Remove pub state_dim field from DQNConfig and GpuReplayBufferConfig; remove the
state_dim field from GpuExperienceCollector. Replace all reads with
ml_core::state_layout::STATE_DIM (and STATE_DIM_PADDED for cuBLAS-padded
strides). Checkpoint loading now validates saved state_dim against the
constant and hard-errors on mismatch. GpuAttentionConfig.state_dim is a
distinct attention-feature dim and is left untouched.
Co-Authored-By: Claude Opus 4.7 (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>
Zero candle_core, candle_nn, or candle_optimisers references remain
in the entire ml crate source code. Workspace compiles clean with
0 errors and 0 clippy warnings.
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>
- 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>
Removes temporary benchmark comment. Docker changes from previous commit
will trigger image rebuilds. This run serves as warm-cache benchmark
after cold-cache run completes.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Temporary comment to trigger detect-changes for both services and
training compile steps. Will be removed after benchmarking.
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>
- Move memory_optimization/ (4.6K lines, 7 files) from ml to ml-core
- Move batch_size_resolver.rs to ml-core (now both deps in same crate)
- Add tempfile to ml-core dev-dependencies (qat tests)
- Re-export both modules from ml facade
- Keep checkpoint/ in ml (model_implementations.rs has model-specific deps)
185 ml-core tests + 2573 ml tests = 2758 total, 0 failures
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Move safety/ module (6.5K lines) from ml to ml-core (self-contained)
- Keep security/ in ml (depends on ensemble::EnsembleDecision)
- Move From<ProductionTrainingError> for MLSafetyError to ml (cross-crate)
- Rename Real-prefixed inference types to proper names:
RealInferenceError → InferenceError
RealInferenceConfig → InferenceConfig
RealPredictionResult → InferencePrediction
RealNeuralNetwork → NeuralNetwork
RealMLInferenceEngine → MLInferenceEngine
- Re-export safety from ml facade for backward compatibility
129 ml-core tests + 2629 ml tests = 2758 total, 0 failures
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Move optimizers, gradient_accumulation, gradient_utils, cuda_compat,
tensor_ops, and gpu (device config, capabilities, memory profiling) to
ml-core. These are shared compute primitives used by all models.
Also commit module files for core types (common, config, error, model,
traits, types) that were moved from ml to ml-core in task 5a but left
staged without being committed.
Notable changes:
- resolve_batch_size() stays in ml (new batch_size_resolver module)
because it depends on memory_optimization::auto_batch_size which
has not yet moved to ml-core
- FactoredAction legacy bridge converted from inherent impl to
extension trait (FactoredActionLegacy) since FactoredAction is now
defined in ml-core, not ml
- candle-optimisers added to ml-core dependencies (needed by Adam)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Move all inline type definitions from ml/src/lib.rs to ml-core:
MLError, MLResult, Trade, MarketRegime, HealthStatus, Features,
MLModel trait, ModelRegistry, ParallelExecutor, LatencyOptimizer,
TrainingMetrics, ValidationMetrics, InferenceResult, ModelMetadata.
Dedup: consolidate ConfigError{reason}/ConfigurationError(msg) into
single ConfigError(String) tuple variant (was 2 variants, 174 refs).
Cleanup: convert create_hft_* free functions to associated methods
(HFTPerformanceProfile::ultra_low_latency(), ParallelExecutor::hft()).
ml facade re-exports via `pub use ml_core::*`.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Move mixed_precision, xavier_init, portfolio_tracker, action_space,
order_router from dqn/ to ml/src/ root. Extract TradingAction enum
from dqn/agent.rs to standalone trading_action module. Re-export
from dqn/mod.rs for backward compatibility.
These modules are shared infrastructure used by PPO, trainers,
hyperopt, and ensemble — not DQN-specific.
Test results: 2757 passed, 0 failed, 25 ignored (unchanged).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Re-export HardwareTimestamp through data crate instead of ml/risk
depending directly on trading_engine. Reduces coupling between
the ML pipeline and the trading engine.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Extends TrafficSplitter with Thompson Sampling (Beta-distributed traffic
allocation) and PromotionCriteria for automated champion/challenger
decisions. 18 tests, 0 clippy.
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>
Split the build pipeline: one compile-services job builds all 8 service
binaries with PVC-backed sccache, saves as artifacts. Then 9 Kaniko jobs
just package pre-built binaries into slim runtime images (~30s each).
Before: 9 parallel Kaniko jobs each doing full cargo build --release
(~20min each, no sccache, 9x duplicated dep compilation)
After: 1 compile job with sccache (~5min cached) + 9 package jobs (~30s)
- Add compile stage between test and build
- Add Dockerfile.runtime (minimal debian + pre-built binary)
- Add Dockerfile.web-gateway-runtime (Node dashboard + pre-built binary)
- Keep Dockerfile.training via Kaniko (needs CUDA dev image for H100)
- Remove all SCCACHE_BUCKET build-args from service builds
- Use dir:// context for Kaniko (only sends build-out/ dir, not full repo)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Move 17 library crates into crates/, CLI binary into bin/fxt,
consolidate 10 test crates into testing/, split config crate
from deployment config files.
Root directory reduced from 38+ to ~17 directories.
All Cargo.toml paths and build.rs proto refs updated.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
