: <8GB→11, <16GB→21, >=16GB→51 atoms
- Dynamic CUDA stack: 16KB for 11 atoms, 32KB for 21, 64KB for 51
- VRAM-aware GPU experience episodes: 16 episodes on <8GB (was 256)
- Bypass CUDA Graph when shmem > 48KB (direct kernel launch fallback)
- Remove .max(51) on num_atoms_max — use actual configured atom count
- Shared query_max_shmem_bytes() used by both trainer and collector
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The fused DQN training kernel allocates DIST_SIZE(HIDDEN)*NUM_ATOMS
per-thread arrays on CUDA stack. With 51 atoms this needs ~52KB/thread
which exceeds stack limits on RTX 3050 (4GB). Scale atoms dynamically:
<8GB→11, <16GB→21, >=16GB→51 (matches smoke_test_real_data pattern).
Note: experience collector still compiles with atoms_max=51 hardcoded
(separate issue — needs collector kernel dim parameterization).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Final cleanup:
- 61 test files + 5 example files: candle imports replaced
- 8 testing/integration files: migrated to cudarc/ml-core types
- 3 services/trading_service test files: migrated
- Root Cargo.toml: candle-core, candle-nn removed from [workspace.dependencies]
- crates/ml/Cargo.toml: candle-nn dependency removed
- testing/e2e/Cargo.toml: candle-core dependency removed
Zero active candle_core/candle_nn/candle_optimisers code references remain.
Zero candle dependency declarations in any Cargo.toml.
Remaining "candle" strings are exclusively in doc comments.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Update all callers to match the new pure-cudarc APIs introduced by the
hive agent CudaSlice migration. Key changes:
- GpuTrainingGuard::new() now takes Arc<CudaStream>; callers use from_device()
- check_and_accumulate/qvalue_stats/qvalue_divergence take &CudaSlice<f32>
instead of &Tensor; callers convert via tensor_to_cuda_slice_f32()
- accumulate_q_value takes f32 scalar, returns () (no Result)
- GpuReplayBuffer::insert_batch gains batch_size arg, takes CudaSlice params
- signal_adapter functions take &Arc<CudaStream> (cudarc 0.17 Arc requirement)
- Add tensor_to_cuda_slice_u32() and cuda_f32_to_tensor() utility functions
- Replace CudaView usage with owned CudaSlice via tensor_to_cuda_slice_f32()
- Fix CudaStorage.device field access (was method call in older API)
- Fix borrow-after-move in copy_actions_out via scoped DtoD copy
Zero errors, zero warnings across lib + tests + examples + full workspace.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Eliminate all 26 Candle Tensor references from signal_adapter.rs.
Three functions (ppo_to_exposure_scores, signal_to_action_scores,
tft_quantile_to_signal) now take CudaSlice<f32> + CudaStream and
return CudaSlice<f32>, backed by three fused CUDA kernels in
signal_adapter_kernel.cu. Dead code evaluate_supervised_gpu_backtest
removed (zero callers). Added cuda_f32_to_tensor helper to
gpu_action_selector for DtoD copy back to Candle Tensor at API
boundaries (PPO hyperopt adapter, evaluate_baseline example).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Root cause: shmem_max_in_dim only included trunk dims (state_dim,
shared_h1, shared_h2) but not head dims (value_h, adv_h). When
hidden_dim_base=32 made the trunk narrow while heads stayed at 128,
the BF16 weight tile for branch output (255×128=32640 BF16 elements)
overflowed the shared memory region (12288 BF16 elements). On H100
the overflow landed in unused-but-mapped hardware shmem (silent
corruption). On RTX 3050 (48KB physical shmem) it hit unmapped
memory → CUDA_ERROR_ILLEGAL_ADDRESS.
Changes:
- gpu_dqn_trainer.rs: shmem_max_in_dim includes value_h/adv_h
- Remove all #[ignore] from smoke tests (feature_coverage,
training_stability, gpu_residency)
- Smoke tests use real .dbn data from test_data/ (hard error if missing)
- Remove synthetic_data() fallback — no fake data in tests
- GPU-direct DtoD training path (train_step_gpu, FusedTrainScalars)
- GPU-native PER priority update kernel (zero CPU readback)
- IQN dual-head integration (gpu_iqn_head.rs)
- BF16 dtype fixes across 6 model adapters
- Hyperopt 30D→31D (iqn_lambda)
- portfolio_transformer: unconditional BF16 (remove dead CPU branches)
- liquid/adapter: all tests use Cuda(0) directly
- Fix pre-existing gpu_kernel_parity_test.rs (stale args)
- Fix pre-existing evaluate_baseline.rs (removed fields)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Remove ALL #[cfg(feature = "cuda")] guards (~400+ occurrences)
- Remove ALL #[cfg_attr(not(feature = "cuda"), ignore)] test annotations (~250)
- Make cuda default feature in 9 ML crates (ml, ml-core, ml-dqn, ml-ppo, etc.)
