Files
foxhunt/crates/ml-explainability
jgrusewski b616d024ad fix(dqn): IQN GPU PER weights, staged GPU buffer, CUDA default in all ML crates
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>
2026-03-08 22:31:49 +01:00
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