GPU experience collector:
- Add GpuHardwareInfo with SM count detection from device name lookup
(H100=132, A100=108, L40S=142, RTX 4090=128, etc.)
- optimal_n_episodes() scales to GPU: sm_count × 2 warps × 32 threads,
capped by 15% free VRAM budget, 256-aligned for block scheduling
- Remove hardcoded .min(256) cap in trainer — auto-scales from 128 to 8192
- MAX_EPISODES_LIMIT raised from 256 to 4096
Numerical stability:
- BF16 cross-entropy epsilon: 1e-8 → 1e-4 (below BF16 precision floor
1e-8 rounds to zero, making log-stabilization a no-op → -inf → NaN)
- Add log_probs.clamp(-20, 0) guard against -inf × 0 = NaN in loss
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
BF16's 7-bit mantissa overflows on exp(50+) in softmax → Inf → NaN.
Cast distributional logits to F32 before softmax in both dueling and
rainbow network heads, then cast back to original dtype.
Increase default batch_size 128→1024 (standard), 128→256 (conservative),
512→2048 (aggressive) to saturate H100 tensor cores. AutoBatchSizer
already caps to VRAM ceiling for smaller GPUs.
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>
Remove phantom foxhunt_training_total_epochs reference (never registered)
and replace with foxhunt_training_step from the recent metrics commit.
Add full coverage for all 52 Tier-1 Prometheus metrics across 5 new rows:
RL diagnostics (Q-overestimation, PPO entropy/KL/advantage), training
health (NaN/grad explosion/feature errors, checkpoint ops, data load
latency), epoch returns, supervised eval (accuracy/precision/recall/F1),
and hyperopt details (mode, trial epoch, failed trials).
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>
clip_grad_norm now returns a GPU-resident Tensor instead of (f64, f64),
keeping backward → clip → optimizer.step fully pipelined on GPU with
zero cuStreamSynchronize stalls. The unconditional multiply trick
(scale = min(max_norm/(norm+eps), 1.0)) avoids the conditional branch
that previously required reading the norm to CPU.
Key changes:
- gradient_utils::clip_grad_norm: return Tensor, unconditional GPU multiply
- Handle BF16 mixed-precision via per-gradient to_dtype cast
- adam.rs: backward_step_with_monitoring returns Tensor (zero sync)
- gradient_accumulation: delegate to gradient_utils (DRY, same GPU path)
- dqn.rs: single sync boundary after ALL GPU work queued
1746 tests passing (ml-core 286, ml-dqn 388, ml-ppo 198, ml 874).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
clip_grad_norm: accumulate squared norms on GPU-resident scalar, single
to_scalar() at end (was 16-20 per-param syncs per step × 2917 steps/epoch).
check_gradients_finite: same GPU-accumulation pattern, single sync.
gpu_replay_buffer sample_proportional/rank_based: generate random targets
via rand(0,1)*total_sum on GPU, normalize weights via broadcast_div
(eliminates 2 to_vec0 syncs per sample call).
gpu_replay_buffer update_priorities_gpu: replace CPU loop of 50 individual
slice_scatter calls with single batched index_add delta trick.
dqn NaN detection (every 500 steps): accumulate 3 NaN counts on GPU,
single to_scalar for total; detailed breakdown only if NaN found.
dqn dead neuron detection (every 1000 steps): accumulate dead count on
GPU-resident scalar (was to_vec0 per parameter tensor, ~16-20 syncs).
Net: ~22 GPU→CPU syncs + 50 micro-kernels per training step → 2 syncs.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Move Q-value stats, gradient norm, and training step from tracing::info!
(stdout-only, block-buffered, invisible mid-epoch) to Prometheus gauges
(atomic set, scrapable every 15s). Downgrade formatted step logs to
debug! level to eliminate allocation/serialization overhead in the hot path.
New gauge: foxhunt_training_step (current step within epoch)
Updated: foxhunt_training_q_value_mean/max, foxhunt_training_gradient_norm
now update every 500 steps instead of only at epoch boundaries.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Three fixes validated by 20/20 hyperopt trials on H100 (zero OOM):
1. Workspace default-features: ml-core, ml-dqn, ml-ppo, ml-supervised
workspace deps now have default-features=false. Prevents cudarc
(which requires nvcc) from leaking into CPU service builds via
Cargo feature unification. CI compile-services was failing with
"Failed to execute nvcc: No such file or directory" (exit 101).
2. BF16 comparison fix: Candle's gt()/le() don't support BF16 operands.
Cast ADX/CUSUM features to F32 before threshold comparison in
regime classification. Previous approach (cast threshold to BF16)
failed due to Candle broadcast_as reverting dtype.
