jgrusewski
d95e205d4b
refactor(ml): delete mixed_precision module — BF16 unconditional on CUDA
...
Eliminate the entire mixed_precision runtime indirection layer:
- Delete crates/ml-core/src/mixed_precision.rs (training_dtype, ensure_training_dtype, align_dim_for_tensor_cores)
- Inline ~100 call sites across 130 files to constants:
training_dtype(&device) → candle_core::DType::BF16
ensure_training_dtype(x) → x.to_dtype(candle_core::DType::BF16)
align_dim_for_tensor_cores(x, &device) → (x + 7) & !7
- Remove re-exports from ml-dqn, ml-supervised, ml lib.rs
- Clean config/toml/json/shell references
No CPU/Metal training path exists — BF16 is the only dtype.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com >
2026-03-16 16:11:48 +01:00
jgrusewski
4a1add5806
cleanup: delete 92 scattered .md files and .serena artifacts
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Remove stale test reports, quick-start guides, benchmark analyses,
profiling reports, and tool artifacts from across the workspace.
Keeps only root README.md per crate/service.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com >
2026-03-01 22:42:36 +01:00
jgrusewski
7458f1be01
feat(wave12): E2E validation complete - 225-feature pipeline ready
...
✅ Validation Results:
- PPO training: 24.2s (1 epoch, 950 samples, dim=225)
- Feature extraction: 105μs/bar (9.5x faster than target)
- Model checkpoint: 293KB (147KB actor + 146KB critic)
- GPU memory: 145MB used (96.4% headroom)
- Zero dimension mismatches
📊 Success Criteria (5/5):
✅ Feature dimension = 225 (Wave C 201 + Wave D 24)
✅ Model state_dim = 225
✅ Training completed without errors
✅ Checkpoint saved successfully
✅ No dimension mismatch errors
📁 Training Data Ready:
- ES.FUT: 2.9MB, 180 days
- NQ.FUT: 4.4MB, 180 days
- 6E.FUT: 2.8MB, 180 days
- ZN.FUT: 65KB, 90 days (clean)
🚀 Next: Full production model retraining (4 models, ~10min GPU time)
🤖 Generated with Claude Code (https://claude.com/claude-code )
Co-Authored-By: Claude <noreply@anthropic.com >
2025-10-22 22:48:04 +02:00