- Convert nvrtc JIT compilation to precompiled nvcc (searchsorted, prefix_sum)
- Move compile_ptx_for_device() to ml-core for shared access
- Delete dead CPU code: multi_step.rs, self_supervised_pretraining.rs,
training_guard_gpu_tests.rs, CPU PER buffer paths, CPU Q-diagnostics
- Replace unwrap_or(Device::Cpu) with hard errors everywhere
- Remove dead is_cuda() else branches in DQN/PPO/hyperopt trainers
- Change config defaults from "cpu" to "cuda" (rainbow, tlob, pipeline)
- Port IQL value network to GPU kernel (5 CUDA entry points)
- Port HER goal relabeling to GPU kernel (warp-per-sample)
- Wire DSR GPU-to-CPU sync in training loop
- cfg!(feature = "cuda") → true in inference_validator
Zero warnings, zero errors across entire workspace.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Delete every CPU fallback block across 14 files (-286 lines):
- dqn.rs: CPU replay buffer, tensor construction, PER fallback, gradient paths
- hyperopt/adapters/ppo.rs: CPU curiosity modules, trajectory generation
- hyperopt/adapters/dqn.rs: CPU backtest fallback, sync no-op
- trainers/ppo.rs: CPU training error stub
- l2_cache.rs: CPU stub functions (gate callers behind cuda too)
- replay_buffer_type.rs: CPU is_gpu_prioritized fallback
- validation/harness.rs: CPU regime breakdown fallback
- build.rs: cpu_only_build cfg (never referenced)
- evaluate_baseline.rs: CPU gpu_handled fallback
- testing/integration/gpu: CPU test stubs
Zero #[cfg(not(feature = "cuda"))] remains in the codebase.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Remove ~850 lines of unreachable CPU fallback code from 3 hot-path files:
- hyperopt/dqn.rs: delete 250-line CPU backtest path (GPU eval mandatory)
- evaluate_baseline.rs: delete CPU PPO eval + non-CUDA fallback (147 lines)
- trainers/ppo.rs: delete 6 dead CPU rollout methods (~370 lines),
split train() into dispatcher + #[cfg(feature = "cuda")] train_gpu()
All .to_vec1() GPU hot-path guard violations eliminated.
GPU failures are now hard errors, not silent CPU fallbacks.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Cast input to weight dtype in DQN residual, rmsnorm, noisy_layers
- Set use_gpu=true in QNetworkConfig defaults and all config sites
- Resolve BF16 boundary mismatches in attention, curiosity, branching,
distributional_dueling across ml-dqn
- GPU-resident regime ops with BF16 boundary casts, eliminate .expect() in CUDA paths
- Eliminate all Device::Cpu fallbacks — GPU-only across 10 ML crates
- PPO: cast logits to F32 before softmax, cast batch tensors to training dtype
- Gradient collapse detection for RegimeConditionalDQN
- Wire halt_grad_collapse from CUDA guard kernel to halt training
- Dead neuron detection uses active network VarMap + squeeze factored readback
- Increment gradient_logging_step in GPU PER path
- Gradient collapse warmup guards use original buffer_size
- Cap training steps per epoch + tracing migration
- Replace Tensor::all() with sum_all() for pinned Candle compatibility
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace closure-based evaluate() with evaluate_dqn_graphed() for non-OFI
walk-forward backtest path. Extracts DuelingWeightSet from VarMap (branching
or standard dueling) and runs hand-written warp-cooperative CUDA forward
kernel with CUDA Graph capture — zero Candle dispatch overhead per step.
Key changes:
- GpuBacktestEvaluator::stream() getter for weight extraction on eval stream
- DQNAgentType::is_using_branching() / network_dims() for CUDA kernel config
- Hyperopt evaluate_gpu() non-OFI path: extract_dueling_weights_branching()
→ evaluate_dqn_graphed() (CUDA Graph accelerated)
- OFI path: retains Candle closure for state permutation (gather kernel
layout mismatch — future CUDA permutation kernel)
- 66+ GPU hot-path violations hardened to hard errors across DQN/PPO/supervised
- Stripped all gpu-ok suppression comments
- Proper #[cfg(feature = "cuda")] gating for CUDA-only code paths
77 files, 0 errors, 0 warnings across workspace.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The hyperopt binary was crashing with "trials (5) must be greater than
n_initial (5)" when --trials 0 was passed. Root cause: the bump logic
set trials = n_initial instead of max(5, n_initial + 1).