3. CI pipeline: expand ML change detection to all 14 sub-crates,
add component:compile labels for sccache network policy matching,
bump training runtime to CUDA 12.6 + Ubuntu 24.04 (glibc 2.39).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
On H100 with BF16 mixed precision enabled, Candle's comparison ops (gt,
lt, le, ge) and arithmetic ops require matching dtypes. F32 threshold
constants compared against BF16 state tensors caused every training step
to fail with "dtype mismatch in cmp, lhs: BF16, rhs: F32".
Fixes:
- regime_conditional.rs: ADX/CUSUM thresholds cast to adx.dtype()
- quantile_regression.rs: Huber kappa, zero, and half tensors match
input dtype (both quantile_huber_loss variants)
- rainbow_network.rs: LeakyReLU slope and ELU alpha/one tensors cast
to x.dtype()
- continuous_ppo.rs: Huber delta/half tensors cast to abs_diff.dtype()
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Three root causes fixed:
1. GPU PER + experience collector (cudarc) created dual CUDA allocator
fragmentation on GPUs ≤8 GB, making training impossible even at
batch_size=1. Added `use_gpu_replay_buffer` config flag; both GPU PER
and experience collector now disabled when VRAM ≤8192 MB.
2. Search space bounds were WIDENED instead of capped — max_batch_size
returning 4096 replaced the original 512 upper bound, and
max_hidden_dim_base_full returning 3072 replaced the original 1024.
Fixed with min() to only narrow, never widen.
3. VRAM estimator assumed GPU features always active, overcharging when
they're disabled on small GPUs. Now conditional: when
replay_buffer_capacity=0 (proxy for GPU PER disabled), collector/cudarc
costs are zero and fragmentation multiplier drops from 3× to 1.5×.
Additional small-GPU guard: GPUs ≤8 GB get clamped search space
(batch≤128, hidden≤512, atoms≤51, buffer≤50K) to fit 3 regime heads
+ C51 + noisy nets + dueling in limited VRAM.
Validated: 7/7 trials complete on RTX 3050 Ti 4 GB, zero OOM, best trial
Sharpe 9.8 with 50.6% win rate. Previous runs had 100% OOM failure rate.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace `actions_tensor.to_vec1()` → CPU loop → `Tensor::from_vec` with
GPU-native `decompose_actions_batch_gpu` using F32 affine+floor arithmetic.
Removes the last `cudaDeviceSynchronize` stall in the branching training
hot path.
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>
training_dtype() now returns BF16 on all CUDA devices, enabling full
tensor-core utilization. F32 is used only at boundaries (scalar
extraction, loss computation, softmax). VRAM estimator updated to
account for BF16 byte sizes, C51/QR atoms, dueling streams, NoisyNet
param doubling, and GPU PER buffer pre-allocation.
Changes:
- mixed_precision.rs: training_dtype() returns BF16 on CUDA
- curiosity.rs: F32 cast before scalar extraction
- network.rs: F32 output at NetworkLayers forward boundary
- traits.rs: estimate_trial_vram_mb_full() with BF16-aware sizing
- dqn.rs adapter: uses full estimator with worst-case architecture
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>
Four monitoring functions (estimate_avg_q_value_with_early_stopping,
collect_qvalue_statistics, compute_q_gap_for_epoch, compute_per_action_q_values)
iterated over batch_sample.experiences to build CPU tensors for forward passes.
When GPU PER (GpuPrioritized) is active, experiences is always vec![] — all data
lives on GPU tensors in gpu_batch. This created zero-element tensors with non-zero
shapes, triggering CUBLAS_STATUS_INVALID_VALUE on the next forward pass.
Fix: all four functions now check for gpu_batch.states and use it directly,
falling back to CPU experiences only for non-GPU buffers. Also removes debug
eprintln probes, wires new_on_device for DQN/RegimeConditionalDQN construction,
and adds GPU-aware smoke tests (33 pass, 874 total, 0 failures).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Eliminates the CPU roundtrip in PER binary search: previously the
cumsum tensor (~400KB for 100K buffer) was downloaded to CPU, searched
in a loop, then indices uploaded back. Now a CUDA kernel runs one
thread per target with O(log n) binary search — zero DMA.
