Remove per-epoch GPU data freeing that forced re-upload every epoch.
Training data within a walk-forward window is immutable — uploading once
and keeping it GPU-resident eliminates the init phase entirely.
Epoch time: 153ms → 78ms on RTX 3050 (epochs 2+).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Based on kernel deep dive: 1.56% occupancy on H100 from 1-warp/sample
architecture, 46KB stack spill, 47 kernel launches per step.
7 tasks: cuBLAS forward, batched backward, fuse BF16/EMA, multi-block
prefix sum, C51 loss kernel, H100 profiling.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Spec: 3-phase plan to reduce DQN training epoch from 37s to <5s on H100.
- Phase 1: eliminate 300 CPU roundtrips in PER sampling (GPU Philox RNG)
- Phase 2: increase kernel occupancy (batch_size 512+, cuBLAS profiling)
- Phase 3: pipeline overlap (dual-stream experience/training)
Profiling instrumentation added to training loop (init/experience/training/validation breakdown).
RTX 3050 baseline: training=97.2%, experience=2.3% — PER sampling
with CPU RNG + DtoH sync is the primary bottleneck.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Remove ALL #[cfg(feature = "cuda")] guards (~400+ occurrences)
- Remove ALL #[cfg_attr(not(feature = "cuda"), ignore)] test annotations (~250)
- Make cuda default feature in 9 ML crates (ml, ml-core, ml-dqn, ml-ppo, etc.)
- Convert nvrtc JIT compilation to precompiled nvcc (searchsorted, prefix_sum)
- Move compile_ptx_for_device() to ml-core for shared access
- Delete dead CPU code: multi_step.rs, self_supervised_pretraining.rs,
training_guard_gpu_tests.rs, CPU PER buffer paths, CPU Q-diagnostics
- Replace unwrap_or(Device::Cpu) with hard errors everywhere
- Remove dead is_cuda() else branches in DQN/PPO/hyperopt trainers
- Change config defaults from "cpu" to "cuda" (rainbow, tlob, pipeline)
- Port IQL value network to GPU kernel (5 CUDA entry points)
- Port HER goal relabeling to GPU kernel (warp-per-sample)
- Wire DSR GPU-to-CPU sync in training loop
- cfg!(feature = "cuda") → true in inference_validator
Zero warnings, zero errors across entire workspace.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- B1: Add LIB_FILTER mapping for tggn→tgnn module name (prevents zero-test false positive)
- W1: Replace ml_supervised:: imports with canonical ml:: paths in supervised_gpu_smoke_test
- W4: Use clean pass/fail exit code instead of raw failure count
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Add Task 3b: GPU smoke test for all 8 supervised models via UnifiedTrainable
- Wire supervised_gpu_smoke_test into Task 4 workflow shell script (replaces wildcard)
- Each supervised model gets explicit test filter: test_${MODEL}_gpu_smoke
- Retain fallback for model-specific _integration tests if they exist
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- 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>