Fix: both hyperopt_baseline_rl and hyperopt_baseline_supervised now clamp
trials to max(5, n_initial + 1). Also add `when` condition to the
compile-and-train DAG so hyperopt step is skipped when trials=0, and
train-best gracefully handles missing hyperopt results.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
When trials=0 (or any value <= n_initial), both RL and supervised
hyperopt binaries now auto-bump to n_initial+1 instead of bailing.
Previously the RL binary bumped to 5 which equalled n_initial=5,
triggering "trials must be greater than n_initial" error. The
supervised binary lacked the bump entirely and just crashed.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The GpuBacktestConfig hardcoded max_position=1.0 with a 2× leverage cap,
reducing effective position to 0.2026 contracts on ES at $35K capital.
Training uses max_position_absolute from PSO params (1.0-4.0 contracts)
with no leverage cap — a 5.4× mismatch that makes transaction costs
overwhelm any alpha in walk-forward evaluation (0% win rate, -688% return).
Fix: Pass max_position_absolute through evaluate_gpu() and disable
leverage cap (max_leverage=0) to match training conditions. Same fix
applied to evaluate_baseline.rs via --max-position CLI arg.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
All three GPU evaluation functions (DQN, PPO, supervised) computed
market_feature_dim = args.feature_dim - 3, which could be >42 when
OFI is enabled. Since extract_ml_features produces [f64; 42], the
feature vectors have exactly 42 market features. Using a larger dim
caused the gather kernel to place live portfolio at the wrong index.
Hardcode market_feature_dim = 42 to match the actual feature extraction output.
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>
- 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>
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>
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>
- 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>
- 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>
- 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>
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>
- DQN hyperopt adapter: remove static VRAM gate for GPU experience
collector and GPU PER — dynamic scaling handles constraints at
runtime with graceful CPU fallback on init failure
- Remove is_parquet_file branching from hyperopt eval path (all data
loads via DBN pipeline now)
- Update training example CLI args for consistency
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Expand FeatureVector from 40 to 42 dimensions by including ADX(14) at
index 40 and CUSUM direction at index 41 from the existing CPU feature
extraction pipeline. This eliminates proxy-based regime classification
and enables GPU-native regime detection via tensor narrow/comparison ops.
Key changes:
- extraction.rs: wire RegimeADXFeatures + RegimeCUSUMFeatures into
extract_current_features_v2(), output 42 features per bar
- regime_conditional.rs: classify_regime_masks_gpu() creates per-regime
mask tensors entirely on GPU (ADX > 0.25 = trending, |CUSUM| > 0.7 =
volatile, else ranging). Zero CPU roundtrip in training hot path.
- trainer.rs/config.rs: state_dim 43→45 (no OFI), 51→53 (with OFI),
aligned dims unchanged (48/56). GPU batch insertion for all 3 heads.
- CUDA header: MARKET_DIM 40→42
- walk_forward.rs: FEATURE_DIM 40→42
- 42 files updated, all [f64;40]→[f64;42] propagated across workspace
Test results: ml=874/0, ml-dqn=354/0, ml-features=282/0, ml-core=274/0
Real data GPU smoke tests: 7/7 passed (OHLCV + OFI + trade enrichment)
Hyperopt baseline RL: 2 trials completed on local RTX 3050 Ti
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Move UnifiedTrainable trait, TrainingMetrics, CheckpointMetadata, and
checkpoint helpers from ml to ml-core (zero new dependencies — ml-core
already had candle-core + serde_json)
- Wrap Mamba2SSM in Mamba2TrainableAdapter to satisfy orphan rule (trait
in ml-core, type in ml-supervised — all other 9 models already used
wrapper pattern)
- Make Mamba2SSM::validate() pub for cross-crate adapter access
- Delete 5 permanently disabled deployment modules (cfg(any()) — never
compiled): registry, hot_swap, validation, monitoring, endpoints
(-5,842 lines)
- Delete 2 undeclared dead files in training/: dqn_trainer.rs,
transformer_trainer.rs (-138 lines)
- Fix pre-existing compute_loss test shape mismatch in mamba adapter
UnifiedTrainable in ml-core unblocks future trainers/ extraction (17.8K
lines) since model-specific trainers can now depend on ml-core for the
trait without pulling in the full ml monolith.
14 files changed, +128 -6,497 (net -6,369 lines)
Tests: 274 ml-core + 948 ml = 1,222 passed, 0 failed
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add QuestDB ILP sink for training metrics, update Prometheus scrape
configs, and fix network policies for monitoring stack connectivity.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add --mbp10-data-dir and --trades-data-dir CLI args to
hyperopt_baseline_rl binary so hyperopt trials can use real
order book and trade data for VPIN/Kyle's Lambda features.
- DQNTrainer: add mbp10_data_dir/trades_data_dir fields + with_ofi_data_dirs() builder
- DQNHyperparameters: pipe through from trainer instead of hardcoded None
- download-trades-job: fix nodeSelector to ci-compile-cpu (platform pool full)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