- Add searchsorted_kernel.cu (40-line CUDA binary search kernel)
- Implement SearchSorted as Candle CustomOp2 with CPU fallback
- Wire into sample_proportional() and sample_rank_based()
- Fix all clippy warnings in gpu_replay_buffer.rs (const fn, shadow,
doc backticks, to_owned, div_ceil, module_name_repetitions)
- Fix wildcard_enum_match_arm in mixed_precision.rs
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace expensive CPU↔GPU data transfers with GPU-native operations:
- check_gradients_finite: sum_all() scalar readback (4B per tensor)
replaces flatten_all()+to_vec1() that copied entire gradients to CPU
(up to 200MB per step across 9 call sites in DQN+PPO)
- Huber loss: affine() replaces 3× Tensor::from_vec(vec![const; N])
CPU Vec allocations in the inner loss computation loop
- update_priorities_gpu: per-index slice_scatter (~1KB DMA) replaces
full-buffer to_vec1()+from_vec() roundtrip (1.2MB DMA per step)
- PER sampling: single from_vec + GPU to_dtype replaces duplicate
from_vec calls and CPU type conversion roundtrips
- RegimeConditional: log warning on silent GPU batch drops
822 tests pass (350 ml-dqn, 274 ml-core, 198 ml-ppo), 0 clippy.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Three fixes for GPU PER hot path:
1. IQN quantile loss used empty CPU weights Vec instead of GPU-resident
weights_tensor_cached — caused CUDA_ERROR_ILLEGAL_ADDRESS from
uninitialized GPU memory. Now uses cached GPU tensor matching C51
and standard DQN paths.
2. GpuPrioritized add()/add_batch() replaced with StagedGpuBuffer:
add() stages on CPU (Vec::push, zero GPU ops), sample() batch-flushes
staging→GPU in one DMA before sampling. Production path (insert_batch_tensors)
bypasses staging entirely — GPU→GPU with zero CPU.
3. All 9 ML sub-crates default to cuda feature so `cargo test -p ml-dqn`
exercises GPU code paths on CUDA workstations. CI service crates use
default-features=false, unaffected.
Test results: 350 passed (was 343+7 failed), 0 failed, 1 ignored.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Wire GpuReplayBuffer into DQN constructor with OOM fallback to CPU PER
- Fix P0: GPU PER priorities never updated in single-batch train_step()
(result.indices was empty CPU Vec; GPU tensors td_errors_gpu/indices_gpu
were silently dropped)
- Fix IS-weight ordering: apply weights AFTER Huber loss, not before
(weighting before nonlinear Huber shifts quadratic/linear regime boundary)
- Fix IQN CVaR: add .contiguous() before sort_last_dim (341/341 tests pass)
- Defer loss scalar readback to after backward pass (piggyback on grad flush)
- Add next_states to ExperienceBatch with episode-aware shift computation
- Add insert_batch_tensors() for direct GPU tensor insertion into replay buffer
- Make Q-value estimation periodic (every 50 steps) to reduce forward passes
- Fix CPU fallback path types (u8 action, i32 fixed-point reward, timestamp)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Plan to reduce ml crate from 91K to ~12K LOC by extracting trainers
into model sub-crates, hyperopt adapters into ml-hyperopt, and
infrastructure into existing sub-crates. ml becomes an orchestration
layer owning inference, model factory, and training pipeline.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
ml-supervised, ml-ensemble, ml-labeling, ml-explainability, ml-hyperopt
all had default = ["cuda"] which pulled cudarc into the CPU services
build, causing compile-services to fail with "nvcc not found".
Changed all to default = [] and wired ml/Cargo.toml cuda feature to
propagate to all 8 sub-crates (was only 3: ml-core, ml-dqn, ml-ppo).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Adds a `source` template variable to the training dashboard that controls
which Prometheus job labels are queried:
- Live: only `training-pods` (running pods scraped directly)
- CI History: only `.*_baseline.*` (completed CI runs via pushgateway)
- All: both sources combined
All 37 panel queries now use `job=~"$source"` for consistent filtering.
Active Workers panel stays hardcoded to `job="training-pods"` since it
only makes sense for live pods. Template variable queries (model/fold
dropdowns) also respect the source selector.
Default is "Live" — dashboard shows only the currently running training
session with no pushgateway stale data contamination.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Stat panels (Active Workers, Current Epoch, Trial Progress, Elapsed, Best
Objective) showed conflicting values from pushgateway stale gauges overlapping
with live training pod metrics. Scoped all stat/gauge panels and template
variable queries to job="training-pods" so the dashboard reflects only the
currently running training session. Time-series panels remain unfiltered via
$model template variable scoping — dropdown only lists live models, so
historical pushgateway data is naturally excluded without explicit filtering.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Remove cuda from default features in ml-core, ml-dqn, ml-ppo, ml
- Propagate cuda feature from ml → ml-core/ml-dqn/ml-ppo
- CI compile-training already uses --features ml/cuda explicitly
- Fix MaxDD log format: {:.1}% → {:.3}% (was rounding 0.033% to 0.0%)
- Suppress unused_labels/unused_variables warnings for cfg(cuda) code
- Add CALLBACK_ENDPOINT env to ml-training-service deployment
- Fix Grafana active_workers query to use sum() with fallback
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Prometheus was configured to scrape pods via prometheus.io/scrape
annotations, but all services and training jobs used gitlab.com/
prefix from legacy GitLab-managed Prometheus — causing Prometheus
to never discover any foxhunt pods. This resulted in stale/missing
metrics on the Grafana training dashboard.
