- Add bayesian_changepoint_test to core tests (spec 4.5)
- Replace fragmented YAML with complete WorkflowTemplate (all 4 templates)
- Add complete notify-result template with notify parameter gating
- Remove dead DQN_SMOKE_EPOCHS env var (no consumer in codebase)
- Replace templateRef with resource template (workflow-of-workflows)
to fix PVC/volume context issue in ci-pipeline integration
- Fix supervised integration test || true → compile-check guard
- Remove dead epochs parameter from all consumers
- Inline shell script into YAML (no separate code block)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Covers PVC creation, test data population, TEST_DATA_DIR wiring,
WorkflowTemplate, CronWorkflow, argo-test.sh CLI, and CI integration.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- 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>
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>
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>
11-task plan across 4 chunks: Training Guard kernel + wrapper (Tasks 1-3),
wire into trainer (Tasks 4-6), Q-value monitor + action routing (Tasks 7-9),
experience collector audit + final verification (Tasks 10-11).
Eliminates all 10 GPU→CPU sync barriers from the DQN training hot path.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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>
