CUDA kernels: 36 of 37 compiled with __nv_bfloat16* (dt_kernels.cu remaining)
Rust types: ALL CudaSlice<f32> → CudaSlice<half::bf16> across ml + ml-core
Build: all kernels now compiled with common_device_functions.cuh (BF16 helpers)
BF16 math wrappers: bf16_sqrt, bf16_log, bf16_exp, bf16_pow, bf16_fabs, bf16_fmax,
bf16_fmin, bf16_cos, bf16_zero, bf16_one, bf16(), atomicAddBF16
NOT YET COMPILING — ml-core boundary errors (Vec<f32> → Vec<half::bf16>)
and dt_kernels.cu float*__nv_bfloat16 ambiguity remain.
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>
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>
Replace Candle's backward()/GradStore/VarMap/VarBuilder system with CUDA-native
primitives in ml-core::cuda_autograd. This eliminates the Candle dispatch overhead
(~2100 kernel launches per batch) for models that adopt the new API.
Components:
- GpuTensor: CudaSlice<f32> wrapper with shape metadata (replaces candle Tensor)
- GpuVarStore: named parameter store with flatten/unflatten (replaces VarMap/VarBuilder)
- GpuLinear: cuBLAS sgemm forward + manual backward (replaces candle_nn::Linear)
- GpuAdamW: per-parameter CUDA kernel optimizer (replaces candle_optimisers::Adam)
- ActivationKernels: ReLU/LeakyReLU/GELU/Sigmoid/Tanh forward+backward CUDA kernels
- LossKernels: MSE/Huber loss with fused gradient computation
- init: Xavier/Kaiming/near-zero initialization via CPU generate + GPU upload
Added cublas feature to ml-core's cudarc dependency for sgemm support.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
CUDA infrastructure in ml-core (cuda_compile, gpu/capabilities, gpu/l2_cache)
previously accessed cudarc types through candle_core::cuda_backend::cudarc --
a transitive re-export that coupled low-level CUDA driver calls to Candle.
Changes:
- Add cudarc 0.17 as direct optional dep (gated behind cuda feature)
- Replace all candle_core::cuda_backend::cudarc paths with direct cudarc::
- Generalize OOM detection to accept &dyn Debug (was &CandleError)
- Add generic computation_error_to_common_error() alongside legacy alias
Candle references: 163 -> 72 (remaining are autograd-dependent: Tensor,
Var, GradStore, VarBuilder -- cannot be replaced with cudarc raw ops)
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>
The ml crate fix alone wasn't enough — ml-core, ml-dqn, ml-ppo,
ml-supervised, ml-ensemble, ml-explainability, ml-hyperopt, and
ml-labeling all had `default = ["cuda"]`, each independently pulling
in cudarc via candle-core/cuda.
Now `default = []` on all sub-crates. CUDA activates only when the
compile-and-train template passes `--features ml/cuda`, which
propagates through ml's cuda feature gate to all sub-crates.
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>
- 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>
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 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 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>
Add 5 empty sub-crates (ml-core, ml-dqn, ml-ppo, ml-supervised, ml-infra)
to workspace. Modules will be moved from monolithic ml crate in subsequent
tasks.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