12 files updated across services/, gpu-overlays/, training/, and
monitoring/ (node-exporter, dcgm-exporter cleanup).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The nodeSelector pointed to non-existent 'ci-training' pool. The actual
Scaleway pool is 'ci-training-h100', matching the CI pipeline template.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
GPU kernel produced raw percentage PnL rewards (~3.5e-4) which were
~10,000x smaller than Q-values (~7.0), making rewards invisible in the
Bellman backup. Q-values froze at initialization.
Port the CPU RewardNormalizer algorithm (EMA mean/variance, z-score
normalization, [-3,3] clamp) directly into the CUDA kernel hot path
as per-thread register state. Each thread maintains its own running
mean/variance with configurable decay rate (reward_norm_alpha, default
0.01 = ~100-step window). EMA resets on episode boundaries.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
21 sub-crates extracted from the 260K-line ml monolith, reducing it to ~90K.
Deletes ~7.5K lines of dead code. Zero compilation errors, zero warnings.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Remove unused struct fields, dead methods, unreachable code across
ml-dqn, ml-supervised, ml-features, ml-ppo, ml-checkpoint, ml-labeling,
ml-observability, and ml-universe. Gate test-only infra behind #[cfg(test)].
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>
Move TFT, Mamba-2, Liquid, TGGN, TLOB, KAN, xLSTM, and Diffusion model
implementations to ml-supervised. Bridge files (UnifiedTrainable adapters,
Checkpointable impls) stay in ml. Delete AsyncDataLoader (replaced by
StreamingDbnLoader + simple .chunks() batching). Remove empty ml-infra
scaffold — the remaining ml modules are too tightly coupled for clean
extraction, so ml stays as the orchestration facade.
- ml-supervised: 234 tests, 0 failures
- ml: 1687 tests, 0 failures
- Workspace: 0 compilation errors
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Move 24 PPO source files + flow_policy/ from ml into standalone
ml-ppo crate. The ml crate's ppo module is now a thin re-export layer
(`pub use ml_ppo::*`) plus two bridge files (trainable_adapter.rs,
stress_testing.rs) that depend on ml-internal types.
Key changes:
- ml-ppo: 25 modules (incl flow_policy subdir), 198 tests, standalone
- ml: depends on ml-ppo, re-exports via ppo/mod.rs
- Import rewrites: crate::common::action → ml_core::action_space,
crate::dqn::{mixed_precision,xavier_init} → ml_core::*,
crate::gradient_accumulation → ml_core::gradient_accumulation
- ml tests: 1929 pass (down from 2127 — 198 moved to ml-ppo)
- Workspace: 0 errors
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Move 53 DQN source files + gpu_replay_buffer from ml into standalone
ml-dqn crate. The ml crate's dqn module is now a thin re-export layer
(`pub use ml_dqn::*`) plus two bridge files (trainable_adapter.rs,
stress_testing.rs) that depend on ml-internal types.
Key changes:
- ml-dqn: 45 modules, 334 tests, compiles standalone
- ml: depends on ml-dqn, re-exports via dqn/mod.rs
- DQN.config: pub(crate) → pub for cross-crate access
- DQNAgent checkpoint methods moved into ml-dqn
- gpu_replay_buffer moved from cuda_pipeline/ to ml-dqn
- 8 dead-code files removed (never declared in mod.rs)
Net: -30,648 lines from ml crate. Workspace: 0 errors.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Move Sharpe ratio metrics and SIMD performance module to ml-core.
These only depend on MLError which is already in ml-core.
Add approx = "0.5" to ml-core dev-dependencies for Sharpe tests.
Skip observability/ (needs prometheus dep — stays in ml).
Test counts: 271 ml-core + 2487 ml = 2758 total, 0 failures
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Move 6 shared modules (~2.3K lines) from ml to ml-core:
- trading_action.rs (TradingAction enum)
- action_space.rs (action masking, re-exports)
- xavier_init.rs (Xavier/Glorot weight initialization)
- mixed_precision.rs (AMP utilities, FP16/BF16)
- order_router.rs (deterministic order routing)
- portfolio_tracker.rs (portfolio state tracking)
With TradingAction now in ml-core alongside FactoredAction, the
FactoredActionExt extension trait is eliminated entirely —
from_trading_action()/to_trading_action() become inherent methods
on FactoredAction. This removes the need for `use FactoredActionExt`
imports in reward.rs, gae.rs, and trajectories.rs.
Test counts: 250 ml-core + 2508 ml = 2758 total, 0 failures
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
