Commit Graph

6 Commits

Author SHA1 Message Date
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
76e1010568 docs: add Kanidm SSO + NetBird mesh design spec and implementation plan
Design spec covers OIDC architecture (RS256 JWKS, WebAuthn-first),
7 service integrations, NetworkPolicy, and phased migration strategy.
Implementation plan: 17 tasks across 6 chunks, reviewed 3 rounds
(2 internal + 1 external Gemini 2.5 Pro expert review, all fixes applied).

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-14 19:41:40 +01:00
jgrusewski
696d0d6bff docs: rev 2 GPU test workflow spec — fix all 4 blockers
- Compile+test in same H100 pod (eliminates cross-node PVC transfer)
- New cargo-target-cuda-test PVC (30Gi) — zero contention with training
- onExit notify, podGC, activeDeadlineSeconds, fsGroup, gpu-warmup
- Continue-on-failure with per-model exit code capture
- CUDA_COMPUTE_CAP=90, complete change detection paths
- TEST_DATA_DIR marked as required prerequisite code change

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-13 11:10:01 +01:00
jgrusewski
9976b55d04 docs: H100 GPU test workflow design spec
Spec for Argo WorkflowTemplate that compiles with --features cuda
on CPU node and runs full GPU/CUDA test suite on H100 with real
market data from a dedicated test-data-pvc.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-13 11:00:52 +01:00
jgrusewski
4709ca8bc2 feat(dqn): enable Branching DQN with 45 factored actions (5×3×3)
Restore 45-action factored space via Branching DQN (Tavakoli 2018),
outputting 11 Q-values (5+3+3) instead of 45. This was reduced to 5
exposure-only actions during debugging and was never intended as permanent.

- Enable use_branching: true by default in DQNConfig and DQNHyperparameters
- Add branching paths to select_action_with_confidence and select_action_inference
- Update agent.rs select_action_factored for branching-aware selection
- Expand CountBonus to per-branch tracking with bonuses_branched()
- Add order_type + urgency distribution tracking in monitoring
- Add DQN_ORDER_ACTIONS=3, DQN_URGENCY_ACTIONS=3, DQN_TOTAL_ACTIONS=45 to CUDA header
- Fix 7 pre-existing clippy doc_markdown errors in regime_conditional.rs
- Fix pre-existing cognitive_complexity in replay_buffer_type.rs (extract helpers)
- Fix flaky GPU test OOM under parallel execution (CPU fallback + test VRAM safety)
- Delete unused flash_attention submodules (block_sparse, causal_masking, etc.)
- Add GPU hot-path guard scripts and ensemble/hyperopt adapter improvements

Tests: ml-dqn 416/0, ml 905/0, clippy 0 errors on both crates

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-11 22:00:13 +01:00
jgrusewski
41440e9ae7 docs: add zero-CPU DQN training hot path design spec
Defines architecture for eliminating all 10 GPU→CPU sync barriers from
the training loop via 4 new GPU components: Training Guard (pinned
memory predicates), Q-Value Monitor (on-device accumulator), GPU-resident
action selection, and async experience collector readback.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-10 15:49:40 +01:00