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 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>
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 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>
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>
